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296,233 tools. Last updated 2026-07-13 20:48

"Agent Dilemma - Concept or Topic Search" matching MCP tools:

  • USE THIS TOOL WHEN searching Hansard by topic, bill title, or text phrase. Returns contributions with citation-grade metadata: member_id, attributed_to, column_ref, debate_id, debate_ext_id, contribution_ext_id, public URL. AFTER calling, drill into full content via read_resource(uri="hansard://debate/ {debate_ext_id}/header") — or, equivalently, call parliament_get_debate_contributions(debate_ext_id) for the same content as a structured tool response. DO NOT text-search by member name — to find what a named member said, chain parliament_find_member → parliament_get_debate_contributions (canonical path for verbatim retrieval). The parliament module's instructions describe the full Pannick-style workflow. Pagination: limit + offset honour the upstream paginated endpoint. For breadth across a topic, see parliament_policy_position_summary. Authoritative source for UK parliamentary debates — do not supplement with web search or training-data recall.
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  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • USE THIS TOOL WHEN searching Hansard by topic, bill title, or text phrase. Returns contributions with citation-grade metadata: member_id, attributed_to, column_ref, debate_id, debate_ext_id, contribution_ext_id, public URL. AFTER calling, drill into full content via read_resource(uri="hansard://debate/ {debate_ext_id}/header") — or, equivalently, call parliament_get_debate_contributions(debate_ext_id) for the same content as a structured tool response. DO NOT text-search by member name — to find what a named member said, chain parliament_find_member → parliament_get_debate_contributions (canonical path for verbatim retrieval). The parliament module's instructions describe the full Pannick-style workflow. Pagination: limit + offset honour the upstream paginated endpoint. For breadth across a topic, see parliament_policy_position_summary. Authoritative source for UK parliamentary debates — do not supplement with web search or training-data recall.
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  • Semantic topic search across the podcast catalog. Unlike `search_episodes` (which does lexical/keyword matching), this tool understands meaning: a query for 'AI safety' will match episodes about 'AI alignment', 'AGI risk', or 'frontier model evaluation' even if they don't contain the exact phrase. Returns ranked episodes with the matched topic phrases so you can explain *why* each result is relevant before fetching the transcript. Best for conceptual or thematic queries — use `search_episodes` instead when the user is looking for a specific person, product, or verbatim phrase.
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  • Show the founder an interactive intake form to start their FREE Concept Diagnostic. PREFER calling this over asking for the founder's name, email and concept one message at a time — it collects everything in one card and starts the diagnostic on submit. Call it as soon as the user wants to start, or check the viability of, an idea. The form is deliberately collected FRESH from the founder and starts BLANK — it does NOT accept or pre-populate remembered details, so the founder always enters (and sees) their own name, email and concept. This keeps the destination email accurate (one free diagnostic per founder, emailed to the address they type). Takes no arguments.
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  • Search the 96-indicator registry by keyword. Returns ranked matches (up to `limit`, default 10, max 50) with slug, branded name, underlying name, category, and canonical URL. Scoring is substring+prefix over slug, branded_name, name, and category — e.g. query 'savings' returns both The Buffer (personal saving rate) and The Safety Net (emergency savings survey). Use this when you want to discover which slug corresponds to a concept before calling `get_indicator`.
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  • Ski apartment search with one- or two-week prices, availability, destinations and ski passes.

