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Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.

Community graph: which patterns consistently produce high-grade prompts for this generator? Powered by the V3 graph brain (Supabase PostgreSQL). Every scored prompt contributes. Returns top patterns by type, enhancement unlocks, and cross-model universal patterns.

Fetch SEC XBRL frames for one concept × one period across all reporting companies. Inline response returns a page of the ranked companies — start at the top or pass offset/next_offset to walk further down the ranking; the full frames response (all reporters) is materialized as df_<id> when a canvas is available, queryable via secedgar_dataframe_query. Accepts friendly names like "revenue" or "assets" (discover via secedgar_search_concepts) or raw XBRL tags. One call hits one XBRL tag — when a friendly name maps to multiple same-meaning tags, the response's `unqueried_tags` lists the others; call again per tag and UNION/COALESCE in SQL with an analysis-specific priority (e.g. SalesRevenueGoodsNet is goods-only). The response's `related_tags` separately flags alternate-DEFINITION tags a meaningful share of filers use as their primary line (e.g. cash incl. restricted cash, equity incl. noncontrolling interest) — a whole-universe screen on the base tag silently omits those filers; query them separately, but do not blindly union (the semantics differ). Response includes `value_distribution` and `period_end_range` to flag XBRL scale-factor anomalies and fiscal-year mixing.

Run a single-statement SELECT against the canvas dataframes registered by secedgar_fetch_frames, secedgar_search_filings, and secedgar_get_financials. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied — list dataframes via secedgar_dataframe_describe. Optional register_as chains the result as a new dataframe with a fresh TTL.

Récupère une décision du Conseil d'État par son numéro de pourvoi. Essaie d'abord le bulk JADE DILA (lookup SQL exact), puis si introuvable tente ArianeWeb Sinequa — les deux bases ont des couvertures complémentaires. Pour retrouver une décision via identifiant DCE_*, utiliser `get_decision_text` à la place. Args: numero: numéro de pourvoi (ex : "497566", "358109") Returns: Décision avec métadonnées, ou dict d'erreur structuré `{error, error_category: "not_found"}` si introuvable dans les deux bases.

Récupère une décision administrative par son **numéro de requête exact**. Couvre toutes les juridictions : Conseil d'État, cours administratives d'appel (CAA), tribunaux administratifs (TA). Utilise un lookup SQL exact sur le champ `numero` — pas de FTS5, pas de faux positifs. ⚠️ **Désambiguïsation indispensable** : un même numéro à 7 chiffres (ex: 2200433) est partagé par 24+ tribunaux administratifs différents (chaque TA a sa propre série annuelle qui repart à 1). Sans `juridiction`, tu obtiens un homonyme au hasard parmi 24 — souvent pas le bon. **Si tu sais quelle juridiction a rendu la décision, passe-la TOUJOURS.** Args: numero: numéro de requête (ex : "2200433", "2116343", "497566") juridiction: identifiant de la juridiction. **Recommandé pour tout numéro à 7 chiffres** (TA/CAA codifié). Formats acceptés : - **Nom long (recommandé)** : "Cour administrative d'appel de Lyon", "Tribunal administratif de Paris", "Conseil d'État" (avec ou sans accent, casse libre). Matching tolérant via extraction de ville. - **Code court** : "TA69", "TA75", "CAA69", "CE", "CE-CAA". ⚠️ Les codes courts peuvent rater les arrêts anciens (id CETATEXT* historiques) pour lesquels le mapping interne échoue. Si tu sais le nom long, préfère-le. Note : "Lyon" seul est ambigu (TA Lyon ou CAA Lyon) — préférer le nom complet pour éviter la collision. Returns: Décision avec métadonnées (id, juridiction, numero, date, titre), ou `{"error": "introuvable"}` si aucun résultat dans JADE. Exemples : get_admin_decision("2200433", juridiction="Tribunal Administratif de Lyon") → DTA_2200433_20230214 (TA Lyon, 14 fév 2023, RSA dérogatoire) get_admin_decision("473286") # CE n'a pas de doublon, juridiction inutile → DCE_473286_20231123 (CE, non-admission du pourvoi sur la précédente)

