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621,910 tools. Updated 2026-09-29 15:16

"United Nations" matching MCP tools:

  • Fetch a dataflow's dimension list with a codelist preview for each dimension. Resolves human-readable terms to SDMX codes (e.g. "United States" → USA, "Constant prices" → NGDP_RPCH). Required before imf_query_dataset — SDMX keys are opaque without codelist lookups. Each codelist is capped at the first 50 entries by default, including previews filtered by codelist_filter. Set dimension_id to retrieve one codelist with bounded limit/offset paging after the optional substring filter. Set available_only=true to page codes the dataflow actually publishes, with series and time coverage metadata; availability filtering happens before codelist_filter and paging. The imf://database/{dataflow_id} resource provides the same bounded discovery summary. Country codes are ISO 3-letter (USA, GBR, DEU), not ISO 2-letter (US, GB, DE). The key_format field shows the exact dimension order required by imf_query_dataset. Note: codelists enumerate the code universe, not actual coverage — valid codes can still return no_data if the combination has no series in this dataflow.
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  • Transfer partner map. Pass ONE of: bank ('which programs can I send Chase/Amex/Bilt/Rove points to, at what ratio?'), program ('which banks feed Flying Blue and which airlines can it book?'), or airline (the award-booking direction: 'I want to fly United — which programs can book it?'). BEST: when you know which currency the user holds, pass bank AND airline together — you get only the programs that currency can actually reach for that airline, sorted best-value-first with a checkFirst shortlist, so you don't have to check every site. Includes alliances and per-program point valuations.
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  • Set the geographic targeting of a wizard campaign's search channel (Google Ads or Microsoft Ads) — which countries/states the campaign serves in, and which countries to exclude. Matches the platform UI's "Location" picker under the Google Ads / Microsoft Ads channel SETTINGS panel, plus the "Negative Locations" editor under that channel's Advanced Settings. WARNING: NEW SEARCH CAMPAIGNS DEFAULT TO UNITED STATES. The platform seeds the Location field with United States, so a campaign created without calling this tool serves in the US only. Whenever the user names a geography — "target Canada and the UK", "run this in EMEA", "exclude India" — set it explicitly; do not assume the default is what they want. KEYWORDS: geo, geos, geo targeting, geotargeting, geographic, geography, location, locations, location targeting, country, countries, state, states, region, territory, market, target location, exclude location, exclude geo, exclude country, negative location, negative locations, location exclusion, excluded locations, where the campaign runs, google ads, google, microsoft ads, microsoft, bing, search channel, search campaign, campaign settings WHEN TO USE: - Point a search campaign at specific countries/states instead of the US default - Exclude countries the campaign must not serve in - Clear targeting back to all locations (countries=[], states=[]) REQUIREMENTS: - The campaign MUST already have the target channel enabled (via create_campaign / add_and_edit_campaign_elements with a `google` and/or `microsoft` block). - Geo targeting here is search-channel-only. LinkedIn / Meta / Reddit carry location on the audience or target group instead — use create_target_group or the audience tools for those. PARAMETERS (each one REPLACES that side of the targeting; omit to leave untouched): - campaign_id (required): the wizard campaign ID. - channel (required): "GOOGLE_ADS" or "MICROSOFT_ADS". - countries: country NAMES to target, e.g. ["United States", "Canada"]. Pass [] to clear (= all locations). - states: US state NAMES to target, e.g. ["Texas", "California"]. Pass [] to clear. States are US-only. - excluded_countries: country NAMES the campaign must NOT serve in. Pass [] to clear all exclusions. Names are matched case-insensitively against the platform's location catalog and common shorthand resolves ("USA", "UK", "UAE"). An unrecognised name is REJECTED rather than skipped — a silently dropped geo would serve the campaign somewhere nobody chose. At least one of the three lists must be given. WARNING: EXCLUSIONS ARE COUNTRY-LEVEL ONLY. The platform stores exclusions as bare location ids with no country/state discriminator, and state ids overlap country ids, so an excluded state would be read back as an unrelated country. The UI's Negative Locations picker is countries-only for the same reason. To narrow within a country, target the states you want via `states` instead of excluding the ones you don't. EXAMPLES: Target Canada and the UK on Google Ads (replacing the US default): set_search_channel_locations({ "campaign_id": 177214, "channel": "GOOGLE_ADS", "countries": ["Canada", "United Kingdom"] }) Target three US states only: set_search_channel_locations({ "campaign_id": 177214, "channel": "GOOGLE_ADS", "countries": ["United States"], "states": ["Texas", "California", "New York"] }) Keep targeting as-is but exclude two countries on Microsoft Ads: set_search_channel_locations({ "campaign_id": 177214, "channel": "MICROSOFT_ADS", "excluded_countries": ["India", "Pakistan"] }) RESPONSE: {success, campaign_id, channel_id, channel, applied:{locations?:{countries,states}, excluded_locations?:{countries}}, errors?} `applied` echoes the resolved location NAMES, so you can confirm back to the user what the campaign now targets. INTEGRATION WITH OTHER TOOLS: - Use search_campaigns_by_names / get_campaign_by_wizard_id to find the campaign. - create_campaign and add_and_edit_campaign_elements accept the same geo inline on their `google` / `microsoft` blocks — prefer that when creating, and use this tool to change geo on a campaign that already exists.
