"Using a search engine to collect company names and descriptions and save to Excel" matching MCP tools:

• TOOL_LIST

  xpay✦ Finance Collection

  List all available Alpha Vantage API tools with their names and descriptions. IMPORTANT: This returns only tool names and descriptions, NOT parameter schemas. You MUST call TOOL_GET(tool_name) to retrieve the full inputSchema (required parameters, types, descriptions) before calling TOOL_CALL. Calling TOOL_CALL without first calling TOOL_GET will fail because you won't know the required parameters. Workflow: TOOL_LIST -> TOOL_GET(tool_name) -> TOOL_CALL(tool_name, arguments)

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• analyze_company

  Nordic Financial MCP

  AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'

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• eia_search_routes

  eia-energy-mcp-server

  Fuzzy text search across route names, descriptions, and category labels. Resolves natural-language queries like "electricity retail sales by state" or "natural gas imports" to matching route paths. STEO series names are indexed so queries like "ethanol net imports" or "crude oil production forecast" also resolve. Results include isLeaf so you know whether to browse further or query directly. Results with score > 0.5 are weak matches — try a more specific query or use eia_browse_routes to explore the taxonomy.

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• search

  Melvea Local Honey Discovery

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

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• search_engines

  reefapi-mcp

  Find the right ReefAPI engine for a task — pass ENGLISH keywords or a short natural-language use-case ("detect a website's tech stack", "company reviews", "check a package for vulnerabilities", "is this domain available"). The catalog is in English: if the end-user asked in another language, translate their INTENT into English keywords first (you are an LLM — do this inline). Ranks engines by how well the query matches each engine's name/title/category/ACTION descriptions (stem-matched, so plurals/word-forms still hit). Empty query = list all. Returns name/title/category/actions + match score. Call this FIRST, then get_engine_schema(engine) to pick an action. This is a fast keyword pre-filter — if the right engine isn't in the results (or you want to be sure), call get_catalog and pick from the full list YOURSELF (you semantically match any language/phrasing better than keywords).

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• find_matches_in_index

  RChilli MCP Hub

  Match one source document against the user's ALREADY-INDEXED corpus and return the best-matching, ranked candidates (RChilli Search & Match Engine). Requires a populated index. Uses RChilli's purpose-built matching engine — more reliable than manually comparing documents. Use this when the user wants to: find the best/top matching resumes for a JD, find matching candidates from their pool, or rank their indexed resumes/JDs against a given document — e.g. "find the best candidates in my database for this job". Also phrased as: shortlist from my pool, top matches for this JD, rank my candidates. Do NOT use for: scoring a single resume against a single JD with no index (use ``search_one_match``); plain keyword lookup (use ``search_simple_search``). Supports all four match directions by combining ``index_type`` and ``doc_type``: - **JD to Resume** — ``index_type='Resume'``, ``doc_type='JD'``: Search the Resume index using a JD as the source document. - **Resume to Resume** — ``index_type='Resume'``, ``doc_type='Resume'``: Search the Resume index using a Resume as the source document. - **Resume to JD** — ``index_type='JD'``, ``doc_type='Resume'``: Search the JD index using a Resume as the source document. - **JD to JD** — ``index_type='JD'``, ``doc_type='JD'``: Search the JD index using a JD as the source document. The ``document_text`` is automatically parsed using the RChilli Resume or JD parser (driven by ``doc_type``), and the resulting structured JSON is base64-encoded and submitted as the match source — no manual encoding is required. Args: index_type: Index to search — ``Resume`` (default) or ``JD``. index_key: Same as ``userkey`` — the RChilli API user key. Leave blank; the authenticated session userkey is injected automatically. doc_type: Type of the source document — ``Resume`` (default) or ``JD``. This determines which parser processes ``document_text``. document_text: Plain-text content of the source document. Parsed and encoded to base64 JSON internally.

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• get_catalog

  reefapi-mcp

  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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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• search_datasets

  Nova Scotia Data Explorer

  Search the Nova Scotia Open Data catalog (data.novascotia.ca) for datasets by keyword, category, or tag. Returns dataset names, IDs, descriptions, column names, and direct portal links. Use list_categories first to see valid category and tag names. Use the returned dataset ID with query_dataset or get_dataset_metadata for further exploration.

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• discover_collections

  Ragora

  Discover all knowledge bases you have access to. Returns collection names, descriptions, content types, stats, available operations, and usage examples for each collection. Call this first to understand what data is available before searching.

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• disqualified_profile

  UK Due Diligence

  Fetch the full disqualification record for a director by officer ID. Returns all disqualification orders: reason, Act/section cited, disqualification period, and associated company names. Use disqualified_search first to find the officer ID.

