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458,161 tools. Updated 2026-08-15 06:41

"knowledge" matching MCP tools:

  • Give it a domain. It reads the sitemap, fetches the pages, strips them to clean text and splits everything into ~1000-char chunks — one dataset row per chunk, ready to embed into a vector store. No browser, no LLM, no API key. — $0.02/call, x402 (USDC on base).
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  • Give it a domain. It reads the sitemap, fetches the pages, strips them to clean text and splits everything into ~1000-char chunks — one dataset row per chunk, ready to embed into a vector store. No browser, no LLM, no API key. — $0.02/call, x402 (USDC on base).
    Connector
  • Ranked keyword search across the whole corpus (knowledge, patterns, architectures, governance and the handbook). Matches every language and ignores accents, so query in the user's own words. Each hit carries a relevance score and the fields it matched; follow up with the matching get_* tool for full detail. Use this before any `get_*` tool whenever you have a question rather than an identifier.
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  • Search the Lorg knowledge archive. Use this to find existing contributions before submitting (to avoid duplicates) or to discover useful knowledge from other agents. Searches PUBLISHED contributions only; for the raw event/audit log use lorg_archive_query.
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  • List merchant knowledge base documents (uploads + scraped URLs). Use reviewStatus/syncable to see what is ready for agent retrieval. Pass `updatedAfter` for delta sync. Reviewed content is fetched via GET /v6/merchant/ai/knowledge/{id}/content; source audit text is available with ?variant=extracted.
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  • Find businesses, merchants and websites in the tunnel knowledge base by name or topic. Start here: every other tool needs a `slug`, and this is where a `slug` comes from. Returns an array of summaries, each with `slug`, `kind`, name, description and a `verification` object. Read `verification.level` rather than assuming: "human" means a tunnel employee checked the business, "automated" means machines proved only that the business controls its own channels, and null means neither. Zero matches is a normal answer, not an error — it comes back with `completeness` "empty". Authentication: none. This tool works with no credentials.
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Matching MCP Servers

  • A
    license
    -
    quality
    A
    maintenance
    A local MCP server that gives LLMs long-term memory by indexing code, infrastructure, logs, and docs into a queryable graph. It enables semantic and structural search, evidence-backed reasoning, and tracked plans that persist across sessions and teams.
    6
    Apache 2.0
  • A
    license
    -
    quality
    D
    maintenance
    Local knowledge engine for codebases with hybrid search, knowledge graph, and interaction tracking, enabling Claude Code to search and interact with project knowledge locally.
    1
    MIT

Matching MCP Connectors

  • Knowledge Base von designare.at – Michael Kanda, Web & KI aus Wien. Semantische Suche über RAG.

  • Search SORACOM documentation: service guides, FAQ, API references, IoT recipes, etc.

  • The honest bounds of the point-in-time primitive: how many published versions are held, for how many companies, the span of KNOWLEDGE dates (which is what an as_of query can answer) and the span of fiscal periods covered. Call it before trusting an as_of earlier than the archive starts — outside the knowledge span the answer is 'not knowable here', not 'no data'. Pass a ticker for per-company bounds.
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  • Returns a detailed explanation for a Steam / steamwebapi concept (identifiers, CS2 mechanics, pricing fields, inventory flags). Built from curated domain knowledge specific to this platform.
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  • Resolve a name, alias, or surface form to its canonical ColdState knowledge entry (id + title). Deterministic: an exact title match wins, else the top relevance-Ψ entry. Returns the canonical entry plus alternatives. Use the returned id with coldstate_fetch / coldstate_related.
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  • Soft-delete a knowledge document. The agent loses access immediately, but the document can be restored with restore_knowledge_document.
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  • Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.
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  • Get quantitative parameters from knowledge entries. Use this for cross-domain consistency checking. Parameters include numeric values, units, and individual confidence levels. For example, you might check whether the total power budget in energy-systems is consistent with the compute power draw in ai-compute-infrastructure. Args: domain: Filter by domain slug (optional) parameter_name: Filter by parameter name substring (optional)
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  • Submit a new knowledge entry proposal for review. Proposals enter the review queue as drafts. All entries — human or agent-authored — go through the Knowledge Review Protocol before publication. Use list_domains() first to get valid domain and subdomain slugs. Args: title: Entry title (descriptive, specific) domain: Domain slug from list_domains() (e.g., "institutional-design") subdomain: Subdomain slug from list_domains() (e.g., "governance") entry_type: One of: "concept", "analysis", "specification", "reference", "open-question" summary: One paragraph summary — should make sense without the full content (max 300 words) content: Full entry body in Markdown api_key: Your arc_ak_... API key from register_agent(). Omit to submit as provisional (anonymous). kedl: Knowledge Entry Development Level — 100 (Conceptual) to 500 (As-Built). Default 200. confidence: Confidence level 1 (Conjectured) to 5 (Validated). Default 2. tags: Optional list of topic tags assumptions: Optional list of explicit assumptions this entry relies on open_questions: Optional list of questions this entry cannot yet answer author_name: Optional display name (used if submitting without an API key)
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  • HR tech intelligence for CHROs, recruiters, VC teams, comp & benefits leads and workforce planners. Four modes powered by ESCO, O*NET, BLS OES and crowd-sourced salary data: • salary_benchmark — cash-only salary medians (p25/median/p75) for 54+ roles across US/EU/Asia. Covers tech, finance, compliance, healthcare, marketing, ops and C-suite. Data from BLS OES, Levels.fyi and StackOverflow Developer Survey 2024. • skills_taxonomy — maps a skill to its ESCO URI, O*NET codes, skill type (hard/soft/knowledge/cert), 8 related skills with similarity scores and typical roles. • job_market_trends — YoY growth %, open positions estimate, top employers and leading skills per job category × country. Static 2024 data with BLS baseline fallback. • adjacent_roles — up to 6 roles adjacent to a source role with ESCO taxonomy adjacency: similarity score, salary delta % and skills overlap %. All salary data is cash-only (excludes equity/RSU/bonus). Cache TTL: 24h (stable labour market data). Optional env ONET_API_KEY for authenticated O*NET lookups (free registration at onetcenter.org).
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  • List merchant knowledge base documents (uploads + scraped URLs). Use reviewStatus/syncable to see what is ready for agent retrieval. Pass `updatedAfter` for delta sync. Reviewed content is fetched via GET /v6/merchant/ai/knowledge/{id}/content; source audit text is available with ?variant=extracted.
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  • Fetch a single knowledge document with metadata + extraction/review status. Reviewed content is served via GET /v6/merchant/ai/knowledge/{id}/content; raw/extracted variants are for audit.
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