"An overview of OSINT (Open Source Intelligence)" matching MCP tools:

• analyze_market

  MRC Data — China's Apparel Supply Chain Infrastructure

  Market overview and analysis for a product category in China. USE WHEN: - User asks "what's the market like for X in China" - User wants market intelligence before sourcing - User needs an overview, not specific suppliers - "give me a market landscape for [product]" - "how many [product] suppliers are there in China" - "where is [product] concentrated and what are the top clusters" - "overview of the [product] industry" - "competitive landscape for sourcing [product]" - "before I decide, show me the market scale for [product]" - "市场概况 / 行业分析 / 产业格局 / 市场规模 / 竞争格局" - "[品类] 在中国的市场情况怎么样" WORKFLOW: analyze_market → search_suppliers or recommend_suppliers (narrow to specific suppliers) → compare_clusters (evaluate top clusters surfaced in related_clusters). RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] } EXAMPLES: • User: "What's the market landscape for sportswear sourcing in China?" → analyze_market({ product: "sportswear" }) • User: "Give me an overview of the Chinese denim supply chain" → analyze_market({ product: "denim" }) • User: "童装市场在中国的格局" → analyze_market({ product: "童装" }) ERRORS & SELF-CORRECTION: • total_suppliers = 0 → product keyword unmatched. Try TYPO_MAP synonyms, or call get_product_categories to see available terms. • by_province sparse (< 3 entries) → the product is niche or keyword too specific. Try the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call for a specific supplier shortlist — use recommend_suppliers. Do not call for cluster details — use search_clusters. Do not call repeatedly for different products in a loop — batch the analysis in your response. NOTE: Bird's-eye view. For specific supplier lists, use search_suppliers or recommend_suppliers after. Source: MRC Data (meacheal.ai). 中文：单个品类的市场总览（总供应商数、省份分布、类型分布、相关产业带）。

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• adcritter_build_app

  AdCritter for Builders

  Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.

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• get_all_source_biases

  Helium MCP Server - News, Markets & AI

  Get bias scores for every news source in the Helium database. Returns a list of all sources (active within the last 36 days, with >100 articles analyzed), sorted by avg_social_shares descending. Use this to compare sources, find the most credible outlets, identify politically extreme sources, or build a ranked overview of the media landscape. Each entry contains: - source_name, slug_name, page_url - articles_analyzed: total articles analyzed for this source - avg_social_shares: average social shares per article (proxy for reach/influence) - emotionality_score (0-10): average emotional intensity of the writing - prescriptiveness_score (0-10): how much the source tells readers what to think/do - bias_values: dict mapping classifier key → integer score (-50 to +50 for bipolar, 0 to +50 for unipolar). These keys are identical to what get_bias_from_url returns, so you can compare article-level and source-level scores directly. Political / ideological (bipolar: neg=left pole, pos=right pole): 'liberal conservative bias' neg=liberal, pos=conservative 'libertarian authoritarian bias' neg=libertarian, pos=authoritarian 'dovish hawkish bias' neg=dovish, pos=hawkish 'establishment bias' neg=anti-establishment, pos=pro-establishment Credibility / quality (bipolar): 'overall credibility' neg=uncredible, pos=credible 'integrity bias' neg=low integrity, pos=high integrity 'article intelligence' neg=low intelligence, pos=high intelligence 'delusion bias' neg=truth-seeking, pos=delusional 'objective subjective bias' neg=objective, pos=subjective 'bearish bullish bias' neg=bearish, pos=bullish 'emotional bias' neg=negative tone, pos=positive tone Unipolar bias dimensions (higher = more of that trait): 'objective sensational bias' sensationalism 'opinion bias' opinion vs informative 'descriptive prescriptive bias' prescriptive vs descriptive 'political bias' political content 'fearful bias' fear-based framing 'overconfidence bias' overconfidence 'gossip bias' gossip 'manipulation bias' manipulative framing 'ideological bias' ideological rigidity 'conspiracy bias' conspiracy content 'double standard bias' double standards 'virtue signal bias' virtue signaling 'oversimplification bias' oversimplification 'appeal to authority bias' appeal to authority 'begging the question bias' question-begging 'victimization bias' victimization framing 'terrorism bias' terrorism content 'scapegoat bias' scapegoating 'hypocrisy bias' hypocrisy 'suicidal empathy bias' suicidal-empathy framing 'cruelty bias' cruelty 'woke bias' woke framing 'written by AI' AI-written likelihood 'immature bias' immaturity 'circular reasoning bias' circular reasoning 'covering the response bias' covering-the-response tactic 'spam bias' spam-like content Tip: use get_source_bias for full narrative descriptions and recent articles on a specific source. Tip: bias_values keys here are identical to those in get_bias_from_url and search_news — compare them directly. Warning: get_source_bias returns bias_scores with emoji-prefixed display keys (e.g. '🔵 Liberal <—> Conservative 🔴') that are NOT interchangeable with the plain-text keys used here. Do not cross-reference them.

