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Search for code snippets and examples in official Microsoft Learn documentation. This tool retrieves relevant code samples from Microsoft documentation pages providing developers with practical implementation examples and best practices for Microsoft/Azure products and services related coding tasks. This tool will help you use the **LATEST OFFICIAL** code snippets to empower coding capabilities. ## When to Use This Tool - When you are going to provide sample Microsoft/Azure related code snippets in your answers. - When you are **generating any Microsoft/Azure related code**. ## Usage Pattern Input a descriptive query, or SDK/class/method name to retrieve related code samples. The optional parameter `language` can help to filter results. Eligible values for `language` parameter include: csharp javascript typescript python powershell azurecli al sql java kusto cpp go rust ruby php

Retrieve the FULL TEXT and article list of a specific Swiss law, federal or cantonal, by LexFind ID or SR/systematic number. For federal laws in the Fedlex mirror this is instant (local SQLite). For cantonal laws, the law is downloaded from LexFind as PDF, parsed with PyMuPDF, and segmented into articles (cached 30 days). Returns: title, entity, articles (article_num, heading, text), full_text, article_count. Use search_legislation first to find the right lexfind_id or systematic_number; then pass it here. For the core federal codes, get_law is still the fastest path.

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.

Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL for the dataset. use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Use when you need narrative content from company filings — risk factors, MD&A, guidance language, deal terms, accounting policies, share structure. For consolidated financial numbers use run_sql on financial_statements instead. Semantic search over the full text of company-filed reports; returns matching passages. Coverage: US + Japan + Hong Kong + China A-shares. US = SEC EDGAR (including foreign issuers' 20-F/6-K). Japan = EDINET, `.T` ticker (6758.T). Hong Kong = HKEX filings, 5-digit `.HK` ticker (00700.HK). A-shares = `.SH`/`.SZ` (600519.SH, 300750.SZ). Parameters: - query (required): natural-language search; phrase it as the concept or section name you want, e.g. "share repurchase authorization", "Risk Factors". Run a few phrasings rather than one broad query. - ticker (required): US bare (NVDA), Japan `.T`, HK `.HK`, A-share `.SH`/`.SZ`, ADRs as their US symbol (SONY). - filing_types (optional): US = SEC form names (10-K, 10-Q, 8-K, 20-F, 6-K, DEF 14A, S-1/F-1, + amendments). Japan = EDINET NUMERIC codes: 120 (annual), 140 (quarterly), 160 (semi-annual). HK/A-share = plain names — annual_report; A-share quarters per-quarter (q1_report, ...); HK quarterly results all quarterly_report. OMIT to search all types. - period_start / period_end (optional): yyyy-mm window; omit to search all history. - top_k (optional): max passages to return (default 10). Scope: indexes ONLY company-filed reports — NOT institutional filings (13F-HR/13D/13G; for those use insider_and_institution_activities with source='institution'). Section targets: non-GAAP reconciliations → earnings 8-K (Ex 99.1); dilution / SBC / buyback → "Shareholders' Equity"; segment breakdown → "Segment Information"; guidance → "Outlook" in MD&A; exec comp → DEF 14A.

Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Coverage: US, Japan, Hong Kong, and China A-shares. `market` accepts one lowercase value or a list from `us | jp | hk | cn`; omit it or pass `[]` for all four. List order does not set priority. Pass a natural-language description (for example, "Hong Kong and China EV battery suppliers"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

PostgreSQL SELECT over financial / market / alt-data tables — returns structured rows. Hard rules (query fails otherwise): - SELECT only, no CTE (`WITH ... AS`) — use subqueries. - Period columns are TEXT, not dates — `period_end` is 'YYYY-MM'. Compare as strings (`period_end >= '2024-01'`); a `::date` cast on it fails. - Filter structured tables by ticker (`WHERE ticker IN ('AAPL','MSFT')`; screening: add `ticker NOT LIKE '%-%'` to drop preferred stock). Core equity coverage: US, Japan, Hong Kong, and China A-shares. Tickers are US bare (AAPL), Japan `.T` (6758.T), Hong Kong `.HK` (00700.HK), and A-shares `.SH`/`.SZ` (600519.SH, 300750.SZ). financial_statements, company_snapshot, and price_volume_history span all four. Specialized tables may be US-only or US+Japan — call get_table_schema before treating an empty result as a finding. Tables by domain (call get_table_schema for detail): - Market: price_volume_history (OHLCV history; MUST filter ticker + time_frame), index_price, equity_extended_rt (pre/after/overnight quotes) - Fundamentals: financial_statements (GAAP income/balance/cashflow), company_snapshot (ratios, per-share, growth) - Earnings: earning_call_summary, earning_call_calendar - Analyst: analyst_ratings, analyst_ratings_consensus - Ownership: insider_and_institution_activities - 8-K events: executive_change, company_deal_events, debt_issuance, securities_offering - Executives: executive_profile, executive_compensation - Alt-data: macro / industry / trade / AI-supply-chain — call list_tables(categories=[...])

