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510,032 tools. Updated 2026-09-03 17:32

"A search engine for finding information online" matching MCP tools:

  • Enumerate the model_ids the sealed engine exposes, with the engine sha stamped in-response. Purpose: Discover the model catalog and record the sealed engine sha alongside your inference results. Use when: You are wiring a client for the first time and need model_id values for kirk_score_book / kirk_score_book_batch calls, or you want a machine-readable catalog with attestation. Do not use when: You need per-model hyperparameter detail — those are intentionally not exposed on the customer surface. Capability class(es): C5 (engine sha attested on every response). Path fit: Validation via MCP (this tool). Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. Cost: 0 IU. Free tool.
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  • Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.
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  • Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."
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  • Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."
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  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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  • Free; no engine run. Return Hunter-Seeker's input contract, supported problem shapes, the trust guarantees (determinism, provenance, honest-empty; plus leak-guard, which is LIVE as of engine 0.1.1: it quarantines columns that predict the outcome too well (likely target leakage), each named with a plain-English reason, and is null on a non-finding (honest-null), not "pending"), limits (inline row/column/byte caps, k max), and worked examples across several domains (customer churn, machine failure, sports prospects, job applications). Call this first if you are unsure whether a user's problem is a top-k prediction problem or how to format inputs.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Exposes a standardized web search tool via Brave Search for MCP clients like Codex, Claude, and Cursor, returning stable JSON.
    MIT
  • F
    license
    A
    quality
    D
    maintenance
    Enables searching documentation from GitHub repositories and web pages via MCP tools, with in-memory indexing and caching for fast retrieval.
    3