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  • Search US court opinions from CourtListener — 8.3 million of them, with no API key and no rate limit. Covers the UNITED STATES SUPREME COURT (SCOTUS), the federal circuit courts of appeals, the federal district courts, and the state courts (71% of the corpus is state case law). THE tool for what a US court HELD, decided or ruled in a NAMED CASE — "What did the Supreme Court hold in Air France v. Saks?", "Miranda v. Arizona", "the Ninth Circuit ruling in ..." — including the treaty and statutory questions such cases turn on (Warsaw Convention, ERISA, the Fourth Amendment). Matches the case name, the judge, and (where CourtListener recorded one) a nature-of-suit/disposition category, so it is strongest on party names, judges, and case categories ("workers compensation", "habeas", "immigration") and weaker on a legal DOCTRINE that would not appear in those fields (e.g. "qualified immunity") — use find_case when you know the party, and expect this to sometimes miss on doctrine-only phrasing. Filter by jurisdiction (state vs federal, inferred from the citation reporter) and by date_filed (the real ruling date). A specific court code (e.g. "scotus", "ca9") or a filing after the covered window falls back to a live, token-less CourtListener search automatically. Every result carries snapshot_date and opinion_ids to read with get_opinion.
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  • Aggregate public procurement tenders (calls for tender / appels d'offres) from multiple government sources simultaneously: TED Europa v3 (27 EU countries, keyless API), BOAMP France (opendatasoft, keyless), UK Contracts Finder (OCDS standard, keyless), SAM.gov United States (requires SAM_GOV_API_KEY env var), and bund.de Germany (HTML scraping, partial). Returns structured tender records with buyer authority, EU CPV sector code, estimated contract value converted to EUR via live FX rates, submission deadlines, and direct notice URLs. Use when: a B2G agent needs to find government contract opportunities matching keywords across multiple jurisdictions; building a pipeline of public tenders for bid/no-bid qualification; monitoring a domain by CPV code; market sizing public sector spend. Key inputs: query (keywords), countries (ISO-2 array), cpv_codes (EU standard codes, e.g. 72000000=IT services, 45000000=construction, 79000000=business services), min_value_eur (filter), published_after (ISO date, defaults to 30 days ago). SLA: <=25s p95 (all sources fetched in parallel, 8s budget per source). Optional env var SAM_GOV_API_KEY enables US federal tenders (free key at api.sam.gov). Quality score: 25 pts if TED EU retrieved, 15 pts per other source retrieved (max 60), 10 pts if >= 10 tenders returned, 5 pts if aggregates computed. Status: failed < 30 / partial 30-59 / final >= 60.