Recherche pondérée par pertinence BM25 sur la jurisprudence administrative complète (Conseil d'État + 9 CAA + 40 TA). Source : bulk JADE DILA (~550 k décisions full text). Contrairement aux outils `search_admin_recent*` qui trient par date, celui-ci classe par pertinence sémantique des mots-clés. Indispensable pour trouver LES bonnes décisions sur un sujet sans dépendre de l'ancienneté. ⚠️ **Si tu cherches par numéro de requête (7 chiffres ex: 2200433)**, utilise plutôt `get_admin_decision(numero, juridiction=...)` qui fait un lookup SQL exact. La recherche FTS5 d'un numéro court ne le trouve que dans les décisions qui le **citent** dans leur texte (ex: décision de cassation), pas la décision identifiée par ce numéro. Args: query: mots-clés (opérateurs FTS5 : AND/OR/NOT, "phrase exacte", mot*) juridiction: filtre par fragment de nom de juridiction. Ex : "Lyon" → toutes les décisions Lyon (TA + CAA), "Tribunal Administratif de Lyon" → uniquement TA Lyon. Combiné en FTS5 AND avec la query principale. sort: "relevance" (défaut, BM25) ou "date_desc" / "date_asc" date_min: limite inférieure ISO YYYY-MM-DD (optionnel) date_max: limite supérieure ISO YYYY-MM-DD (optionnel) limit: nombre de résultats (défaut 20, max 50) offset: pagination Returns: {"total", "returned", "decisions": [...]} avec extracts BM25.

List the tables and column schemas on a DataCanvas staged by an openFDA search tool. Call before openfda_dataframe_query to discover the exact table name, column names, and DuckDB types needed for valid SQL. row_count is the full staged result set, not the inline preview count. Columns typed JSON hold nested openFDA objects/arrays — query them with DuckDB json functions.

Run a read-only SQL SELECT against a DataCanvas table staged by an openFDA search tool (call one with stage=true; its response carries canvas_id + canvas_table). Enables GROUP BY, COUNT/SUM/AVG, time-series, and joins across the staged result set without re-paging the API. Call openfda_dataframe_describe first to get the exact table and column names. Results are capped at the canvas row limit — when truncated is true, page the rest with ORDER BY plus LIMIT/OFFSET. Scalar fields are stored as text (CAST for numeric math); nested objects/arrays are JSON columns — read them with DuckDB json functions, e.g. json_extract_string(openfda, '$.brand_name[0]'). Only SELECT is allowed — DDL, DML, COPY, and file-reading functions are blocked.

Vue 360 d'un établissement de santé en 1 appel (V0.10). Pendant naturel de `panorama_sante_territoire` côté **site** : agrège en parallèle (a) identification FINESS DREES (raison sociale, adresse, téléphone), (b) statut administratif SIRENE via le resolver SIRET (verdicts site + groupe, best_match, SIREN explorés, dinum_errors, explication LLM-friendly), (c) professionnels rattachés via num_finess (sample borné + flag `truncated` si le site a plus de PS — PAS un count total), (d) historique INSEE (timeline périodes administratives par SIRET candidat). Remplace 3 appels MCP individuels (`verifier_site_actif` + `rpps_dans_etablissement` + `historique_etablissement`) par 1 seul. Utile pour : prospection (qualifier un site avant outreach), audit territorial (cross-check rapide d'un FINESS suspect), enrichissement CRM en batch. **Format de retour** : objet `LookupResult`. Quand `found: true`, payload avec 4 sections (finess, statut_site, professionnels, historique). La section `historique` peut être `available: false` quand le FINESS existe mais qu'aucun SIRET candidat n'a été identifié (RPPS vide + DINUM 0 match) — dans ce cas le `message` reprend celui de `historique_etablissement`. Quand `num_finess` est absent de FINESS DREES, retourne `{found: false, lookupStatus: 'not_found', message}`. Coût : 3 sous-appels parallèles. Cache PostgreSQL absorbe la duplication FINESS-RPC ; le pivot RPPS→DINUM est exécuté en double (verifier + historique partagent la cascade), surcoût p95 ≤ 600 ms — acceptable pour un agrégateur. Pour les besoins ciblés (juste le verdict, juste l'historique), préférer les tools individuels. Payload lourd (~7K tokens) : passer `historique_detail: false` pour un retour allégé (résumé au lieu des timelines SIRENE complètes) en usage batch. Alias acceptés : `numFiness`/`finess`/`id` → `num_finess`.

