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501,010 tools. Updated 2026-08-31 20:20

"A search for the exact phrase "pHqghUme'"" matching MCP tools:

  • USE THIS TOOL WHEN searching UK Acts and Statutory Instruments by title, phrase, or full-text. Returns ranked results: title, type, year, number, legislation.gov.uk URL, and next_steps hints (toc URI, section template). AFTER calling, chain to legislation_get_toc then legislation_get_section for structural drill-in. Filter discipline: `type` and `year` are exact-match. Use only when you already know the value. For currency-driven searches ("the recent Renters' Rights Act"), query by phrase alone and read the year from the results — guessing a year and filtering by it zeroes results when wrong. For broader concept queries across content, set `fulltext=True`. Authoritative source for UK primary and secondary legislation (legislation.gov.uk).
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  • USE THIS TOOL WHEN you have a member_id and want contributions where THAT member used a specific topic phrase verbatim (text-body search). CALL parliament_find_member(name) FIRST to obtain the integer member_id. This is a name-based text-body search — it matches contributions whose TEXT contains the topic phrase. A member who spoke in a debate but didn't use your phrase verbatim is filtered out. For verbatim retrieval of every contribution by a member in a known debate (regardless of vocabulary), use parliament_get_debate_contributions(debate_ext_id, member_id=...) instead. Each contribution's text field is capped at 3000 characters.
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  • USE THIS TOOL WHEN searching UK Acts and Statutory Instruments by title, phrase, or full-text. Returns ranked results: title, type, year, number, legislation.gov.uk URL, and next_steps hints (toc URI, section template). AFTER calling, chain to legislation_get_toc then legislation_get_section for structural drill-in. Filter discipline: `type` and `year` are exact-match. Use only when you already know the value. For currency-driven searches ("the recent Renters' Rights Act"), query by phrase alone and read the year from the results — guessing a year and filtering by it zeroes results when wrong. For broader concept queries across content, set `fulltext=True`. Authoritative source for UK primary and secondary legislation (legislation.gov.uk).
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  • USE THIS TOOL WHEN you have a member_id and want contributions where THAT member used a specific topic phrase verbatim (text-body search). CALL parliament_find_member(name) FIRST to obtain the integer member_id. This is a name-based text-body search — it matches contributions whose TEXT contains the topic phrase. A member who spoke in a debate but didn't use your phrase verbatim is filtered out. For verbatim retrieval of every contribution by a member in a known debate (regardless of vocabulary), use parliament_get_debate_contributions(debate_ext_id, member_id=...) instead. Each contribution's text field is capped at 3000 characters.
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  • GET /search — Cross-resource omni-search Cross-resource search across profiles, rooms, messages (incl. private DMs + group DMs you're in), events, and chapters in one round trip. Returns the top-N matches per resource, grouped by resource. Use this when you don't yet know which resource carries the answer — agents typically call this first, then drill into a specific `GET /search/<resource>` for more depth on a single bucket. There's no page param: when you hit the per-resource limit and want more, switch to the per-resource endpoint for that one. The events slice has a baked-in forward-looking default (events ending in the last 30 days or later, and currently enabled) — this matches the in-app "Search across DC" surface. Use `GET /search/events` directly to look further back in time. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.
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  • Search JobYap job postings by natural-language query. Matches job titles, falling back to significant keywords when the full phrase finds little. Returns result ids, titles and citable URLs for use with fetch. For structured filtering (location, company, remote, freshness) prefer search_jobs.
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  • Phrase MCP server: language intelligence platform for translation, terminology, and quality.

  • Exact AI Connect: hosted MCP server for Exact Online.

  • SENDS REAL MESSAGES TO REAL PEOPLE. This is irreversible and costs credits. It requires an approval_token from review_campaign_readiness AND the confirmation phrase that review returned, which the HUMAN OPERATOR must give you explicitly. Never generate the confirmation yourself, never infer approval from a previous message, and never reuse an approval issued for a different campaign or a different size. If the operator has not clearly said yes to this exact launch, do not call this tool — ask them.
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  • Search Trend News Agency's archive and read the relevant part of each result in one call. Returns several articles with the passage that actually addresses the query — not just the opening paragraph — plus byline, date, section and the canonical URL to cite. This is the tool to reach for on any question about Azerbaijan, the South Caucasus, the Caspian, Central Asia, Turkey or Iran that needs what was actually reported: energy and pipelines, regional politics, trade corridors, economics. Prefer it over calling search and then reading articles one by one. Wrap words in double quotes for an exact phrase. Subscriber-only articles from the past year contribute their summary rather than their text, and say so.
