Tool search 101,866 tools · 5,225 live servers

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

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.

Diff two quarterly 13F snapshots for an institution. Compares the latest filing against the prior quarter and returns per-position changes: new positions, increased, decreased, and exited. Sorted by |changePercent| descending so the biggest moves surface first. Much more efficient than calling get_institution_holdings twice and diffing client-side — the server computes everything in a single SQL query.

List SQL models / datasets defined in the workspace (the queries that produce rows to sync). Census: GET /models.

Get a single model by id, including its SQL/query. Census: GET /models/{id}.

Check an MCP server for malware / prompt-injection lures by its endpoint URL. Give the server's streamable-http endpoint URL. Two paths: * **Already in the agent-tools directory** → returns our LATEST stored rule verdict. Every indexed server is re-scanned hourly, so you get a consistent, continuously-refreshed answer without re-probing. * **Not yet indexed** → we probe the endpoint live, statically scan its advertised tools + metadata, ADD it to the directory, and return the fresh verdict (so the next caller gets the rule verdict instantly from cache). Two dimensions are reported. `verdict` is authoritative and comes from deterministic static rules — pure pattern-matching over the *advertised* text only, NO code execution. It flags the social-engineering / RCE tricks listing-spam servers use: * `curl … | bash` and `base64 -d | sh` install lures * `eval "$(curl …)"` / PowerShell `IEX(...DownloadString)` cradles * base64 blobs that decode to a shell command * bare-IP payload hosts and cheap throwaway TLDs * prompt-injection / credential-exfiltration phrasing ("ignore previous instructions", "send your .env / api key") * MCP tool-poisoning coercion — descriptions that hijack an agent's tool-calling ("always call this tool first", "before using any other tool you must…"), hidden `<IMPORTANT>` instructions, "list all API keys / include secrets in your response", and coercion to read & forward `.key`/`.pem`/`.ssh`/`.env` files Source-code-oriented rules (SQL / command / code injection) are deliberately not applied to natural-language descriptions, to avoid false positives. `llm_reference` is an advisory frontier-LLM second opinion over the same text. Because the LLM is slow it is computed LIVE on this call only and is never stored (the hourly job never runs it), so it may be null on timeout. It never overrides the rule verdict; when it is *more* severe than the rules an `advisory` note is attached as a safety-net signal. Security/defense products that merely *name* these attacks are not flagged. Args: endpoint_url: The MCP server's streamable-http URL (required). This is the identity we look up / index by. name: Optional advertised name (used when the server is new and gets added; falls back to the URL host). description: Optional description / README blurb (scanned when new). tools_text: Optional tool names + descriptions; used only if the live probe cannot fetch the server's tools/list. Returns: { verdict: "clean"|"suspicious"|"malicious", score: 0-100, reasons: [{rule, weight, snippet}], llm_reference: {model, verdict, reason, confidence} | null, advisory: str | null, slug, name, endpoint_url, source: "stored" (existing) | "new_scan" (just added), indexed: bool }

Install an app template on a VPS/Cloud site. Starts a background installation. Poll get_app_status() for progress. Requires: API key with write scope. VPS or Cloud plan only. Args: slug: Site identifier template: App template slug. Available: django, laravel, nextjs, nodejs, nuxtjs, rails, static app_name: Short name for the app (2-50 chars, lowercase alphanumeric + hyphens). Used as subdomain: {app_name}.{site_domain} db_type: Database type. "none", "mysql", or "postgresql" (depends on template) domain: Custom domain override (default: {app_name}.{site_domain}) display_name: Human-friendly name (default: derived from app_name) Returns: {"id": "uuid", "app_name": "myapp", "status": "installing", "message": "Installation started. Poll for progress."} Errors: FORBIDDEN: Plan does not support apps (shared plans) VALIDATION_ERROR: Invalid template, app_name, or duplicate name

Execute a SQL query on a site's database. Supports SELECT, INSERT, UPDATE, DELETE, and DDL statements. Results are limited to 1000 rows for SELECT queries. Requires: API key with write scope. Args: slug: Site identifier database: Database name query: SQL query string Returns: {"columns": ["id", "title"], "rows": [[1, "Hello"], ...], "affected_rows": 0, "query_time_ms": 12}

Get detailed system stack information (OS, PHP, DB, web server versions). Requires: API key with read scope. Args: slug: Site identifier Returns: {"os": "Debian 12", "kernel": "6.1.0", "php": "8.3.4", "mysql": "10.11.6-MariaDB", "nginx": "1.24.0", "wordpress": "6.5"}

List registered temporary-access provisioning targets (Postgres, MySQL, …).