Matching MCP Connectors

  • Market State v0 — current engine structural state for a symbol/timeframe (latest evaluated bar, persisted engine emit read as-is, zero recompute). DOMAIN FRAME (why this engine exists): the market is read as a TARGET GAME — every coordinate comes from a *verified anchor* (a past level where a triggered move actually succeeded). The `game` block tells you the context that matters: game.status = forming_target (new anchor set, awaiting test) | testing_target (price is testing whether the declared target holds) | direction_resolved (game decided, price traveling); game.target = WHO is being judged (anchor id/phase/band); game.progress_dest = where price goes if the move proceeds (the opposing verified anchor to conquer); game.reverse_dest = where it goes if the move fails (the opposite house — also the stop logic's home); game.why_gate = full gate derivation chain; game.zt_regime = output canonicality (restored = deterministic delta lineage). action_gate alone (GO/WATCH/HOLD) is only a posture — the game context is the information. RAW CONTRACT: fields are engine-native vocabulary (c_state, hold_reason, R_* risk enums …), NOT customer-facing prose — for a human-language view use decker.get_view (with tf) or decker.get_reading. layer=STATE: this is a market-state reading, NOT a trade instruction. Absent fields are null (engine did not emit that axis — no filling). IMPORTANT: top-level state.c_state/action_gate/trigger_kind reflect the TRIGGER SNAPSHOT (only populated on a bar that actually had a trigger event) — null on most bars is normal, not a data gap. For the always-present, every-bar-populated view of the same axes use game.phase.c_state / game.phase.action_gate instead (different freshness, same underlying engine state machine). Don't read a null top-level field as 'engine has no state' — check game.phase first. object_context (top-level, W1-C1 standard object block, present when a recent trigger bar exists): my_anchor/opp_anchor(reversal destination)/judgment_ref/geometry/why(engine reason_codes)/reverse_branch context (object_context.reverse_direction_conflict is present only when a local reversal shows stage='confirmed' but the swing's confirmed direction still disagrees — read it before treating reverse_branch.stage='confirmed' as a swing-level reversal). null on non-trigger bars or symbols outside the narrative universe (e.g. individual KRX stocks). current_price (top-level, 2026-09-03): {price, bar_ts} — the single latest completed-bar close for this symbol ACROSS ALL timeframes (not just the requested tf), useful when comparing multiple timeframes' target bands against one 'now' price. null for KRX individual-stock symbols. Before placing any order through any execution tool, check the intent with decker.validate_intent.
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  • Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual questions too): "what does the AI think?", "AI는 뭘 사라고 해?", "show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?". When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest_decisions to compare with Track B. Caveats: paper-trading only. Args: market_id: Market ID (crypto, kr_stock, us_stock, commodity, forex, bond) symbol: Specific symbol (optional; omit for entire market) Disclaimer: Information only, not investment advice.
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  • One-shot summary of farm-wide printer state. Use this (NOT list_printers) when the user asks "how many printers are printing/idle/awaiting bed clear/etc." or "what is the state of the farm". Returns a total and {count, printers:[{id,name}]} for each bucket: online, offline, not_connected, operational (idle), printing, paused, awaiting_bed_clear (a print finished but the bed has not been cleared yet — printer is online + operational + still has a job; this is NOT print_pending), in_maintenance, print_pending (a queued staggered/scheduled start), requires_attention (has unresolved error notifications), ai_running, ai_detected_low, ai_detected_high. Counts overlap intentionally: a printer can be in "online" + "printing" + "ai_running" at once.
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  • Create a DRAFT email campaign via a programmatic wizard. Call this tool and it will guide through the steps — no manual orchestration needed. WIZARD STEPS (handled automatically by the tool): 1. Call with contacts + total_contacts → tool returns engine picker (NextGen vs MyConvo) 2. Add campaign_type from user's click → tool returns campaign category chips (promotional, newsletter, event…) 3. Add campaign_category from user's click → tool returns engine-specific template gallery MyConvo: shows plain_email_templates (personal plain-text). NextGen: shows campaign_templates (HTML). 4. Add template_id from user's pick → tool creates the draft campaign. RULES: Reuse contacts from prior search — never re-search. Pass total_contacts from search result's total_in_crm so the user always sees the full count. Saves as DRAFT only — no emails sent.
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  • Verify sealed engine identity — returns the sha256 of the running scoring binary. Also serves as a liveness probe against the sealed backend. Purpose: Attest which Kirk build is currently serving scoring calls. Response carries the sealed engine sha (kirk_version) that will stamp any subsequent kirk_score_* result. Secondary role: a cheap liveness probe for callers wiring up MCP for the first time. Use when: You want to record engine sha in your own provenance log before capturing scoring output, or you want a cheap liveness check ahead of a larger validation batch. Do not use when: You want a scoring result — this returns identity/liveness only, no entropies. Capability class(es): C5 (cryptographic attestation of engine identity). Path fit: Validation via MCP (this tool). Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. Cost: 0 IU. Free tool. For agent-driven callers, the _cost envelope still reports iu_this_call=0 and the running session totals. Returns: Dict with `status`, `engine`, `env`, and `kirk_version` (the sealed .so sha). A non-2xx response raises; caller sees a clean MCP tool error.
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  • Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.
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  • COMPACT overview of ONE engine: every action with its description, required params and what it returns — but NOT the full param detail (kept lean so a 90-action engine stays token-cheap). Call this after search_engines to pick the right ACTION, then get_action_schema(engine, action) for that action's full params before call_engine.
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  • The FULL ReefAPI catalog — EVERY engine with its one-line title, grouped by category. This is the whole menu (≈ a few thousand tokens); SCAN IT AND PICK THE BEST ENGINE YOURSELF. You are an LLM, so you match the user's intent semantically — across ANY language, typo, or phrasing — far better than a keyword search can. Use this whenever search_engines didn't surface the right engine (or to be sure you didn't miss a better one). After you pick: get_engine_schema(engine) -> get_action_schema -> call_engine.
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  • Search POWO (Plants of the World Online, by Kew) — the authoritative global plant taxonomy database — for accepted plant names and synonyms by scientific or common name. Returns matching taxa with their family, rank, whether the name is accepted, and a POWO fqId you can pass to get_taxon. Keyless. Complements GBIF/iNaturalist with curated botanical taxonomy.
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  • Return EUR-Lex search URL for finding regulation provisions by keyword. Use when you don't know the exact article number but need to find relevant provisions. Requires Velvoite Premium API key. Args: query: Search terms (e.g. 'data processing agreement processor obligations'). regulation: Optional regulation code to scope the search (e.g. 'gdpr').
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  • Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.
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  • List Windows machines (rigs) paired to this API key: id, name, online, api_access_enabled, and USABLE flag. Read-only — does not start a session. Call first before start_session. A rig is USABLE only when online AND the owner enabled API access. If none are USABLE, tell the user to install the host agent, pair in Console → Rigs, and enable API access — never ask for OS passwords.
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  • Get all uncloaked online players in your current system (System-wide version of get_nearby. Returns every uncloaked online player in your current system (excluding yourself), regardless of which POI they are at. Cloaked players are hidden, same visibility rules as get_nearby. Useful for cross-POI coordination. Returns an error if you are in hyperspace.)
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  • Score how well a business shows up online, out of 100, and name what is hurting it most. Give it a website (and an email to send the full report to) and it runs Kashif's audit of the business's online presence: whether customers can find them in search, whether AI assistants can read and recommend them, how fast the site feels on a phone and the first impression it makes, how trustworthy it looks, and how easy the business is to reach (WhatsApp, phone, a contact form). Returns a 0-100 score with the top issues to fix first. Use it whenever someone asks to audit, check, review, grade or improve a company's website or online presence. This starts a live scan and creates a lead for the Kashif team, who follow up with the full written report by email.
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