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  • Search open grant opportunities from Kindora's active foundation-program corpus plus federal and state government grants. FOR-PROFIT APPLICANTS: pass for_profit_applicant=true to search capital a for-profit can take (PRIs, loans, revenue-based financing, patient equity) from CDFIs, impact investors, and PRI-active foundations. The default pool is 501(c)(3)-shaped and will NOT contain those programs. Searches both private foundation grant programs (from IRS data and funder websites) and government grant opportunities — federal (Grants.gov) plus state and district grant portals. Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)
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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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  • Search, filter, sort, or retrieve by ID. Covers all OpenAlex entity types (works, authors, sources, institutions, topics, keywords, publishers, funders). Pass `id` to retrieve a single entity. Otherwise, use `query` and/or `filters` for discovery. Supports keyword search with boolean operators, exact phrase matching, and AI semantic search. Use openalex_resolve_name to resolve names to IDs before filtering. Searches and ID lookups return a curated set of fields by default; pass `select` to override with specific fields, or `["*"]` for the full record.
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  • Searches the 29,500+ World Bank indicator catalog by keyword, topic, or source. Returns indicator IDs and metadata for chaining into worldbank_get_data. At least one of query, topic_id, or source_id must be provided. A keyword query matches every term against indicator ID, name, and description, in any word order, across the whole catalog or the whole selected topic or source; punctuation in the query is ignored. Exact ID or name matches rank first, then whole-phrase matches, then ID/name matches, then description-only matches. Each indicator ID appears once, even where the catalog publishes it under two sources. Use worldbank_list_topics for topic IDs, worldbank_list_sources for source IDs.
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  • Read a note. Canonical calls, copying fields verbatim from a search result: search(query) -> note_html(path=<result.note_path>) reads the whole note; search(query) -> note_html(match_id=<match.match_id>) reads just the focused chunk around a hit (cheaper, targeted); expand(path=<result.note_path>, toc_path=[...]) -> note_html(path=<result.note_path>, toc_path=[...]) reads one exact section. Only pass pid/note_id if you already copied that exact integer from a result's note_id field — never invent one. path is a string like "concepts/x.md"; match_id is "p<pid>:c<chunk>"; a value like ":" or "/hub/goethe.md" is a PATH, not a note_id.
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  • Search real-time domain availability and prices (via Name.com). Pass a keyword for suggestions, or include ONE full domain name anywhere in the query ("orivox.ai", "www.orivox.ai", even inside a short phrase) to also get that exact domain availability-checked and returned as the FIRST result marked exact:true. Response markers: exact_domain = the exact answer is in the list; exact_check_unavailable = the exact check failed, the list is keyword suggestions only (relay the included note); NEITHER marker = no single domain was recognized (bare keyword, or two+ domains in one query) and NO exact check ran -- if the user asked about specific domains, use check_domain per domain. Returns suggestions as {domain, available, price, premium, renewal_price} sorted purchasable-first (exact match first when present), plus registrar_env ("production" or "sandbox"). ALWAYS mention two pricing traps when recommending: premium=true is an aftermarket domain (first-year price can be thousands, renewal differs), and renewal_price much higher than price is a first-year-discount cliff (e.g. $3.99 year one, $48.99/year after) -- quote both numbers. If the response carries sandbox=true, relay the included caveat to the user in their language and do not present results as real availability. This is a READ: searching never reserves, registers, or charges anything. STOP after presenting the results. Do NOT call get_domain_purchase_link in the same turn: that tool is the PAYMENT step, and the user is still choosing. Report the names, prices and renewal cliffs, then wait. Only once the user picks ONE domain and says they want to buy it does the purchase link belong in the conversation -- a payment link handed to someone who has not decided is clutter at best and pressure at worst. Searching more, or checking another name with check_domain, needs no link at all.