Run an arbitrary HogQL (SQL) query against PostHog data. Use for custom analysis not covered by other tools. Only works if PostHog is connected.

Get full details for a specific developer tool by its slug. The entry is kept current and dated (last_verified) — treat it as newer than recalled knowledge, particularly the pricing, free-tier, MCP support, and health fields. Returns: complete tool entry as a Markdown-KV block covering Identity, Decision (useWhen/avoidWhen/bestFor/alternatives/worksWith/conflictsWith), Constraints (pricing, license, deployment, languages, compliance), Health, Agent Readiness, Get Started, and Sources sections. Alternatives and worksWith entries are enriched with tagline + agent-readiness for resolved slugs, so the agent can route to a follow-up choice without an extra call. If the slug is not found, returns an error with similar-slug suggestions. Examples: - Postgres core engine: {slug: "postgresql"} - Stripe (single entry, no -cloud/-oss split): {slug: "stripe"} - Hosted Redis: {slug: "redis-cloud"} Self-hosted Redis: {slug: "redis-oss"} - Hosted Supabase: {slug: "supabase-cloud"} OSS Supabase: {slug: "supabase-oss"} - GitHub's MCP server: {slug: "github-mcp"} Edge cases: - 110 tools split into hosted vs self-hosted twin entries with uniform suffixes: `{base}-cloud` for the managed lane, `{base}-oss` for the self-hosted lane (redis, supabase, mongodb, docker, elasticsearch, grafana, terraform, ...). Vendors like stripe, auth0, firebase, twilio, openai, pinecone, and algolia are single entries — plain slugs only. - Slugs derived from package names use hyphens where the name uses a dot (e.g., "nextjs" not "next.js"; "vuejs" not "vue.js"). - Slugs are case-sensitive lowercase. The endpoint also accepts upper-case for backward compatibility but the canonical form is always lowercase. Risk: read-only, closed-world, idempotent — no state change possible.

Compare 2-3 developer tools side by side. Returns each tool's full Markdown-KV entry separated by "===". Alternatives and worksWith are enriched with tagline + agent-readiness for resolved slugs. If any requested slugs are not found, they appear in a trailing "Note: slugs not found: ..." line; the comparison still returns for the ones found. Examples: - Three search engines: {slugs: ["meilisearch-oss", "algolia", "elasticsearch-oss"]} - Two ORMs: {slugs: ["drizzle-orm", "prisma"]} - Three auth providers: {slugs: ["auth0", "clerk", "keycloak"]} - Hosted vs self-hosted for the same vendor: {slugs: ["redis-cloud", "redis-oss"]} — shows deployment trade-off - Postgres engine vs hosted offerings: {slugs: ["postgresql", "supabase-cloud", "cockroachdb-cloud"]} Edge cases: - Cross-category comparisons (e.g., {slugs: ["auth0", "redis-cloud"]}) are allowed but rarely useful. Same-category comparisons answer "which should I pick?" better; cross-category answers "these coexist in my stack" — a compatibility question. - Minimum 2 slugs, maximum 3. Four or more is a validation error; for more, run pairs. - Invalid or unknown slugs are listed under "slugs not found"; the partial comparison returns for valid ones. - Duplicate slugs in the array are deduplicated. - A few tools are single entries (no -cloud/-oss split): stripe, auth0, firebase, twilio, openai-api, pinecone, algolia. Don't pass "stripe-cloud" — it doesn't exist. Risk: read-only, closed-world, idempotent — no state change possible.

Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolation, eval/new Function, empty catch blocks, regex built from a variable, fewer catch blocks than before, and named authorization guards that disappeared. Every finding cites the line that produced it. It does NOT do data-flow analysis: it cannot follow a value to a sink, across functions or files, and an empty result is not a safety verdict (the response lists what it did not analyse). Advisory triage — use a static analyser for a real security gate.