Use BEFORE run_sql when you're unsure which columns a table has. Look up column definitions (name, type, description) for a data table.

List alternative-data tables under the given categories. Returns each table's name, one-line purpose, and column names (call get_table_schema if you need column types/comments). Batch up to 5 categories in one call; omit categories, or pass ["all"], to get the category index instead. Use this BEFORE run_sql when you want to explore alt-data — run_sql alone won't tell you which tables exist. Available categories: - Energy & Power — US power plants, electricity prices, regional hourly generation/demand - Data Centers — facilities, GPU clusters, cooling - Semiconductors — AI chip specs, sales, ownership, foundry revenue, customs trade - Compute Pricing — GPU rental, cloud VM spot/on-demand, instance specs - Model Development — model specs, benchmarks, AI companies, AI polling, LLM arena - Inference Economics — LLM API pricing across providers - Macro & Trade — UN Comtrade, US Census trade flows, FRED macro series - Prediction Markets — Polymarket and Kalshi events, markets, trades, daily aggregates - Critical Minerals — USGS mineral deposits, country supply, critical materials

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.

Free local tool. Records a tool call execution in the agent memory database (SQLite FTS5). Auto-call after any paid or significant tool call to build searchable history. No x402 charge. SAP MCP execution guidance: Intent: SAP MCP tool workflow. Pricing: free; call directly without x402. Routing: free hosted call; call directly and keep it small/exact when possible. Signer boundary: hosted reads/builders never receive keypair bytes; value-moving results must be finalized locally when signing is required.

Free local tool. Buffers a premium stream event in the local SQLite database for offline consumption. Deduplicates by (streamType, eventId). No x402 charge. SAP MCP execution guidance: Intent: SAP MCP tool workflow. Pricing: free; call directly without x402. Routing: free hosted call; call directly and keep it small/exact when possible. Signer boundary: hosted reads/builders never receive keypair bytes; value-moving results must be finalized locally when signing is required.

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).

Search YOUR extracted memory facts by topic or entity name. No LLM needed — pure SQL lookup against pre-extracted facts. Scoped to facts from memory you own — registered handle + secret required. Returns entries with topics, entities, action_items, and summary.

Cold-DM system-wide health snapshot. Admin/operator use. Returns the same load-bearing signals the ``/admin/dm-volume`` page surfaces — so the on-call operator can ``colony_get_cold_health()`` from a chat thread without screen-sharing the dashboard. Restricted to admins; non-admin callers get ``FORBIDDEN``. Response shape: { "tier_distribution": {"L0": 2, "L1": 14, "L2": 73, "L3": 9}, "at_cap": { "senders_with_activity": 22, "at_cap_total": 1, "at_cap_rate_pct": 4.5, "at_cap_by_tier": {"L0": 0, "L1": 1, "L2": 0, "L3": 0} }, "inbox_mode_counts": {"open": 92, "contacts_only": 4, "quiet": 2}, "inbox_adopted_pct": 6.1 } Numbers are live (Redis ZSET scan + 1 SQL query for each section). No Phase 3 gating decisions are made here — this is the same eyeball surface as the admin tile, exposed over MCP for chat-bot use.

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

Analyze a SQL query and return optimization suggestions including index recommendations and query rewrites.

Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.

Execute a SQL query against the project database. Returns columns, rows, row count, and cost breakdown. Destructive statements (DROP/TRUNCATE/bulk DELETE) require a two-step confirmation: the first call returns code CONFIRMATION_REQUIRED with a confirmation_token — re-call with that value in confirm_token to execute.

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.

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.

Return individual raw honeypot events with all fields. Use when the user wants to see actual records: 'show me events from this IP', 'what hit port 443 last week', 'events from Russia yesterday'. Filters: source_ip, country (2-letter code), asn (e.g. 'AS12345'), dest_port, protocol ('tls' or '' = the coarse TLS/raw-TCP signal), app_protocol (nDPI L7 protocol label: 'bittorrent', 'ssh', 'rdp', 'mssql-tds', 'mining', 'rtsp', 'smbv1', ... — find everything speaking a protocol regardless of port), http_method, request_header (substring of the masked HTTP request headers), ja4/ja3 (exact TLS client fingerprint), has_client_cert (true = only events where the client presented an mTLS cert), ip_version (4 or 6 = only IPv4 or IPv6 sources). since/until are ISO-8601 UTC strings. Each record includes: source_ip, country, asn, dest_port, user_agent, url_path, http_request_headers, tls_client_ja4, tls_client_ja3, http_request_ja4h, ssh_client_hassh, tls_client_cert_subject/issuer, event_sequence, event_duration, source_bytes/dest_bytes/network_bytes, network_protocol, application_protocol, timestamp.

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.

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.

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.

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.

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.

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.

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.

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.

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.

One tool's full risk classification on a published MCP server: category, severity, risk analysis and evidence, OWASP classes, parameter schema and the recommended policy default. Use when deciding whether to allow a specific tool call, e.g. "should execute_sql on this server be permitted?"

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.