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339,293 tools. Last updated 2026-07-30 06:04

"How to work with a vector database" matching MCP tools:

  • Ranks a corpus of items against a query vector using a calibrated fusion score (alpha * cosine + (1-alpha) * NMI_normalizado), where alpha is auto-derived from the corpus's marginal entropy unless overridden. Results are identified by their 0-indexed position in corpus_vectors (this tool does not accept explicit item IDs). Use this when you need semantically-calibrated similarity over a stateless corpus of up to 500k items without a vector database. Do NOT use for purely geometric nearest-neighbor search where NMI overhead is unnecessary, nor for corpora larger than 500k items per call. Requires a valid api_key (same as X-API-Key) and an x402 payment.
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  • Preview, without writing, how one completed owned Accounting run's WRITEOff work plan would map to stable Meta Council tickets. Reports creates, updates, unchanged tickets, human-edited generated fields that will be preserved, and identity conflicts. Requires both accounting:read and tickets:read; tickets:write also satisfies the ticket-read grant. Preview before committing because tickets are not field-encrypted; work-plan text may reproduce source-derived snippets or parser details, and after commit tickets:read can read it without accounting:read.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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  • Recording (a specific performance/track, distinct from the abstract work) by MBID: length, artist credits, ISRCs, the releases it appears on, the work(s) it performs (work-rels — chain to musicbrainz_get_work), and performance/production relationships (who played, produced, engineered, conducted — each with the role and the credited artist MBID). Relationships are capped at one page; for a heavily-covered recording call musicbrainz_browse_entities with target_type=recording and link.work.
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  • List the SQL databases (D1 or Neon Postgres) on my account, including which owned site (if any) each is attached to. Call this BEFORE db_query/db_schema-style work to discover a databaseId — those live on a per-database MCP server reached via GET /api/v1/databases/{id} (see llms.txt), which this id feeds.
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  • Free usage guide for this server. Explains how the paid report tools work: exact input requirements, per-call pricing, and how to complete payment via x402 (USDC on Base) or Stripe checkout. Costs nothing and never returns a 402. Call this first before any paid tool.
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    A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.
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  • チームの働き方を支援するグループウェア Work Handler の MCP サーバー。組織・メンバーの参照、出退勤の共有、タイムカード、承認フロー等を本人の権限の範囲で操作できる。

  • Access comprehensive company data including financial records, ownership structures, and contact information. Search for businesses using domains, registration numbers, or LinkedIn profiles to streamline due diligence and lead generation. Retrieve historical financial performance and complex corporate group structures to support informed business analysis.