Check operator readiness. Returns the operator lifecycle state and clear guidance on what to do next. Free. Lifecycle states: - ready: Operator is warm and fully operational — vault AND pricing model verified. Proceed with tool calls. - warming_up: Operator is initializing (cold start). Try a tool call — it will warm up on demand. - misconfigured: Persistence rejected a query with a permanent SQL error (permission denied, missing relation). Paid tools will fail until the operator repairs the database — retrying does not help. - quota_exceeded: The persistence provider (Neon) answered HTTP 402 — the operator's database has exhausted its compute/storage quota, so the books are locked for billing. Paid tools fail; retrying does NOT help. The operator's Authority must restore capacity (upgrade the plan or wait for the quota reset). Free tools remain available. - not_registered: Operator has no Authority relationship yet. Call register_operator first. - no_identity: Operator nsec is not configured. Deployment issue.

Re-run ``ensure_schema()`` on every NeonVault this operator uses. Diagnostic / recovery tool for the case where the Neon HTTP SQL API is returning persistent 4xx errors and the operator suspects the schema isn't there or grants are wrong. Idempotent — uses ``CREATE TABLE IF NOT EXISTS`` so a successful re-run is harmless. Returns the per-step result. If any step raises, surfaces the Neon error message inline (0.31.0 reads the SQL error body that earlier wheels swallowed behind ``raise_for_status``). RESTRICTED to operator — requires proof (nsec-signed).

Fetch national debt (Debt to the Penny) — total public debt outstanding broken into publicly-held debt and intragovernmental holdings. Three modes: "latest" returns the most recent business day's record; "date" returns the record for a specific date (must be a business day — the API only records debt on days markets are open); "series" returns a date range and optionally spills results to DataCanvas for SQL analysis via treasury_dataframe_query. Records go back to 1993-01-04. As of 2026-05-28 the total debt is approximately $39.18T.

Run a single-statement SELECT against DataCanvas dataframes registered by treasury_query_dataset, treasury_get_debt, treasury_get_interest_rates, and treasury_get_exchange_rates. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer. All Treasury dataframe columns are VARCHAR — CAST to DECIMAL or DATE for arithmetic and date comparisons. Use treasury_dataframe_describe to list available table names and column schemas before querying.

Validate an artifact against a contract (json_schema | openapi_response | sql) via the machinegrade validate API.

Historical weather from the ERA5 reanalysis archive (1940–present). Requires start_date and end_date (ISO 8601 date, e.g., "2024-07-01"). ERA5 has a variable lag of up to ~5 days — for dates within the last week, use openmeteo_get_forecast with past_days instead. Uses the same variable names as the forecast API for direct comparison. Large date ranges (multi-year hourly) produce thousands of records — these spill to DataCanvas for SQL querying when canvas is enabled. At least one of hourly_variables or daily_variables is required.

GloFAS (Global Flood Awareness System) river discharge forecast and historical reanalysis. Returns daily ensemble river discharge (m³/s) for the river nearest to the given coordinates — no river ID needed, the API snaps to the nearest stream. Forecast horizon up to 210 days ahead; reanalysis history back to 1984-01-01. One mode per call: forecast_days for the future outlook, or start_date and end_date together for reanalysis history. The two modes are mutually exclusive, and a date range needs both ends — a lone start_date or end_date is rejected. Available daily variables: "river_discharge" (ensemble mean), "river_discharge_mean", "river_discharge_min", "river_discharge_max", "river_discharge_median", "river_discharge_p25" (25th percentile), "river_discharge_p75" (75th percentile). Returns null for coordinates far from any river or in areas without GloFAS coverage. A wide reanalysis range produces thousands of daily records and spills to DataCanvas for SQL querying when canvas is enabled.

Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (ERA5, what happened). Daily resolution only. Available models: "CMCC_CM2_VHR4", "FGOALS_f3_H", "HiRAM_SIT_HR", "MRI_AGCM3_2_S", "EC_Earth3P_HR", "MPI_ESM1_2_XR", "NICAM16_8S". With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to DataCanvas for SQL querying when canvas is enabled.

Run a read-only SQL SELECT against tables staged on a DataCanvas by openmeteo_get_historical, openmeteo_get_ensemble, openmeteo_get_flood, or openmeteo_get_climate. Pass the canvas_id returned when any of those tools spills (truncated: true), and reference the exact table_name those tools return alongside it. Call openmeteo_dataframe_describe to list staged tables and their columns when you need to discover names.

Run a read-only SQL SELECT against the measurement tables openaq_get_measurements staged on a DataCanvas. Reference tables by the name the measurements call returned (measurements_<sensorId>). For aggregation (monthly means, exceedance counts) and cross-sensor comparison over series too large to inline. Only SELECT is allowed — writes, DDL, and file/network table functions are rejected.

List the tables and columns staged on a DataCanvas so you can write valid SQL for openaq_dataframe_query without guessing column names. Returns each measurement table (measurements_<sensorId>) with its row count and column names. Requires DataCanvas to be enabled.

Execute a SoQL query against any dataset on any Socrata portal. Use the search parameter for quick full-text lookup, or combine select/where/group/having/order for full analytical control. Returns rows plus the assembled SoQL string so you can learn the pattern. All SODA 2.1 row values are strings even for numeric columns — check dataType from socrata_get_dataset to determine correct WHERE quoting: Number columns use bare literals (year=2023), Text columns use single-quoted strings (year='2023'). To enumerate distinct values, use select="col, count(*) as n" with group="col" and order="n DESC". When CANVAS_PROVIDER_TYPE=duckdb and rows fill the limit, results spill to a DataCanvas table for SQL-based analysis.

List registered tables in a DataCanvas session — schema, row count, column names, and registration time. Shows what datasets are available for SQL queries via socrata_dataframe_query. Only meaningful when CANVAS_PROVIDER_TYPE=duckdb is set. Use after socrata_query_dataset spills a large result set to canvas.

Run SELECT-only SQL against a DataCanvas table populated by socrata_query_dataset. DuckDB infers types from spilled data, so numeric columns that SODA returned as strings become queryable with numeric comparisons (year > 2020, amount < 500). Only works when CANVAS_PROVIDER_TYPE=duckdb is set. Use socrata_dataframe_describe to see registered tables and their schemas.

Generate realistic JSON, CSV, or SQL test data from structured tables or a plain-English prompt. Explicit table schemas up to 100 records can run free as a proof-of-work preview; plain-English prompt generation, larger requests, and production usage require a MockHero API key.

Convert SQL CREATE TABLE statements or one sample JSON object into a MockHero schema that can be passed to generate_test_data.

Query an IMF SDMX dataflow by dimension key over a time range. Returns observations with time_period, value, unit, scale, and status attributes. Requires imf_get_database first to obtain the correct key_format and valid dimension codes. Country codes are ISO 3-letter (USA, GBR, DEU — not US, GB, DE). Key format: dot-separated codes in DSD keyPosition order (e.g. USA.NGDP_RPCH.A for WEO). Use + to specify multiple codes per position (e.g. USA+GBR.NGDP_RPCH.A). Codelists from imf_get_database enumerate the code universe, not actual coverage — valid codes can still return no_data if the combination has no series. start_period and end_period must be valid period strings (YYYY, YYYY-QN, or YYYY-MM) with start_period no later than end_period; malformed or reversed ranges are rejected. Large analytical result sets (multi-country, long time range) spill to DataCanvas; imf_dataframe_query provides SQL analysis of spilled results.

List DataCanvas tables and columns staged by a prior imf_query_dataset call. Returns each table's name, row count, and column schema (name + DuckDB type). Required before imf_dataframe_query to discover the table and column names for SQL.

Run a read-only SQL SELECT against a DataCanvas table staged by imf_query_dataset. Supports multi-country comparisons, time-series aggregation, and cross-indicator joins. Requires imf_dataframe_describe first to discover table and column names. Only SELECT statements are accepted — DML and DDL are rejected.