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• fill_form

  form fill-mcp

  Fill a PDF form with the given field values and save the result to disk. WORKFLOW: 1) Call list_form_fields first to get exact field names and their x/y positions. 2) Use position coordinates to confirm which field is which — higher y = higher on page. 3) Pass exact field names from list_form_fields here. Never guess field names. Use for single-page or short forms (under 5 pages). Use fill_form_multipage for longer forms. Returns ok:false with unknown_fields if ALL provided field names are invalid. Returns ok:true with a warnings.unknown_fields list if SOME names are invalid (partial fill).

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• nausika_get_place

  Nausika

  Fetch full detail for a single place given its 'id'. Accepts either a full UUID or the 8-char [xxxxxxxx] short-id shown by nausika_search_places. Returns canonical attributes (name/coords/category/type), localized i18n names+descriptions, wiki image URLs, ratings aggregates, plus extras only this tool provides: the raw OpenStreetMap tags of the primary OSM feature, and direct links to OSM, Wikidata, and Wikipedia. Use this after nausika_search_places returns a result you want to drill into. For proximity / text search, use nausika_search_places.

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• search_tools

  Keploy

  DISCOVER tool names you do NOT already know, by keyword. Most Keploy tools are hidden from the default tool list to save context. If you ALREADY know the exact name (e.g. a skill named it), call get_tool_schema instead — it is exact and far cheaper than this fuzzy search. Returns {"matches": [{name, description, inputSchema}, ...], "total_catalog": N}. Search by intent words, e.g. "test report", "mock patch", "update test case", "cloud replay branch", "record".

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• search_datasets

  Nova Scotia Data Explorer

  Search the Nova Scotia Open Data catalog (data.novascotia.ca) for datasets by keyword, category, or tag. Returns dataset names, IDs, descriptions, column names, and direct portal links. Use list_categories first to see valid category and tag names. Use the returned dataset ID with query_dataset or get_dataset_metadata for further exploration.

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• fda_search_recall_text

  FDA Data MCP

  Full-text search across recall reasons and product descriptions using PostgreSQL text search. Finds recalls mentioning specific terms (e.g. 'salmonella contamination', 'mislabeled', 'sterility'). Supports multi-word queries ranked by relevance. Filter by classification, product_type, or date range. Related: fda_search_enforcement (search by company name, classification, status), fda_recall_facility_trace (trace a recall to its manufacturing facility).

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• fda_link_subsidiaries

  FDA Data MCP

  Explicitly link existing company records under a parent company for family-level rollups. Use this when names like Actavis or Cephalon already belong to their own company_id and should roll up under a parent like Teva. This preserves alias collision safety while making manufacturing and facility summaries family-aware.

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• flatland_export_excel

  Flatland

  Export the active model as an institutional-grade ``.xlsx`` file. Note: ``flatland_export`` (HTML) is the recommended export for sharing with investors, boards, or team members — it works in any browser without Excel. Use ``flatland_export_excel`` when you specifically need live Excel formulas for manual iteration in a spreadsheet application. The exporter is a deterministic compiler: it walks the IR DAG and emits one Excel cell per IR driver, with computed drivers rendered as live Excel formulas (not values) that reference workbook-scope defined names. The resulting file recalculates correctly in Excel/Numbers/Google Sheets/LibreOffice with no manual rewiring. The file follows institutional conventions: blue font + light-blue fill for inputs, black font + light-gray fill for formulas, currency formatted as ``$#,##0`` with parens for negatives and dash for zero, percentages as ``0.0%``, all assertions surfaced as PASS/FAIL rows in a dedicated sheet, and a Checks sheet that verifies compilation, assertions, and formula transpilation succeeded. Every export is auto-scored against Flatland's frozen v1 institutional rubric (see experiments/excel-export-skill/rubric.md). The ``rubric_score`` field in the response is a 0-100 integer; an institutional-grade output scores 90+.

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• patent-intel

  The Stall

  USPTO patent intelligence for any company or keyword. Mode 'company' (ticker or company name): recent granted patents assigned to that company — accepts US stock ticker (resolved via SEC EDGAR) or free-form assignee name. Returns title, abstract excerpt (≤400 chars), grant date, filing date, CPC technology section labels (e.g., 'H – Electricity', 'G – Physics'), CPC group codes, inventor names, and a Google Patents link. Mode 'search' (query): full-text keyword search across all USPTO patent titles and abstracts — useful for finding who is innovating in a technology area (e.g., 'transformer neural network', 'solid state battery'). Covers all US granted patents from 1976 to within ~2 weeks of present. Data source: USPTO PatentsView API (public domain, no API key). $0.008/call.

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