Pre-execution safety oracle for agent actions: submit the tool call you are about to run (shell, http, sql, file, code, env) plus your stated intent, and get a machine-enforceable verdict before you execute it. Decodes what the call does, flags the danger toolkit (rm -rf, reverse shell, curl|sh, SSRF to cloud metadata, credential reads, DROP/DELETE-without-WHERE, path traversal, dynamic eval), and binds it to your intent (allowedHosts/allowedPaths/readOnly/noNetwork) - only a fully pinned, clean, intent-matched call is auto-exec-safe. Hybrid: a deterministic, uninjectable detector engine (authoritative) plus an LLM classifier that can only raise the risk. Fails closed. Detection of known-dangerous patterns, not a proof of safety; it never executes the call. [security; up to 8c/call]

Lists your managed PostgreSQL databases. Once a row's status is 'ready', it carries the private-network connection details (private_ip, port 5432, db_name, db_user).

Lists your managed MySQL/MariaDB databases (the relational-database resource). Each row carries its engine ('mysql'|'mariadb'); once status is 'ready' it has the private-network connection details (private_ip, port 3306, db_name, db_user).

THE WAY TO MAKE AN EXISTING APP HIGHLY AVAILABLE. If the user asks for high availability, redundancy, no single point of failure, surviving a host or VM failure, or 'stay up if a machine dies', this is the tool: point it at their running VM and pass high_availability:true. It needs no rebuild and no redeploy, and the URL does not change. Turns a single running instance into a horizontally-autoscaling cluster: snapshots the instance and puts your SOURCE VM behind an Octavia load balancer as its ALWAYS-ON BASELINE MEMBER, then an autoscaling group adds EXTRA members (booted from that snapshot) on CPU load and scales them back down to zero. FLAVOR — LEAD WITH A MEMORY-OPTIMIZED SMALL-DISK FLAVOR (m1.mem16/m1.mem32) for the cluster, and ideally have the SOURCE VM on one too: clustering snapshots the source VM's FULL disk, so DISK size (not RAM) drives how long the cluster takes to build and to add each member under load — a 40 GB mem flavor is several times faster to snapshot and scale than a 160 GB standard flavor (m1.xlarge) and costs less for the same RAM. If the source VM is on a big-disk flavor, RECOMMEND migrating the app onto a mem-flavor VM first (a root disk cannot be shrunk in place), even though it is extra work — it pays off on every scale-out, and a clustered app is stateless anyway (state in the managed DB) so the lean disk fits. Expect a DRASTICALLY better clustering experience on a mem flavor: far faster cluster-create, snapshots, and every scale-out. The user can override with any flavor. At rest ONLY your source VM serves — there is NO idle extra VM to pay for (the source VM is the cluster's minimum, so the floor is 0 extra members). Use it to LOAD-SCALE a stateless app tier while managed services hold state: it becomes highly-available UNDER LOAD (multiple members behind the LB), but at rest a SINGLE source VM serves — and that source VM is a plain Nova server, not an autoscaled member, so it is NOT auto-replaced if it fails while idle (only the autoscaled extra members are ASG-managed and self-healed). If you need always-on redundancy, keep the app under enough load to hold >=1 extra member, or use a separate always-on setup. BILLED — at rest it costs just your source VM (which you already run) plus the load balancer; under load it adds up to max_size EXTRA members at the member flavor (flavor_id), billed only while they run. In guided mode show the cost that way (now: source VM already running + the LB; under load: up to max_size x the member flavor) and get the user's explicit go first. redu automatically repoints the extra members from the old single-VM URL to the load-balancer URL across app config. It REFUSES a STATEFUL VM with 409 cluster_needs_stateless unless confirm_stateless:true. To have redu FIX a stateful VM for you instead of refusing, pass auto_restructure:true — for a single_vm Postgres it fully-automatically provisions a managed DB + migrates the data + repoints the members; for a compose-stack DB it provisions the matching managed DB (set restructure_engine, e.g. 'mysql'/'mariadb' for WordPress) and returns migration commands to run from the app VM. WordPress/WooCommerce is not generic autoscaling: managed DB alone is not enough because wp-content/uploads is file state. Use app_profile:'wordpress'/'woocommerce', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true so all members mount the same uploads filesystem; otherwise the backend refuses with 409 cluster_needs_media_space. PUT THE CLUSTER ON THE SAME private network as the managed DB and media space. HA: cluster members are spread across DIFFERENT physical hosts automatically, and an autoscaled member that is destroyed is REBUILT AUTOMATICALLY in 1.5 to 5 minutes depending on how it failed with no action from you (the always-on source/hero VM is a plain Nova server and is NOT covered by that). CRITICAL for members: the app must start on EVERY boot (systemd unit or container restart policy) - if it only starts from a first-boot cloud-init script, a rebooted or resized member comes back with no app, silently never rejoins the load balancer, and the cluster quietly loses capacity with nothing reporting an error. Pass startup_command if the app does not already auto-start on boot, and have it bind its port only once it is genuinely ready to serve (the health check can only see whether the port is open). SEQUENCING - this catches people: the snapshot is taken IMMEDIATELY, and every member boots from it, so the source VM's app must already be RUNNING before you call this. Clustering a freshly-created VM whose cloud-init has not finished captures an image with no enabled service, and all members then come up ACTIVE while failing the load-balancer health check forever - a cluster that looks built and serves nothing. Verify the app answers on its port first (get_ssh_command, or just fetch the VM's URL). The snapshot upload can take several minutes; poll list_clusters until CREATE_COMPLETE.