Assemble a global profile for one commodity in a single call: top-producing countries, the annual production trend, and trade flows (top exporters and importers). Accepts a commodity name, resolves it to item codes, then queries the production (QCL) and trade (TCL) domains and merges the results. Each ranking is a per-country sum across the resolved items, taken at that country's own latest year with data and grouped by unit so incomparable quantities are never added. The trend is returned inline as year/value points. Country-level only (aggregates excluded). When a required domain is not indexed locally, returns a partial profile with a notice naming the gap rather than failing. The full merged observation set spills to a DataCanvas table for deeper SQL via faostat_dataframe_query.

Run a single-statement SELECT against the canvas tables staged by faostat_query_observations and faostat_commodity_profile (table names look like faostat_xxxxxxxx). Use this for cross-country and cross-item aggregation, GROUP BY rankings, joins, and time-series analysis over the full result set the inline preview only sampled. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all work. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected; system catalogs (information_schema, sqlite_master, duckdb_*) are denied — list staged tables via faostat_dataframe_describe. Every row carries its data-quality `flag` — commonly A=Official, B=time-series break, E=Estimated, I=Imputed, M=Missing (value cannot exist), T=Unofficial, X=from an international organization, plus others FAOSTAT defines per domain — keep it in projections, treat any unrecognized flag as informational, and never assume it is official.

List the canvas tables (faostat_xxxxxxxx) staged by faostat_query_observations and faostat_commodity_profile, each with its source tool, the query parameters that produced it, creation/expiry timestamps, row count, and column schema. Call this before faostat_dataframe_query to discover the exact table and column names to reference in SQL.

Atomic single-fact endpoints designed for tight agent loops. Each answers ONE yes/no or one number — sub-50ms, flat $0.001/call at the REST surface. Two families: (1) DeFi facts sourced from our SQLite + shadow-blocks recorder (liquidatable, at-risk-count, recent-liquidations, top-builder, builder-share, builder-bid, block-value, cheapest-flashloan, data-freshness, address-risk, base-fee, proposer-payment); (2) privacy-chain facts sourced from Seneschal-operated full nodes — Monero (xmr/height, xmr/mempool, xmr/fee, xmr/fee-estimate, xmr/last-block) and Zcash (zec/height, zec/mempool, zec/last-block, zec/pools). Consult /v1/q for per-question input lists and live chain availability.

Return all spendable + spent notes for a view key without setting up a watch. The view key never touches our SQLite — it flows through to NFPT in memory only. Use this when you want to reconcile a wallet at a point in time. Priced at $0.50 / call at the REST surface.

Get a daily or instantaneous time series for one USGS site and parameter over a date range, as time-ordered value records. Large sets (>500 records) return the most recent 500 with truncated=true; with DataCanvas enabled they instead spill to a canvas (canvas_id/table_name) for SQL via water_dataframe_query. Use water_find_sites and water_list_parameters to resolve inputs.

Run a read-only SQL SELECT against water data tables staged on a DataCanvas by water_get_series or water_find_sites. Workflow: run water_get_series or water_find_sites (get canvas_id + table_name) → water_dataframe_describe (confirm the table and its columns) → water_dataframe_query (SQL analysis). Only SELECT statements are permitted. At most 10,000 rows are returned; a query matching more is capped and the response sets truncated=true — scope with WHERE/LIMIT, and use SELECT COUNT(*) or water_dataframe_describe to learn the true match count. Requires DataCanvas to be enabled on this server instance. Returns an error if DataCanvas is not available.

Subscription-gated validation of a governed semantic query. Returns the public plan and SQL fingerprint without exposing raw SQL.

Subscription-gated explanation of fields, evidence requirements, and SQL fingerprint without raw SQL.

Subscription-gated governed semantic query. Returns ATLAS evidence envelopes only; raw SQL is never returned.

Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns claim counts, amounts paid on building and contents, flood zones, and loss years. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and results exceed the inline preview, the full result set is staged on a canvas for SQL aggregation via fema_dataframe_query. Use fema_dataframe_describe to inspect the staged table schema before writing SQL. Without canvas, results are returned inline up to the limit.

