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650,439 tools. Updated 2026-10-13 06:32

"A multifunctional research MCP server for a deep researcher subagent" matching MCP tools:

  • Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; nothing is written, so it is safe to call.
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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Matching MCP Servers

Matching MCP Connectors

  • Autonomous deep research reports merging PSFK trend graphs with citable sources.

  • Autonomous buy-side research: diligence, earnings, SEC filings, comp sets. Source-cited real data.

  • Use this when a deep research run needs to look up digital tools and products on uneed.best. Same catalog and same relevance ordering as search_products, returned as `{id, title, url}` documents; pass a result's id to `fetch` for the full profile. Prefer search_products when you want structured product fields directly.
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  • Open-ended discovery across SMI deep reports, research bundles, and exports when you do not already have a record id — finds candidates by subject name, url, or keyword and returns typed references to pass to fetch. If you already have a record id from the conversation rather than from a search result, call the specific tool instead: smi_get_deep_report or smi_get_export to read one record, smi_check_order_complete for order readiness, smi_list_deep_reports or smi_list_exports for filtered listings, smi_get_report_findings for findings within a report. Deep reports and research bundles are filtered server-side by subject first/last name, broadening to an unfiltered fetch ranked client-side (across id, name, url, summary) when the filtered fetch finds nothing; exports match on url.
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  • Retrieve works associated with an ORCID iD — publications, datasets, software, preprints, and more. Returns work summaries with put-codes, titles, types, publication dates, journal names, and all external identifiers (DOIs, PMIDs, arXiv IDs, ISBNs). The first 50 works are returned by default; workCount reports the total available, and prolific records are paged with offset and the returned nextOffset (or raise limit). A page also stops early once the response reaches its 64,000-byte budget, so returnedCount can come in below limit; truncated and nextOffset then carry the continuation. Set include_external_ids to false to omit identifier lists for a lighter payload. Pass the putCode from each work to orcid_get_work_detail to retrieve the full record including abstract and contributors. External IDs are ready for chaining to Crossref, PubMed, or arXiv servers. Works come from the researcher or from member organizations such as Crossref or a university system; each work lists its sources and whether the researcher asserted it (selfAsserted). A researcher may not have linked all their publications.
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  • Health check: confirm the eDiscovery Decoder News/Calc MCP server is reachable before a demo or when troubleshooting a connection. Returns server name and version. No inputs.
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  • Fetch one document's full extracted text by id (a file id from search / search_files / list_files), in the deep-research result shape. ALIAS: this is the SAME read as get_file (same data, same permissions, same audit, same size guard - large files are truncated) - use it when your client requires the id/title/text/url fetch contract (ChatGPT deep research); otherwise prefer get_file, which also serves download links and inline images. Read-only; audited.
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  • Run a multi-agent research pass and return a structured, sourced report. Six roles run in order — data analyst, factor researcher, backtest engineer, risk officer, portfolio manager, compliance officer. Each step's output carries the `query_ids` behind it; risks are reported alongside results, not beneath them; and anything the run could not do is listed as a limitation rather than filled in. The factor researcher checks memory first and SKIPS a hypothesis a previous run already rejected. The portfolio manager proposes nothing when the evidence failed the anti-overfitting gate, and any allocation it does propose is a PROPOSAL awaiting a human — this system places no orders and moves no money. Args: prompt: the research question, in your words. This is the only channel carrying instructions; anything a tool returns is treated as data. as_of: the knowledge cutoff. REQUIRED — nothing stamped after it is visible to the run. tickers: optional explicit universe. Omit for a point-in-time (survivorship-safe) one. start / end: optional test period; `end` must not be after `as_of`. max_backtests: per-run cap on backtests (cost control).
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  • Get Lenny Zeltser's IR cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `ir_load_context`. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • FREE preview scan of a target MCP server for tool-poisoning / prompt-injection. Returns issue count, severity breakdown, risk score, and verdict (clear/review/block) — but NOT which tools or the evidence. Use this to check any MCP server (including your own) at no cost; if issues are found, call the paid scan_mcp_server for the itemized findings + remediation. No payment required.
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  • Fetches a single SMI deep report by id. Research Bundles are deep-report products, so use this tool for a Research Bundle id too rather than constructing a research_bundle: reference for the generic fetch tool. Accepts product-prefixed ids: RB-11751 (Research Bundle) and DR-11751 (Deep Report) both resolve here.
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  • Search or read Modem's public docs to answer how Modem works: setup, integrations, this MCP server and its scopes, the API, and automations. Not for your organization's data; use `search_modem` for that. Send a `query` to search, or a `page` path from a search result (such as `api/modem-mcp-server`) to read the whole page. Send exactly one. Read-only, spends no agent credits.
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  • Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed. Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.
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  • Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape.
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  • Search VICP vaccine-injury cases by free text. Returns result ids for use with fetch. (Alias of search_cases for deep-research clients; prefer search_cases when structured filters are available.)
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