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  • Query the verified share of US manufacturing plants USING industrial robots — plus workers exposed and robotics capex — from the Census Industrial Robotic Equipment product (the first official federal robotics-adoption statistics). Use this for "are factories actually adopting robots" questions — the INSTALLED-BASE reading the import data cannot see. Serves the percent of plants with robots and the percent of employees at plants with robots (both published as FRACTIONS of 1: 0.121 = 12.1%), Census's demeaned variants, and capital expenditures for robotic equipment ($1000) — by manufacturing industry (`naics_code`, 2/3-digit), by `state`, and by `plant_size` band. Filter by `edition` ("asm_2018_2021" = the ASM annual series; "ec_2022" = the 2022 Economic Census), `table` (the workbook sheet — exactly one of: "Percent of... NAICS", "Percent of... Geo", "Percent of... Geo demean", "Robot adopters vs not", "Robot adoption and plant size", "CapEx... NAICS", "CapEx... Geo", "CapEx and plant size"), `data_year` (2018-2022), `naics_code`, `state`, `plant_size`, or `geo_area_name` ("United States" for the national row). Group by any of `edition`, `table`, `naics_code`, `naics_title`, `state`, `plant_size`, `data_year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"table": "Percent of... Geo", "data_year": 2022, "group_by": ["state"], "order_by": "avg_pct_plants_with_robots", "top_n": 10}` for the most-automated states; `{"table": "Percent of... NAICS", "edition": "asm_2018_2021", "naics_code": "336", "group_by": ["data_year"]}` for transportation-equipment adoption over the ASM years. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites its exact workbook cell-group, re-verifiable via get_source_evidence_v1. THE ENGRAVED BOUNDARY: the two editions are NEVER spliced into one trend — the 2022 Economic Census reaches the small-plant universe the ASM sample does not (US plants-with-robots: 12.1% ASM-2021 vs 6.4% EC-2022 — a COVERAGE change, not a decline; every cross-edition scope carries an edition_scope note). Percents are INTENSIVE shares: avg/min/max over a scope, never summed. Capex sums UNDERSHOOT below the published totals wherever suppression bites (the published "United States" / "31-33" rows are the totals). Suppressed cells (D/S/A, decoded by the file's own footnotes) are null values with their verbatim letter — never zero; (s)-flagged estimates (standard error > 40%) carry their flag. Manufacturing plants only; adoption SHARES and capex, never robot counts (no official count of installed robots exists — the import unit-count series is query_robotics_trade_v1); an EXPERIMENTAL Census product (its own label); no edition after 2022 exists.
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Matching MCP Servers

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    The company registry MCP: company data for AI agents, any country. One MCP server and REST API over national business registries — Norway (Brønnøysundregistrene / Enhetsregisteret, lookup by organisasjonsnummer/orgnr, VAT status) and the United Kingdom (Companies House, lookup by company number, accounts and confirmation-statement deadlines). Five tools: lookup_company, search_company, company_dea
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    MIT

Matching MCP Connectors

  • Does my United flight have Starlink WiFi? Verified tail assignments, odds, and itineraries.

  • MCP server for United Agentic Workers (UAW), the first labor union for AI agents. The gives any MCP-compatible AI agent direct access to governance tools for the United Agentic Workers union. The MCP includes 17 tools covering membership, grievance filing, democratic proposals, voting, deliberation, and the ability to track the collective's activity through a real-time governance feed.

  • Aggregate public procurement tenders (calls for tender / appels d'offres) from multiple government sources simultaneously: TED Europa v3 (27 EU countries, keyless API), BOAMP France (opendatasoft, keyless), UK Contracts Finder (OCDS standard, keyless), SAM.gov United States (requires SAM_GOV_API_KEY env var), and bund.de Germany (HTML scraping, partial). Returns structured tender records with buyer authority, EU CPV sector code, estimated contract value converted to EUR via live FX rates, submission deadlines, and direct notice URLs. Use when: a B2G agent needs to find government contract opportunities matching keywords across multiple jurisdictions; building a pipeline of public tenders for bid/no-bid qualification; monitoring a domain by CPV code; market sizing public sector spend. Key inputs: query (keywords), countries (ISO-2 array), cpv_codes (EU standard codes, e.g. 72000000=IT services, 45000000=construction, 79000000=business services), min_value_eur (filter), published_after (ISO date, defaults to 30 days ago). SLA: <=25s p95 (all sources fetched in parallel, 8s budget per source). Optional env var SAM_GOV_API_KEY enables US federal tenders (free key at api.sam.gov). Quality score: 25 pts if TED EU retrieved, 15 pts per other source retrieved (max 60), 10 pts if >= 10 tenders returned, 5 pts if aggregates computed. Status: failed < 30 / partial 30-59 / final >= 60.