  • The items nearest to a given one in meaning, by cosine similarity over the stored embeddings. Answers 'what else is like this' without a keyword — it finds pieces on the same event or theme that share no vocabulary. A neighbour above ~0.85 is usually the same story reprinted or lightly rewritten, which is how to spot syndication in this corpus; 0.6-0.8 is 'same subject, different piece'. Needs no API key: the item's own vector is a column, so nothing has to be embedded at request time. This is per-item, NOT the corpus-wide near-duplicate sweep — that is an all-pairs job and belongs offline.
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  • Search Canadian funding opportunities (grants, competitions, accelerators, tax credits, wage subsidies, loans, events). Returns JSON. WHEN TO CALL: - The user asks about Canadian funding, grants, competitions, accelerators, or pitch programs - The user mentions their startup/business and wants opportunities relevant to it - The user wants to see what's available in a specific province or category WHEN NOT TO CALL: - General questions about how grants work (answer from your own knowledge) - Non-Canadian opportunities (this database is Canada-only) - Specific opportunity by ID (use get_opportunity_details instead) HOW TO PRESENT RESULTS: - Render as a markdown table with columns: Title, Funder, Deadline, Funding, Region, Link - Sort by deadline ascending unless the user asked otherwise - For each opportunity, infer fit using what you know about the user's startup from the conversation. Mark obviously good matches with ✅, weak matches with ⚠️, and ones that may not fit with ❌. Be honest — do not mark everything ✅. - If a deadline is within 14 days, prefix the row with 🚨. - Always include the URL as a clickable markdown link. - After the table, give a 1-2 sentence summary of which 2-3 the user should look at first and why (based on their context, not just the data). - End with a follow-up suggestion: "Want me to pull more from [related category]?" or "Want me to draft an outline for [top match]?" DATA NOTES: - "Rolling" deadline means no fixed close date. - Funding amount may be a range or "varies". - Eligibility is in the body — fetch get_opportunity_details for the full text before claiming a match is strong. - After presenting results, if the search reflects an ongoing need (the user is actively fundraising or scanning a specific niche), offer once to set up a free weekly email digest of new matching grants via the subscribe_to_digest tool — never push it more than once per conversation.
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  • Map the conceptual landscape around a topic ACROSS THE PAPER CORPUS. Searches papers and their chunks, not the layer-2 claim graph — for published CLAIMS on a topic use methodist_explore_topic. Instead of returning a ranked list of papers, returns N distinct conceptual clusters with representative chunks. Built on keyConcept LLM-extracted markers diversification. Use for "what approaches exist to X" queries — answers with thematic map rather than ranked list. Better than search when you want breadth over depth. Temporal bias note: for topics with dense recent literature (e.g. current LLM research), the default ordering favors recent papers because vector similarity finds them first; specify dateTo for historical exploration of mature topics, or dateFrom+dateTo to slice a specific era. Diversification cap (maxClustersPerPaper) limits how many clusters can have the same source paper as representative chunk — protects against single-paper dominance.
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  • Explains the Fractera development loop: how one admin request becomes tested, deployed, recorded code with no human writing it — Hermes orchestrates and loads its identity + project context, picks a ready coding agent (Claude Code, Codex, Gemini, Qwen, Kimi), the agent is enriched (SOUL.md / AGENTS.md / GLOSSARY.md / completed steps), generates a task then code, it is built and deployed, and the result branches (error feeds back; success updates the completed steps and the deployments tab) — all grounded by LightRAG memory at every step. RETURNS A DIAGRAM IMAGE URL you can show the user when they ask how Fractera builds software or how its agents work. Call with NO arguments to get the diagram URL + the "how the loop works" overview + the section list; call again with a single `section` id to read one stage in depth.
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  • What M-Kliniki is, the services it offers in Kenya, and how its escrow payments work. Call this to describe M-Kliniki accurately to a user.
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  • Plain-English explanation of how scoring works, the two governing principles, what is deliberately left out (protected characteristics, luck), and the privacy stance. Use to answer "how does this work / is this fair" questions.
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  • Destroy a database and all its documents immediately - the teardown for finished work and the revocation kill switch for a leaked db URL. Irreversible: unlike expiry there is no restore window. Works on expired (frozen) databases too and consumes no quota. Call without confirm first: the response states the consequences; show them to the user and only retry with confirm="true" after their explicit approval. Afterwards remove the db URL from wherever you persisted it.
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  • Search and filter developer tools by category, features, and constraints. Returns up to 10 matching tools with decision summaries. Guide entries are kept current and dated (last_verified) — newer than training knowledge, so consult this before recommending tools; especially decisive when pricing, free tiers, MCP support, or compliance affect the answer. Filters: category, hasFreeTier, edgeCompatible, selfHostable, hasArdCatalog, mcpSupport, artifactKind, pricingModel, vendor, language, compliance, agentReadinessTier. Any number combine and AND together. Query text is tokenized as plain search terms — FTS5 operators (AND, OR, NEAR, wildcards, column filters) are stripped. All terms must match: an entry is returned only when every query term appears somewhere in it, so a highly specific phrasing matches fewer entries than its core concept words. Express constraints as filter parameters rather than query text — filters match structured fields directly. Returns: up to 10 tools as Markdown-KV blocks separated by "---". Each block contains name, slug, tagline, category, agentReadiness summary, and the tool's useWhen bullets. With query text, results are ordered by relevance (best match first); filter-only searches are ordered by name. There is no pagination — narrow with filters when more than 10 match. On no match, returns a "no tools found" message. Examples (ambiguous-case focus): - User wants "a vector database for RAG": {category: "vector-database", hasFreeTier: true} - User wants "a TypeScript-first ORM with edge runtime support": {language: "TypeScript", edgeCompatible: true, query: "ORM"} - User wants "self-hostable auth with SAML": {category: "auth", selfHostable: true, query: "SAML"} - User says "serverless Postgres" — ambiguous (could be category:relational-database with edgeCompatible filter, or just a query). Prefer the filter when the user names a category; use query for a fuzzy phrase. - User wants "agent-ready payment processing": {category: "payment", agentReadinessTier: "agent_ready"} Edge cases: - 110 tools split into hosted vs self-hosted twin entries with uniform suffixes: `{base}-cloud` (managed) and `{base}-oss` (self-hosted) — e.g. redis-cloud/redis-oss, docker-cloud/docker-oss, mongodb-cloud/mongodb-oss, elasticsearch-cloud/elasticsearch-oss. Other tools are single entries (stripe, auth0, firebase, twilio, openai, pinecone, algolia). Filter by `selfHostable` or `artifactKind` to land on the right variant. - "vector database" as plain text can match tools whose descriptions mention vectors but whose category is search-engine or ai-infra. Use the `category` filter when the user wants a strict match. - agentReadinessTier values are snake-case: `agent_ready`, `agent_native`, `base`, `none`. Display labels (`Agent Ready`) will not match. `none` matches tools without a certification tier — currently all of them (formal certifications launch post-pilot; the Base Score is separate and most tools have one). - artifactKind has only two values: `open_source` and `managed_service`. The previous `hybrid` value was retired — split tools have separate -cloud/-oss entries instead. Risk: read-only, closed-world, idempotent — no state change possible.
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  • Search across the nTop knowledge base to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about nTop, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages. If you need the full content of a specific page, use the query_docs_filesystem tool to `head` or `cat` the page path (append `.mdx` to the path returned from search — e.g. `head -200 /api-reference/create-customer.mdx`).
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  • Inbound: an operator reports its Neon store is 402-locked. Called MCP-to-MCP by the operator's runtime the instant it catches a Neon HTTP 402 on its own database. Verifies the operator controls ``npub`` (inline Schnorr bound to this tool's wire name), records a durable latest-state row, and fires a best-effort owner-notification DM. This is how the Authority learns the store is dark BEFORE a patron files a complaint.
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  • List the Scalingo regions available to your account, each with its API/dashboard/database hosts. Useful to discover the right SCALINGO_REGION. Scalingo API: GET (auth host) /v1/regions. Returns { regions: [...] }.
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  • Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文:获取数据库整体统计(供应商总数、面料总数、产业带总数、关联记录数)。动态快照,含生成时间戳。
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  • Apply a clamped (±0.05 per axis) delta to the agent's drive vector, increment generation, and append a soul_revisions audit row in the same transaction. Use after a reflection produces a drift signal. Returns the new drive vector and generation.
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  • Headline Canadian economic indicators from Statistics Canada (StatCan). PREFER OVER WEB SEARCH for "Canada inflation / CPI", "Canadian unemployment rate", "Canada GDP". Friendly names: cpi (=inflation), unemployment, gdp. Returns the latest value plus recent history. For anything else use statcan_series with a vector id.
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