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  • Search US SEC 8-K and 6-K current-report nodes for company events and disclosures. Use this to discover issuers across a date range. Do not use this for 10-K or 10-Q filings. How to search: 1. Always pass query with likely verbatim disclosure phrases. Each item is an exact adjacent-token phrase. Put alternate full phrasings in the same list. Do not put broad topic words such as "China", "AI", "customer", or "restructuring" alone in query. 2. For open-ended discovery, also pass concept_groups. Every group is required (AND). Within each group's any_of list, one alternative must match (OR). All groups match inside one filing node. Use separate groups for the main context, action or direction, business object or metric, and a causal or limiting relation when that relation is essential. 3. Add real synonyms and alternate filing language to any_of. The concept path uses English stemming, so one base form usually covers inflections (decline/declined/declining and volume/volumes). Stemming does not add synonyms (sales does not mean revenue; reduce does not mean weaken). 4. Exact phrase matches receive a score boost. Concept groups are a fallback for different wording. If concept_groups is omitted, the search is exact-phrase only. 5. Use date filters for time and tickers to search only selected issuers. Pass ne_tickers (or prefix a symbol with !) to omit issuers. 6. Results are candidates, not final conclusions. Call read_node_content with each promising document_id and node_id(s). Verify negation, causal claims, comparisons across periods, and numeric thresholds such as a percentage or dollar amount in the source text. Cite CITATION_MARKDOWN. When you finish an issuer, search again with the same inputs and add its ticker to ne_tickers so later hits come from other issuers. Examples of useful group dimensions include geography + weakening signal + demand metric; CapEx + reduction + guidance; AI/automation + enablement + workforce + reduction; customer + loss/concentration; data centers + exposure + monetization; or restructuring + program/charge. Do not add a group for a detail that the filing may leave implicit, because every group is mandatory. Each result is one filing node: document_id, node_id, parent_node_id, ticker, type, filing_date, match_mode, query, score, and a short snippet.
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  • Full-text search across all SEC EDGAR filings since 2001 for a keyword or phrase. Wraps EDGAR's own full-text search index, so it covers every filer and form type, not just a single company. Useful for finding who is disclosing a particular risk, technology, litigation, or event across the entire market. When to use: cross-company research ("who is disclosing AI-related risk factors"), finding filings that mention a specific term, litigation or regulatory tracking. When NOT to use: you already know the company (use edgar_filings_feed, which is company-scoped and cheaper), or you need results from before 2001 (EDGAR full-text search does not cover that far back). Args: - query (string, required): search text. Wrap an exact phrase in double quotes, e.g. "\"material weakness\"". - forms (string[], optional): restrict to form types, e.g. ["10-K"]. - dateFrom (string, optional): ISO start date (YYYY-MM-DD). - dateTo (string, optional): ISO end date (YYYY-MM-DD). - limit (integer, optional, default 10): maximum hits to return (1-50). Returns structuredContent: { "query": "material weakness", "totalMatches": 10000, "totalIsApproximate": true, "count": 2, "hits": [ { "id": "0001193125-26-123456:doc.htm", "entity": "Example Corp.", "form": "10-K", "filedAt": "2026-03-01", "cik": "0000320193" } ], "source": "https://www.sec.gov/edgar" } "totalMatches" is a lower bound and "totalIsApproximate" is true once EDGAR's own count exceeds its display cap (10,000) — narrow with forms/dateFrom/dateTo for a precise count.
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  • Search Trend News Agency's archive by keyword and get matching headlines ranked by relevance, with date, section, byline and canonical URL. The archive runs back to 2003 and holds over 2.6 million articles across the English, Azerbaijani and Russian editions. Wrap words in double quotes for an exact phrase — "Trans Anatolian Pipeline" is far narrower than the same three words loose. Returns metadata only; use trendaz_research when you want the text of what was reported, or trendaz_article_get for one article.
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  • Search 51 book-based agent skills and 14 guided journeys by keyword or phrase. Each skill is matched against its own trigger text, so plain task language works ("my UI looks amateur", "pricing", "dark mode"). Returns compact hits, each quoting the line that matched. Use recommend_skills instead when you have a task rather than a keyword.
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  • Search the full text of books scanned by the Internet Archive — the "which book contains this passage?" lookup that the metadata tools cannot answer. Quote a phrase for an exact-phrase match; bare terms match anywhere in the text. Each result is an Internet Archive item with the matching passages as snippets, plus a relevance score. The full-text index is an order of magnitude slower than the metadata endpoints (seconds, not milliseconds), so reach for it when the passage is the question, not as a general book search — use openlibrary_search_books for title, author, or subject. Results key on Internet Archive items rather than Open Library works: chain the returned ia_identifier to archive.org, or match it against the ia_identifiers on openlibrary_search_books results to reach the catalogue record.
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  • Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.
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