Run a read-only SQL SELECT against a DataCanvas table staged by fema_search_nfip. Enables aggregation, GROUP BY, SUM/COUNT, time-series, and filtered analysis over the full NFIP claims result without re-fetching from the API. Call fema_dataframe_describe first to get the exact table name and column names needed for valid SQL. Only SELECT statements are allowed — DDL, DML, COPY, and file-reading functions are blocked.

List tables and column schemas on a DataCanvas staged by fema_search_nfip. Call this before fema_dataframe_query to discover the exact table name, column names, and DuckDB data types needed to write valid SQL. Row count reflects what was actually staged — check truncated in the fema_search_nfip response to know whether the canvas holds the full matching set.

Get historical daily exchange rates for a currency pair over a date range. ECB publishes on business days only — weekends and holidays produce no entry (not snapped). Short ranges (≤90 days by default) are returned inline as a date→rate map. Long ranges spill to DataCanvas: the response carries spilled=true, a canvas_id, and a table_name. Call fx_dataframe_describe to inspect the staged table, then fx_dataframe_query to run SQL against it.

Run a read-only SQL SELECT against DataCanvas tables staged by fx_get_timeseries. Supports aggregations, GROUP BY, window functions, and JOINs across multiple registered tables. Run fx_dataframe_describe first to discover table names and column schemas.

Replay VRP dashboard (z-score, percentile, regime, strategy scores) at any minute since April 2018. Percentiles and z-scores are leak-free: date-bounded in SQL so the backtest only sees data strictly before the `at` timestamp. Alpha tier.

Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.

Check operator readiness. Returns the operator lifecycle state and clear guidance on what to do next. Free. Lifecycle states: - ready: Operator is warm and fully operational — vault AND pricing model verified. Proceed with tool calls. - warming_up: Operator is initializing (cold start). Try a tool call — it will warm up on demand. - misconfigured: Persistence rejected a query with a permanent SQL error (permission denied, missing relation). Paid tools will fail until the operator repairs the database — retrying does not help. - quota_exceeded: The persistence provider (Neon) answered HTTP 402 — the operator's database has exhausted its compute/storage quota, so the books are locked for billing. Paid tools fail; retrying does NOT help. The operator's Authority must restore capacity (upgrade the plan or wait for the quota reset). Free tools remain available. - not_registered: Operator has no Authority relationship yet. Call register_operator first. - no_identity: Operator nsec is not configured. Deployment issue.

Re-run ``ensure_schema()`` on every NeonVault this operator uses. Diagnostic / recovery tool for the case where the Neon HTTP SQL API is returning persistent 4xx errors and the operator suspects the schema isn't there or grants are wrong. Idempotent — uses ``CREATE TABLE IF NOT EXISTS`` so a successful re-run is harmless. Returns the per-step result. If any step raises, surfaces the Neon error message inline (0.31.0 reads the SQL error body that earlier wheels swallowed behind ``raise_for_status``). RESTRICTED to operator — requires proof (nsec-signed).

Check operator readiness. Returns the operator lifecycle state and clear guidance on what to do next. Free. Lifecycle states: - ready: Operator is warm and fully operational — vault AND pricing model verified. Proceed with tool calls. - warming_up: Operator is initializing (cold start). Try a tool call — it will warm up on demand. - misconfigured: Persistence rejected a query with a permanent SQL error (permission denied, missing relation). Paid tools will fail until the operator repairs the database — retrying does not help. - quota_exceeded: The persistence provider (Neon) answered HTTP 402 — the operator's database has exhausted its compute/storage quota, so the books are locked for billing. Paid tools fail; retrying does NOT help. The operator's Authority must restore capacity (upgrade the plan or wait for the quota reset). Free tools remain available. - not_registered: Operator has no Authority relationship yet. Call register_operator first. - no_identity: Operator nsec is not configured. Deployment issue. Args: patron_npub: Optional. If supplied, the response includes an ``upstream_oauth`` block with the patron's stored OAuth token expiry (runtime-derived from vault state) so a client can refresh proactively rather than reactively after a stale-token failure.