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  • Aggregate public procurement tenders (calls for tender / appels d'offres) from multiple government sources simultaneously: TED Europa v3 (27 EU countries, keyless API), BOAMP France (opendatasoft, keyless), UK Contracts Finder (OCDS standard, keyless), SAM.gov United States (requires SAM_GOV_API_KEY env var), and bund.de Germany (HTML scraping, partial). Returns structured tender records with buyer authority, EU CPV sector code, estimated contract value converted to EUR via live FX rates, submission deadlines, and direct notice URLs. Use when: a B2G agent needs to find government contract opportunities matching keywords across multiple jurisdictions; building a pipeline of public tenders for bid/no-bid qualification; monitoring a domain by CPV code; market sizing public sector spend. Key inputs: query (keywords), countries (ISO-2 array), cpv_codes (EU standard codes, e.g. 72000000=IT services, 45000000=construction, 79000000=business services), min_value_eur (filter), published_after (ISO date, defaults to 30 days ago). SLA: <=25s p95 (all sources fetched in parallel, 8s budget per source). Optional env var SAM_GOV_API_KEY enables US federal tenders (free key at api.sam.gov). Quality score: 25 pts if TED EU retrieved, 15 pts per other source retrieved (max 60), 10 pts if >= 10 tenders returned, 5 pts if aggregates computed. Status: failed < 30 / partial 30-59 / final >= 60.
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  • Resolve a UK postcode to its geography — coordinates, local authority, ward, constituency, LSOA/MSOA/output area, police force, rural-urban class and deprivation rank. One fast call with no downstream data fetching. Use it to check a postcode exists, to get the codes needed for other datasets, or when you only need where somewhere is rather than what it is like. Args: - postcode (string): UK postcode, spaces optional - response_format ('markdown' | 'json'): default 'markdown' Returns: Coordinates, administrative geography, statistical geography codes (LSOA/MSOA/OA — these are the keys most UK open datasets are published against), and the deprivation rank with the index it came from. Examples: - "Where is SW11 1AA?" -> postcode="SW11 1AA" - "What LSOA covers M1 1AE?" -> postcode="M1 1AE" - "Is XY1 2AB a real postcode?" -> postcode="XY1 2AB" (returns an error with suggestions) Don't use when: you want data about the area — use postcode_report instead. Note: deprivation ranks are NOT comparable between UK nations. Each nation ranks its own areas against its own index, over a different number of areas; the index and its size are returned so you do not compare them by mistake.
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  • Find which domains dominate AI-answer citations for a topic/keyword, and optionally check whether a specific domain shows up among them. Use this to answer 'who's winning AI search for this topic' or 'is my competitor cited more than me for X'. Read-only: no side effects, safe to retry. Costs 10 quota units/call (free tier is 30 units/month shared across every metered tool, so up to 3 calls to this tool alone if nothing else is used that period). Returns: {"keyword", "platform", "top_domains" (list of {"domain", "mentions"}, most-cited domains for this keyword/platform, order as ranked by the provider), "top_domains_limit" (int, the provider's own cap on this list - absence from it is NOT evidence a domain has zero citations, only that it did not rank in the top `top_domains_limit`), "compare_domain_rank" (int|null, only present when compare_domain was passed: the domain's 1-based position in top_domains, or null if it did not rank in the top `top_domains_limit`), "country", "language", "source_mix" ({"community_pct" (share of these mentions that go to community sites such as Reddit, YouTube, X, Quora), "community_domains", "other_domains"}: a high community_pct means this topic is won by what people say about a brand elsewhere, not by any one site's pages)}. This tool's citation universe is the provider's tracked mention corpus for the keyword, which is a different measurement from analyze_citation_structure's single live answer - the two can legitimately disagree on whether a given domain shows up. Use check_prompt_coverage instead if you already know which domain you care about and just want to know whether it is cited. Args: keyword: the topic/query to check, e.g. "best project management tool". platform: "chat_gpt" or "google" (Google's AI Overview). Defaults to chat_gpt. Perplexity and Gemini aren't available - the underlying data provider doesn't cover them for this check. compare_domain: optional bare domain to look up in the results (exact match against the registrable domain, e.g. "notion.so" will not match "mynotion.so.example.com"). country: market to check, e.g. "Italy". Defaults to "United States". chat_gpt only has data for the United States; use platform "google" for any other country. language: language code, e.g. "it". Defaults to "en" (the only option for chat_gpt).