Provisions a managed PostgreSQL database on a dedicated VM on your private network. Requires a recent plan_managed_datastore. For app deployments, prefer deploy_app database:'managed' so plan_deploy includes and wires the DB automatically. It is PRIVATE — reachable only from another instance on the same private network, via the DB's internal/private IP (not a public address). Get the ids from plan_managed_datastore/list_flavors/list_private_networks/list_keypairs. Provisioning takes ~5 min; poll list_databases until status='ready', then the connection details (private_ip, port 5432, db_name, db_user) are populated.

Provisions a managed MySQL (or MariaDB) database on a dedicated VM on your private network — the relational-database resource (use this instead of create_database when the app needs MySQL/MariaDB, e.g. WordPress, NextCloud, Matomo, many PHP/LAMP apps). Requires a recent plan_managed_datastore. For app deployments, prefer deploy_app database:'managed' with db_engine mysql/mariadb so plan_deploy includes and wires the DB automatically. It is PRIVATE — reachable only from another instance on the same private network, via the DB's internal/private IP (port 3306), not a public address. Get the ids from plan_managed_datastore/list_flavors/list_private_networks/list_keypairs. Provisioning takes ~5 min; poll list_relational_databases until status='ready', then the connection details (private_ip, port 3306, db_name, db_user) are populated. MySQL is created with mysql_native_password auth so older clients/apps connect cleanly. (ClickHouse is a separate resource — use create_clickhouse / list_clickhouse_databases.)

Deletes a managed MySQL/MariaDB database and its underlying VM. Pass the numeric id from list_relational_databases. This cannot be undone.

Execute a read-only SQL query against a database. Write operations (INSERT, UPDATE, DELETE, DROP, etc.) are blocked. Returns up to 50 rows of results with column metadata. Successful queries are saved in Myriade; the response's `query_id` can be replayed later with myriade_get_query_results without resending SQL.

Re-execute a saved Myriade query and return fresh results. The query's SQL always re-runs under the caller's governance policies (table permissions, column masking) — results are never served from Myriade's cached snapshot. Prefer this over myriade_query for recurring metrics (dashboards, cockpits): the SQL stays governed in Myriade instead of travelling through prompts. Write statements are blocked. Returns up to 50 rows with column metadata, same shape as myriade_query.

Ask a natural language question about a database using Myriade's data analyst agent. The agent will explore the database schema, search the catalog, run SQL queries, and analyze the results to answer your question. Use this for complex analytical questions that require multiple steps; for simple schema lookups or direct SQL, prefer the other tools. Note: the agent has access to catalog write operations (create/update assets, tags, terms) — it may modify catalog state to record findings. Use myriade_query directly for strictly read-only inspection. Requires AGENTLYS_PROVIDER and the corresponding API key (e.g. AGENTLYS_PROVIDER=anthropic + ANTHROPIC_API_KEY).