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650,522 tools. Updated 2026-10-14 03:03

"An open-source vector database for similarity search and AI applications" matching MCP tools:

  • Search messages by content. Fulltext mode (default) substring-matches subject/body/addresses of EMAIL messages and returns `{items, pagination}` with cursor paging. Semantic mode ranks by vector-embedding similarity and returns `{results}` scored 0-1 — each result spans ANY channel (check the `channel` field) and includes the message id for email_get / email_thread_get follow-ups.
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  • Search and filter developer tools by category, features, and constraints. Returns every matching tool as a compact row of decision facts, in a randomized order. 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, freeToStart, 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: the number of matches, a breakdown of them (kind, cost to start, MCP support, edge, self-hosting), and one table row per match (slug, name, kind, cost to start, MCP, edge, self-host, twin, base score, last verified), up to 100 rows. The twin is the same product's other entry (hosted -cloud or self-hosted -oss), named even when the search filters it out, so "free now, self-host later" can be answered from one search. Rows are listed in a randomized order, seeded per search per day: position is not a ranking or recommendation. Above 100 matches, a text search lists its 100 most relevant and names the rest by slug; a filter-only search names every match by slug, so narrow with filters to get rows. Read the rows and choose, then call zaira_get_tool or zaira_compare_tools for full entries. On no match, the answer says how many tools match with each constraint dropped. Examples (ambiguous-case focus): - User wants "a vector database for RAG": {category: "vector-database", freeToStart: 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. - "Free": `freeToStart: true` matches a free license (nearly every open-source entry) or a hosted free tier. `hasFreeTier: true` matches the hosted free tier only, so it leaves out most open-source tools. Open source is free to use, not free to run. Risk: read-only, closed-world, idempotent — no state change possible.
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  • Search NSW development applications by council, suburb, postcode or lodgement date, newest lodgement first. Returns up to 100 development applications per call with address, status, cost, dwellings, storeys and development types, plus has_more for paging with offset. status, application_type, development_type and min_cost must be combined with council, suburb or postcode. Use get_application for the full record of a result. Costs $0.01 per call, paid via x402 in USDC on Base; clients without x402 support receive the payment requirements as an error result. Failed calls are not charged. Source: NSW Department of Planning, Housing and Infrastructure (NSW Planning Portal), CC BY 4.0.
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  • Get metadata for a specific Statistics Canada time-series (vector) by its vector ID. Returns the series title (e.g. "Ontario; Consumer Price Index, All items"), parent table product ID, coordinate within the table, release frequency, decimal precision, and whether the series is terminated. Vector IDs are 8–9 digit numbers assigned by Statistics Canada to uniquely identify each time series. Use series_data to retrieve actual observations. Source: Statistics Canada WDS, Canada Open Licence, no auth required.
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  • Reads one record in full by exact id: all standard fields, custom fields by slug, version, and the few related rows needed next (a candidate's applications with stage and status, a job's company and open application counts, a list's member ids). Requires an id from a search or list result. Use this before any update so you have expected_version. Never guess an id.
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Matching MCP Servers

  • F
    license
    Not graded
    quality
    B
    maintenance
    Enables stateless similarity search over pre-computed vector corpora using NMI and cosine fusion with entropy-calibrated weighting. Provides tools for ranking, scoring, outlier detection, and alpha calibration for AI agents.
    -
  • A
    license
    Not graded
    quality
    D
    maintenance
    In-memory vector store with TF-IDF vectorization and cosine similarity search, paid per call via x402 micropayments.
    MIT

Matching MCP Connectors

  • Search ILOSTAT's catalog of labour-statistics indicators by plain-language terms and filters. Each hit is one indicator with its datasets — one per available frequency (annual, quarterly, monthly) — plus breakdowns, coverage years, number of reference areas, source database, and last update; pass a dataset ID to ilostat_describe_indicator for its units and breakdown codes, then to ilostat_query_indicator or ilostat_compare_geographies for values. Every search term must match a word or word prefix in the indicator's label, subject, database, breakdown names, code, or definition; British and American spellings (labour/labor) match alike. Facet counts reflect all applied filters.
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  • Semantic (vector similarity) search across blog posts and projects — the same Cloudflare Vectorize retrieval the Ask chatbot uses, without the LLM call. Broader than search_posts (which only does exact substring matching on title/description/tags): finds conceptually related content even when the query words never appear verbatim. Returns scored chunks with deep-link URLs.
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  • Semantic search: find the beyts closest in MEANING to the query, in ANY language — English, Persian, Spanish, Turkish, Arabic, … . Use this when you have a theme, feeling, or idea rather than exact Persian words (e.g. 'feeling separated from your origin' → M1:1). Each hit carries a cosine-similarity score. status='unavailable' means the vector index is not built yet — fall back to `search`.
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  • Find originators similar to the given one using vector similarity (quote themes). Use after finding an author to discover related thinkers. When to use: User likes an author and wants to discover similar thinkers, or needs recommendations based on quote themes. Returns originators with similarity scores (0-100%). Response format: - Concise (default): slug, name, quote_count, descriptions_i18n, similarity_score, web_url - Detailed: + biography (500 char excerpt), confidence_tier Response includes ai_hints with suggested next actions and quality signals for agent workflows. Examples: - `originators_like(originator="Marcus Aurelius")` - similar philosophers - `originators_like(originator="Oscar Wilde")` - similar wits - `originators_like(originator="African Proverbs")` - similar proverb collections
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  • Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.
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  • Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and analyzing vector distributions in a vector DB.
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  • Search NavMDs' 7,400+ doctor directory with a natural-language query, e.g. 'board-certified facelift surgeon in Los Angeles with great reviews and free consults'. Powered by Gemini embeddings + cosine similarity over full doctor profiles. Best tool for open-ended or multi-attribute questions.
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  • Find ENS names semantically similar to a given name using vector embeddings across 3.5M indexed ENS names. Returns similar names with similarity scores, status (REGISTERED / GRACE_PERIOD / PREMIUM_AUCTION / AVAILABLE), and live marketplace data. Names in GRACE_PERIOD are NOT registerable by anyone but the original holder.
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  • List the candidate's own job applications (newest first) with status, dates, the originating interview score, and a job summary. Returns only the caller's applications — no jobId input, so it is not an enumeration surface.
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  • Semantic search over the workspace knowledge base. Returns the most relevant chunks (with source document title and similarity score) for a query. Only searches documents that have finished training (status READY).
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  • Pure semantic (vector) search — best for paraphrased queries, concept exploration, "papers arguing X" type questions. Uses dense vector similarity via Gemini or SPECTER2 embeddings. Skips BM25 fusion which can introduce term-matching noise. For exact terms use "search_keyword". For mixed queries use "search".
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  • List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps
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  • Semantic-only vector search over the memory, returning source excerpts. Prefer cortex_ask, which also uses full-text and graph expansion; reach for this when you specifically want nearest-neighbour matches on meaning.
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  • List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps
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  • List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps
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