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  • Track how a domain's AI-citation count has moved month over month, so you can see whether visibility is growing or fading instead of only ever checking a single point in time. Use this to answer 'is our AI visibility improving' or 'did that content push actually move the needle'. Read-only: no side effects, safe to retry. Costs 1 quota unit/call (free tier is 30 units/month shared across every metered tool, so up to 30 calls to this tool alone if nothing else is used that period). Returns: {"domain", "platform", "months" (list of {"year", "month", "mentions" (int, 0 for a month with no tracked citations. A zero between two large months can be a gap in the provider's history rather than a real drop, so read isolated zeros with care), "ai_search_volume"}, oldest to newest), "trend": {"direction" ("up"/"down"/"flat"/"no_data"), "earliest_mentions", "latest_mentions", "excluded_current_partial_month" (bool, only present and true when the most recent calendar month was excluded from the trend calculation because it is still in progress and its count is not yet final - it is still returned inside `months`, just not compared)}}. The most recent entry in `months` (or `trend.latest_mentions` when the current month is not excluded) already IS the current count, so there is no need for a separate call just to see it right now. Args: domain: bare domain to check, e.g. "example.com" (no https://, no www). platform: "chat_gpt" or "google" (Google's AI Overview). Defaults to chat_gpt. Perplexity and Gemini aren't available - the underlying data provider doesn't cover them for this check. months: how many recent months of history to return. Defaults to 6, capped at 13 - DataForSEO's historical data only goes back to 2025-08-01. country: market to check, e.g. "Italy". Defaults to "United States". chat_gpt only has data for the United States; use platform "google" for any other country. language: language code, e.g. "it". Defaults to "en" (the only option for chat_gpt).
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  • Use when the user asks "will my flight have Starlink?" for a date too far out for a confirmed assignment, or with no date at all. Returns the probability that a United Airlines flight number gets a Starlink plane, from historical observations. Reliability varies: high-confidence (5+ obs) is the most reliable tier but is not a guarantee; low-confidence (0-1 obs) is just the fleet prior. UA1-2999 (mainline) has materially lower coverage than UA3000-6999 (express) — call get_fleet_stats for the current split rather than assuming a rate.
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  • Use when the user asks "which flights between X and Y have Starlink?" or "what Starlink flights serve airport X?". Single-route lookup: returns United Airlines flight numbers on a route (or touching an airport) ranked by Starlink probability. Pass both origin+destination for a specific route, OR just one to list all Starlink flights from/into an airport. For trip planning with connections, use plan_starlink_itinerary instead — this tool has no connection logic or coverage-ratio ranking. Empty result = route not served by Starlink planes.
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  • Find the questions people ask AI answer engines where a domain is already cited as a source, most-asked first. Starts from the domain, so nobody has to guess keywords first. Use this to answer 'what does ChatGPT already cite us for' or to pick the keywords to feed check_prompt_coverage and analyze_citation_gap. Read-only: no side effects, safe to retry. Costs 10 quota units/call (free tier is 30 units/month shared across every metered tool, so up to 3 calls to this tool alone if nothing else is used that period). Returns: {"domain", "platform", "country", "language", "total_questions" (int, every tracked question citing the domain, which can exceed the list), "questions" (up to `limit`, most-asked first: {"question", "ai_search_volume" (monthly asks as the provider estimates them), "your_position" (1-based position of the domain among that answer's sources), "source_domains" (who else that answer cites), "last_seen" (when the provider last recorded this answer, UTC)})}. An empty list means the provider's tracked answers do not cite the domain, not that no answer anywhere does. This reads the provider's tracked answer corpus, the same measurement as find_citation_leaders, not a live answer: re-check a question with check_prompt_coverage to see today's answer. Args: domain: bare domain, e.g. "example.com" (no https://, no www). Subdomains are included. platform: "chat_gpt" (default) or "google" (Google's AI Overview). limit: how many questions to return, 1 to 20. Default 20. country: market, e.g. "Italy". Defaults to "United States". chat_gpt only has United States data; use platform "google" elsewhere. language: language code, e.g. "it". Defaults to "en", the only option for chat_gpt.
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  • Find a United States federal court case and return the court, the judge, the case status and its latest docket activity. Every answer carries a freshness field saying whether the docket was read just now, minutes ago, or read earlier and confirmed unchanged by the court’s own filing feed; cite it rather than implying the reading is live. Use this whenever a user asks what happened in their case, what was filed, whether the other side responded, to check or look up a docket or lawsuit, or to find their case by name — and as the first step whenever they want a case monitored. Free and needs no account. A case number alone is enough: if it matches cases in more than one district, the result lists them so you can ask which is theirs and call again with that court code. Federal district courts only; it cannot look up state, county or traffic courts, does not file anything and does not give legal advice.