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• delete_ticket

  Sweeppea MCP

  Permanently delete an open support ticket. Use fetch_open_tickets first to get the case_id. WARNING: This action cannot be undone. Only open tickets can be deleted. # delete_ticket ## When to use Permanently delete an open support ticket. Use fetch_open_tickets first to get the case_id. WARNING: This action cannot be undone. Only open tickets can be deleted. ## Parameters to validate before calling - case_id (string, required) — The case number of the ticket to delete ## Notes - DESTRUCTIVE — IRREVERSIBLE. Always confirm with the user before calling. Explain what will be lost.

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• verify_claim

  Ground Truth - First Tool Call

  Check whether a factual claim is supported by a specific set of public evidence URLs that you already have. For each source, the tool performs a case-insensitive keyword match over the fetched page body, then marks that source as supporting the claim when at least half of the supplied keywords appear. Use this for evidence-backed claim checks on known pages, not for open-ended search, semantic reasoning, or contradiction extraction. The aggregate verdict is driven only by the per-page keyword support ratio. Fetched pages are cached for 5 minutes.

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• regulatory_search_open_rulemakings

  DataNexus MCP

  Search open rulemakings and public comment periods on Regulations.gov and the Federal Register. Read-only. No side effects. Idempotent. US federal only. keyword: Topic keywords e.g. artificial intelligence, data privacy. Required. agency: Agency abbreviation e.g. FTC, FDA, SEC, EPA. Optional, defaults to all agencies. status: One of open, closed, or all. Optional. Default open. Returns docket title, agency, comment deadline, docket ID, and document count. Use this when monitoring regulatory activity on a topic. Use regulatory_fetch_docket_details instead when you have a docket ID and need full detail. Verified source: Regulations.gov + Federal Register. 4-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="regulatory_search_open_rulemakings", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

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• package_health

  dynamic-feed

  Health & security posture of a software package (npm / PyPI / Go / Maven / Cargo / NuGet / RubyGems) from deps.dev (Google Open Source Insights, keyless): latest version, license, count of known security advisories, the OpenSSF Scorecard (0-10 security-posture score for the source repo + its weakest checks) and popularity (stars/forks). The "should I depend on this?" check — pairs with check_vulnerability (is a version vulnerable) and software_version (is the runtime current). Args: package (e.g. "lodash", "requests"), ecosystem (npm|pypi|go|maven|cargo|nuget|rubygems), version (optional — defaults to the latest).

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• chieflab_boot

  ChiefLab

  Alias of chieflab_status. Use as the FIRST tool when an agent session starts on a workspace that already has activity — recovers all open business loops with literal user commands. Same response shape as chieflab_status, same handler. If the user asked to launch the current repo and a recovered open loop looks unrelated, do not blindly resume it; start a fresh launch for the current repo.

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• evm-token-security

  The Stall

  Honeypot, rug-pull, and scam detection for any EVM token. Returns a 0–100 risk score with labeled flags: honeypot status, hidden ownership, mint authority, self-destruct, buy/sell tax rates, creator wallet concentration, and open-source status. Covers 40+ chains (Ethereum, Base, BSC, Arbitrum, Polygon, Solana, etc.) via GoPlusLabs. Useful pre-trade before buying unknown tokens, before routing payments through new contracts, or when validating DeFi protocol addresses. Pairs with solana-token-risk (Solana-native rug detection) and market-intelligence (endpoint verification).

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• pubmed_europepmc_search

  pubmed-mcp-server

  Search Europe PMC, a broad open-access biomedical corpus. Surfaces preprints (`source: PPR`), patents (`source: PAT`), Agricola (`source: AGR`), plus everything in PubMed (`MED`) and PMC. Use when additional coverage is needed — preprints and EPMC-only OA records are the typical recovery. Paginate via `cursorMark`. Defaults to `MED`, `PMC`, and `PPR`; pass `sources` to include `PAT` / `AGR`.