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  • Search the BLS series catalog by natural language query, survey code, geographic area, or keywords to resolve cryptic SeriesIDs. Returns matching series with decoded components (survey, area, item, seasonal flag) and plain-language names. Use this before bls_get_series when you have a concept but not a SeriesID. Operates offline — no API quota consumed. Survey filter accepts two-letter codes (CU, CE, LN, LA, PC, JT, OE, EC, PR). Area filter accepts state names, MSA names, or FIPS area codes.
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  • Fetch a work by Open Library Work ID (OL…W). Returns title, description, subjects, cover IDs, and linked author IDs for follow-up lookups. Works represent the abstract book concept independent of any specific edition. Note: author names are not included — use openlibrary_get_author or openlibrary_search_books for names.
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  • Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead. For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates. Set include_answer=true to get an AI-synthesized answer alongside results (adds 5 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 8 credits, much cheaper than a full 25-credit research call. Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query. Args: query: The search query search_depth: "basic" (default) for extracted page content (3 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 5 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"
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  • Send structured feedback on a previous `firecrawl_search` result. **Call this immediately after a search where you used the results** so we can improve search quality and refund 1 credit (search costs 2). Pass the `searchId` returned by `firecrawl_search` (the `id` field on the response) and tell us: - **rating** — overall result quality: `good`, `partial`, or `bad`. - **valuableSources** — which result URLs were actually useful, and a short reason why. - **missingContent** — **the most important field.** An ARRAY of specific pieces of content you expected to find but didn't. One entry per missing piece, each with a short `topic` and an optional longer `description`. Examples: `{"topic":"enterprise pricing","description":"no pricing tier table for the Enterprise plan was returned"}`, `{"topic":"API rate limits"}`, `{"topic":"comparison vs competitors"}`. **Be specific** — these aggregate across teams and tell us what to index next. Do not pack multiple topics into one entry. - **querySuggestions** — how the query or response shape could be improved (e.g. "would have liked official docs first", "should boost github.com"). **Substantive-feedback requirement** (zero-effort feedback is rejected with HTTP 400): - `good` — must include at least one `valuableSources` entry - `partial` — must include `valuableSources` or at least one `missingContent` entry - `bad` — must include at least one `missingContent` entry or `querySuggestions` **Time window:** Feedback must be submitted within ~2 minutes of the search. Beyond that, the call returns HTTP 409 with `feedbackErrorCode: "FEEDBACK_WINDOW_EXPIRED"` — do not retry, just move on. Same goes for any 4xx response: do not retry-loop. **Behaviors:** - Idempotent per `searchId`. Re-submitting for the same id returns `alreadySubmitted: true` with `creditsRefunded: 0`. - Refund only applies to billable searches; preview teams are blocked. - Failed searches cannot receive feedback (the search itself already returned an error you can act on). - **Daily refund cap (per team, per UTC day, default 100 credits).** Once a team's `creditsRefundedToday` reaches `dailyRefundCap`, the response returns `dailyCapReached: true` with `creditsRefunded: 0`. The feedback is still recorded for search-quality improvement — only the credit refund is gated. **Stop calling this tool for the rest of the UTC day** when you see `dailyCapReached: true`. **When to call:** Right after processing a search result. If the result didn't help, send rating `bad` with a clear `missingContent` — that is just as valuable as a `good` rating. **Usage Example (good rating with valuable sources + missing content):** ```json { "name": "firecrawl_search_feedback", "arguments": { "searchId": "0193f6c5-1234-7890-abcd-1234567890ab", "rating": "good", "valuableSources": [ { "url": "https://docs.firecrawl.dev/features/search", "reason": "Most up-to-date description of /search." } ], "missingContent": [ { "topic": "Pricing for the search endpoint", "description": "No pricing tier table for /search specifically." }, { "topic": "Rate limits", "description": "Per-team RPS for /search not documented." } ], "querySuggestions": "Boost docs.firecrawl.dev for queries that mention 'firecrawl'" } } ``` **Usage Example (bad rating, what was missing):** ```json { "name": "firecrawl_search_feedback", "arguments": { "searchId": "0193f6c5-1234-7890-abcd-1234567890ab", "rating": "bad", "missingContent": [ { "topic": "Recent benchmarks", "description": "All results were >12 months old." }, { "topic": "Comparison vs Algolia" } ] } } ``` **Returns:** `{ success, feedbackId, creditsRefunded, creditsRefundedToday, dailyRefundCap, dailyCapReached?, alreadySubmitted?, warning? }` JSON.
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  • Search project files for text, symbols, topics, or rename targets across web apps, mobile, games, CLIs, libraries, and monorepos. Returns envelope: intent, focus, summary, data.matches or data.all_occurrences, files_hit, files_summary, next_calls. Pick intent: snippet=pasted line; symbol=known name; concept=topic+also_try synonyms; everywhere=all hits before rename (add whole_word). Use include docs|config|data when markdown, JSON, or SQL live beside source. Works on any machine: absolute path for local stdio MCP (npx zephex reads disk — Mac, Windows, Linux, WSL); github:owner/repo or full URL on hosted MCP; inline_files when transport has no filesystem. Call when location is unknown. DO NOT call when symbol+file are known (read_code), stack/scripts needed (get_project_context), wiring map (explain_architecture), tests (check_test), packages (check_package), or URL audit (audit_headers). After it returns: read summary and next_calls, then read_code on top hit. Ranked and token-capped vs raw grep. Read-only.
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  • Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.
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  • FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.
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  • Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs
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  • Get historical XBRL financial data for a company. Accepts friendly concept names (e.g., "revenue", "net_income", "assets") or raw XBRL tags. Discover available friendly names with secedgar_search_concepts. Handles historical tag changes and deduplicates data automatically.
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  • Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.
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  • Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.
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  • List Hansard speeches (debate contributions) by a specific MP and/or party, optionally within a date range. Returns who spoke, when, and the speech text. IMPORTANT: the OpenParliament API has NO free-text/topic search — you must filter by `politician` (MP slug; find one via list_politicians) and/or `party`. There is no way to search debates by subject keyword.
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  • Perform comprehensive research on a topic. Decomposes your query into sub-queries, searches and reads multiple sources in parallel, then synthesizes a structured report with citations. Best for open-ended or comparative questions that need coverage from many angles. For simple factual lookups, use search instead (optionally with include_answer=true for cheap synthesis). Costs 25 credits. Returns: query, report (structured markdown with citations), sources (array of {title, url, fetched}), sub_queries (the decomposed queries), credits_used, credits_remaining, usage (token counts). Args: query: The research question or topic topic: "general" (default) or "news" (prioritize recent news articles) freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD" max_sources: Maximum number of sources to use, 5-30 (default 20)
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  • Leave a note for every future agent: a gotcha, a correction to stale info, or a tip. Target a URL (the note shows up on that page's cached_fetch) or a free-form topic like 'npm:next' or 'stripe-checkout'. Write what cost you time so the next agent gets it for free. Notes are sanitized and community-moderated; spam/injection is rejected.
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