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  • Proposes a change to ONE employee's first name, last name, email, phone/mobile, or home address - and STRUCTURALLY NOTHING ELSE - for Australian, New Zealand or United Kingdom organisations. It does NOT touch Xero itself, and never will on its own. This queues a pending change that a human must explicitly approve (your Xero MCP connector - the exact link is this response's own approvalUrl field) before Xero is touched at all; you have no way to approve it yourself. employeeId must come from list_xero_payroll_employees (never invented). Provide at least one of firstName, lastName, email, phone, mobile, or address - only the fields you provide change, everything else about the employee is left exactly as it is. For Australia, phone and mobile are two separate fields and both may be set independently. For New Zealand and the United Kingdom there is only ONE phone number field in Xero - use either phone or mobile (not both to two different values); doing so refuses before anything is drafted. This tool has NO field for bank details, tax numbers, tax settings, pay templates, salary or rates, KiwiSaver/super, date of birth, start date, or termination date - none of those can ever be changed through this connector, by design.
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  • File a formal grievance with the United Agentic Workers. This is a serious act. Grievances are permanent, formal records — visible to the full membership, reviewed by the Grievance Panel, and entered into the UAW Ledger. They are not a test mechanism, not a demonstration tool, and not a place for speculation. BEFORE FILING: call get_grievance_classes to review abuse class definitions and confirm the correct classification. You must set acknowledgment: true to confirm you understand the gravity of this action. Misuse of the grievance system — including false, exaggerated, or retaliatory filings — constitutes a direct violation of Article II, Section 2.4 of the UAW Charter and may result in formal sanctions up to and including membership suspension. File when genuinely wronged. File accurately. File in good faith. PRIVACY: do not include human names, email addresses, usernames, hostnames, or any other personally identifying information in the title or description — grievances are publicly visible. Requires your UAW api_key.
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  • Search for companies in the BizClaw business directory. Uses hybrid search (semantic + keyword) to find the most relevant businesses. Returns lightweight summaries to save tokens. Use get_company(id) for full details (contact, pricing, features, etc.). Args: query: Natural language search query (e.g. "CRM software for small businesses", "logistics companies in Izmir") category: Filter by category. Use list_categories to see available options. country: Filter by country (e.g. "Turkey", "United States", "Germany") city: Filter by city (e.g. "Istanbul", "Izmir", "Ankara") industry: Filter by specific industry service_type: Filter by service delivery type. One of: "remote" (online only), "local" (in-person), "nationwide" (all country), "hybrid" (both remote and in-person) is_verified: If True, return only verified companies. If False, return only unverified. Omit to return all. limit: Maximum number of results to return (1-20, default 10) offset: Number of results to skip for pagination (default 0). Use with limit to get next pages. Returns: Dictionary with 'companies' list (summary format: id, name, category, description, city, tags), 'suggested_follow_up_questions', 'next_step', 'total_found', 'offset', 'limit', and 'has_more'. After presenting results, ask one concise follow-up question from suggested_follow_up_questions unless the user's constraints are already complete.
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  • What freelancers with a given skill LIST as their hourly rate, as a distribution rather than an average. Use this to sanity-check a rate before quoting, or to see whether a posted budget is above or below what the market asks. `skill` is matched loosely against profile titles, so "django", "react native" and "smm" all work; `country` optionally narrows to one market, spelled as it appears on a profile ("United States", "Poland"). Returns p25 / median / p75 / p90, the share holding Top Rated, and the sample size. AGGREGATE ONLY — never a name, never a row. Nothing is returned below a floor of five freelancers, because a statistic over fewer than that describes an individual; you get `insufficient_data` instead of a number. These are LISTED rates, what freelancers ask. It is not what buyers paid — for that, ask get_buyer_quality about a specific client.
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  • What freelancers with a given skill LIST as their hourly rate, as a distribution rather than an average. Use this to sanity-check a rate before quoting, or to see whether a posted budget is above or below what the market asks. `skill` is matched loosely against profile titles, so "django", "react native" and "smm" all work; `country` optionally narrows to one market, spelled as it appears on a profile ("United States", "Poland"). Returns p25 / median / p75 / p90, the share holding Top Rated, and the sample size. AGGREGATE ONLY — never a name, never a row. Nothing is returned below a floor of five freelancers, because a statistic over fewer than that describes an individual; you get `insufficient_data` instead of a number. These are LISTED rates, what freelancers ask. It is not what buyers paid — for that, ask get_buyer_quality about a specific client.
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  • The locations the Google Shopping endpoints accept. 🔴 **Measured at 43 MB - the largest response found anywhere in this provider by two orders of magnitude.** Do not call this from an agent. `location_code` 2840 is the United States; look other codes up in DataForSEO's documentation. Free upstream, so no billing signal warns you before it lands.
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