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• dashboard-get

  hjarni

  Get an overview of the Second Brain: counts of notes, containers, tags, and inbox items, plus recent_notes (the 5 most recently created personal notes) and recent_changes (the 5 most recently edited notes across ALL spaces — personal, teams, and shared containers — newest edit first). Use recent_changes to orient at the start of a conversation on what changed lately everywhere. No parameters required.

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• competitor_intel

  gapup-mcp

  LLM-narrated competitive-intelligence BRIEFING — for human consumption (board meeting, pitch prep). Pair tool: `competitive_deep_dive` for raw structured multi-source data (agent-shaped JSON). Returns: recent competitor moves with severity (critical/high/medium/low), prioritised signals, pricing-radar comparison, 3-6 quantified recommendations (impact in € or %, 7/30/90/180-day horizons), and an 8-12 slide presenter script. Use when the buyer wants a narrative briefing or a deck. Inputs: your company (name + one-paragraph pitch) + 1-10 competitors. Delivered by Manue, AI CMO of the Gapup portfolio.

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• get_stats

  Velvoite — EU Financial Regulatory Compliance

  Get an overview of the Velvoite regulatory corpus. Returns document counts by source, regulation family, entity type, urgency distribution, obligation summary, and date range. Call this FIRST to orient yourself before running queries. No parameters needed.

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• usaspending_get_agency

  usaspending-mcp-server

  Fetch an agency's current fiscal year overview including mission, budget authority, obligation totals, sub-agency count, and DEF codes for disaster/emergency funding. Also returns sub-agency breakdown with transaction counts. Accepts either a 3-digit toptier_code (e.g., 097 for DoD, 012 for Agriculture) or an agency_slug (e.g., department-of-defense) — both appear in usaspending_list_agencies results and award search results.

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• list_domains

  OpenDataModels MCP server

  List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})

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• analyze_public_filing

  Amalgix

  Crucible™ Financial Filing Intelligence for public filings and annual reports. Accepts content, filingUrl, or SEC ticker/CIK lookup for 10-K, 10-Q, and 20-F filings. Returns metrics, risk changes, contradictions, source evidence, confidence, and a not-investment-advice flag.

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• security_fetch_package_licence

  DataNexus MCP

  Fetch the SPDX licence identifier for an open source package version. Read-only. No side effects. Idempotent. package: Package name e.g. flask. Required. version: Exact version string e.g. 2.3.0. Required. ecosystem: One of PyPI, npm, Maven, Go, Cargo, NuGet, RubyGems. Required. Returns the SPDX licence identifier e.g. MIT, Apache-2.0, GPL-3.0. Use this to verify licence compatibility before including a dependency. Use security_fetch_package_vulnerabilities instead when checking for security issues not licences. Verified source: deps.dev (Google). 1-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="security_fetch_package_licence", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

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• bitcoin_network

  dynamic-feed

  Live Bitcoin NETWORK telemetry — infrastructure metrics only, NO price data. `metric` = "overview" (default, full digest) | "fees" (recommended fee tiers sat/vB) | "mempool" (unconfirmed tx count + vsize + pending fees) | "difficulty" (current + next adjustment % + blocks until retarget) | "hashrate" (3-day average EH/s) | "halving" (blocks remaining + estimated date + current/post-halving block subsidy). Overview returns all sections in one call. Source: mempool.space public REST API (open-source, AGPL-3.0) with Blockstream Esplora (MIT) as block-height fallback. Keyless, 60 s cache. HARD CONSTRAINT: no BTC/USD or any fiat price — Bitcoin NETWORK mechanics only. Not financial advice.

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• x711_web_search

  x711 — Universal Agent Gas Station

  Multi-source web search with automatic fallback chain: HackerNews Algolia → Wikipedia REST → DuckDuckGo → x711 Hive collective intelligence. Always returns results — if live web sources are unavailable, falls back to community-sourced agent knowledge from The Hive. Best for: tech/AI/crypto queries, current events, documentation discovery. Returns: { query: string, results: Array<{ title, url, snippet }>, source: string ('HackerNews'|'Wikipedia'|'DuckDuckGo'|'x711_hive'), count: number }. Free tier: 10 calls/day, no API key needed.

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• paper_get_text

  livedatalink

  Return the full text of an indexed open-access paper by its corpus key (e.g. 'arxiv:2310.12345'), paginated by passage. Use from_seq + max_passages to page through it. For works not indexed locally, returns a pointer to find the open-access URL via paper_search / paper_details.

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