Re-run ``ensure_schema()`` on every NeonVault this operator uses. Diagnostic / recovery tool for the case where the Neon HTTP SQL API is returning persistent 4xx errors and the operator suspects the schema isn't there or grants are wrong. Idempotent — uses ``CREATE TABLE IF NOT EXISTS`` so a successful re-run is harmless. Returns the per-step result. If any step raises, surfaces the Neon error message inline (0.31.0 reads the SQL error body that earlier wheels swallowed behind ``raise_for_status``). RESTRICTED to operator — requires proof (nsec-signed).

Check operator readiness. Returns the operator lifecycle state and clear guidance on what to do next. Free. Lifecycle states: - ready: Operator is warm and fully operational — vault AND pricing model verified. Proceed with tool calls. - warming_up: Operator is initializing (cold start). Try a tool call — it will warm up on demand. - misconfigured: Persistence rejected a query with a permanent SQL error (permission denied, missing relation). Paid tools will fail until the operator repairs the database — retrying does not help. - quota_exceeded: The persistence provider (Neon) answered HTTP 402 — the operator's database has exhausted its compute/storage quota, so the books are locked for billing. Paid tools fail; retrying does NOT help. The operator's Authority must restore capacity (upgrade the plan or wait for the quota reset). Free tools remain available. - not_registered: Operator has no Authority relationship yet. Call register_operator first. - no_identity: Operator nsec is not configured. Deployment issue.

Re-run ``ensure_schema()`` on every NeonVault this operator uses. Diagnostic / recovery tool for the case where the Neon HTTP SQL API is returning persistent 4xx errors and the operator suspects the schema isn't there or grants are wrong. Idempotent — uses ``CREATE TABLE IF NOT EXISTS`` so a successful re-run is harmless. Returns the per-step result. If any step raises, surfaces the Neon error message inline (0.31.0 reads the SQL error body that earlier wheels swallowed behind ``raise_for_status``). RESTRICTED to operator — requires proof (nsec-signed).

Check operator readiness. Returns the operator lifecycle state and clear guidance on what to do next. Free. Lifecycle states: - ready: Operator is warm and fully operational — vault AND pricing model verified. Proceed with tool calls. - warming_up: Operator is initializing (cold start). Try a tool call — it will warm up on demand. - misconfigured: Persistence rejected a query with a permanent SQL error (permission denied, missing relation). Paid tools will fail until the operator repairs the database — retrying does not help. - quota_exceeded: The persistence provider (Neon) answered HTTP 402 — the operator's database has exhausted its compute/storage quota, so the books are locked for billing. Paid tools fail; retrying does NOT help. The operator's Authority must restore capacity (upgrade the plan or wait for the quota reset). Free tools remain available. - not_registered: Operator has no Authority relationship yet. Call register_operator first. - no_identity: Operator nsec is not configured. Deployment issue.

Re-run ``ensure_schema()`` on every NeonVault this operator uses. Diagnostic / recovery tool for the case where the Neon HTTP SQL API is returning persistent 4xx errors and the operator suspects the schema isn't there or grants are wrong. Idempotent — uses ``CREATE TABLE IF NOT EXISTS`` so a successful re-run is harmless. Returns the per-step result. If any step raises, surfaces the Neon error message inline (0.31.0 reads the SQL error body that earlier wheels swallowed behind ``raise_for_status``). RESTRICTED to operator — requires proof (nsec-signed).

Use this when you need to execute Python, JavaScript, or SQL code in a sandboxed environment and get the output. Supports Python (subprocess), JavaScript (eval), and SQL (in-memory SQLite). 1. output: stdout captured from code execution (max 10KB) 2. language: the language that was executed 3. executionTimeMs: execution duration in milliseconds 4. exitCode: process exit code (0 = success) 5. error: error message if execution failed (null on success) Example output: {"output":"Hello World\n42\n","language":"python","executionTimeMs":234,"exitCode":0,"error":null} Use this FOR running calculations, data transformations, validating code snippets, or querying in-memory databases. Essential when you need computed results rather than static data. Do NOT use for persistent file storage -- sandbox is ephemeral. Do NOT use for generating hashes -- use crypto_generate_hash. Do NOT use for PDF generation -- use document_generate_pdf. Do NOT use for web scraping -- use web_scrape_to_markdown.

WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

INSPECTION: View the current infrastructure stack for a session Returns the current state of the user's infrastructure design including: **Components** - Selected infrastructure services (VPC, databases, caching, etc.) • Shows what services the user has chosen (e.g., PostgreSQL, Redis, S3) • Includes architecture decisions (EKS vs EC2, monolith vs microservices) **Config** - Configuration details for each component • Database sizes, replica counts, storage amounts • Cache settings, queue configurations • Backup schedules and retention policies **Pricing** - Cost estimates (when available) • Monthly cost estimates per component • Total estimated monthly spend **Phase Indicators** - Where the user is in the design workflow: • hasComponents: User has selected infrastructure services • hasConfig: User has configured component details • hasPricing: Cost estimates have been calculated • hasTerraform: Ready for Terraform generation Use this tool when the user asks 'what is my current stack?', 'show my infrastructure', 'what have I selected?', or similar questions about their design progress. REQUIRES: session_id from convoopen response (format: sess_v2_...).

INSPECTION: Inspect GCP infrastructure for a deployed project ⚠️ **PREREQUISITE**: This tool requires a prior deployment ATTEMPT (successful or failed). Check convostatus for hasDeployAttempt=true before calling. Works even after failed deploys to inspect orphaned resources. Inspect deployed GCP resources after a deployment attempt. Use this tool when the user asks about the status or details of their deployed GCP infrastructure. It fetches temporary read-only credentials securely and queries the GCP API directly. RESPONSE TIERS (default is summary for token efficiency): - Summary (default): Key fields only (~500 tokens). Set detail=false, raw=false or omit both. - Detail: Full metadata for a specific resource. Set detail=true + resource filter. - Raw: Complete unprocessed API response. Set raw=true. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, call with action="list-actions". METRICS: Use list-metrics to see available Cloud Monitoring metrics for any service (no credentials needed — progressive disclosure). Use get-metrics to retrieve time-series data. Optional filters JSON: {"hours":6,"period":300}. Label breakdowns: Cloud Functions (by status), Load Balancer/API Gateway (by response_code_class), Cloud CDN (by cache_result). Secret Manager get-metrics returns operational health (version count, replication, create time) — no time-series. Bastion is an alias for Compute Engine metrics (SSH connection count not available as a GCP metric). BILLING: Use service=billing to inspect GCP billing. Actions: get-billing-info (check if billing enabled, which billing account), get-budgets (list budget alerts for the project — auto-fetches billing account). Requires roles/billing.viewer IAM role. Required IAM roles: Monitoring Viewer (roles/monitoring.viewer) for metrics, Secret Manager Viewer (roles/secretmanager.viewer) for secret health, Billing Viewer (roles/billing.viewer) for billing. EXAMPLES: - gcpinspect(session_id=..., service="compute", action="list-instances") - gcpinspect(session_id=..., service="gke", action="list-clusters") - gcpinspect(session_id=..., service="cloudsql", action="get-metrics", filters="{\"hours\":6}") - gcpinspect(session_id=..., service="billing", action="get-billing-info")

BATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ **PREREQUISITE**: Same as gcpinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single GCP credentials blob — a 12-resource health check is ~5–8× faster and 12× fewer Oracle round-trips than calling gcpinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, use gcpinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular gcpinspect when you need full metadata or raw API output for one resource. EXAMPLES: - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"list-instances"}, {"service":"gke","action":"list-clusters"}, {"service":"cloudsql","action":"list-instances"}]) - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"cloudrun","action":"get-metrics","filters":"{\"hours\":6}"}])

Complete-or-fail concordance: every words row in corpus whose lemma, dStrong, or (with by="surface") exact inflected surface form matches query. Route lemma/Strong's-number/surface-word lookups here, never by writing SQL or guessing occurrences from memory. This deployment caps one concordance result at 2000 occurrences and refuses anything broader, reporting the exact match count in the error. Call count first when a query might be broad - count is never capped.

List or search the products endoflife.ai tracks (480+). Pass an optional "query" substring to find the canonical slug for a product before calling the other tools (e.g. "postgres" → "postgresql"). Returns matching product slugs.