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523,734 tools. Updated 2026-09-06 14:19

"SWC" matching MCP tools:

  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).
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  • Undo a soft-delete: restores a thesis, watchlist, signal, claim or report that `delete_*` archived. The record returns to the state it held before the delete — a closed thesis comes back closed, a paused signal comes back paused. When the item was deleted before the server began recording its prior state, `prior_status_known` is false and the response says which default was used. A restored report returns to its prior status AND visibility, so a report that was public comes back public and one that was private stays private; when that state predates the change that began recording it, the report returns private and `prior_status_known` is false rather than guessing at publication. Citation overrides are NOT restorable (that delete removes the row outright) — use the approval flow. Idempotent: restoring a live item succeeds and changes nothing. Tier: paid + free (sample rejected).
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • Render a structured research brief into a professionally-styled Word document — a branded masthead-first page (Valuein letterhead: brand rule, wordmark, 'EQUITY RESEARCH' kicker + date, then the ticker eyebrow, the title as hero, and the named analyst's byline), the body (abstract, optional snapshot table with figures in mono, markdown sections incl. GFM tables, and a citations table with clickable SEC EDGAR links), with a running footer (ticker, 'Built on Valuein · valuein.biz', page number, a single disclosure line) repeated on every page. No embedded charts in v1; pair with `generate_dcf_xlsx` / `generate_comps_xlsx` for visuals the analyst pastes in. SERVER-TRUST: prose, snapshot rows, and citations are rendered as-supplied and are NOT verified by Valuein, so the brief carries a visible 'figures supplied by caller, not verified by Valuein' watermark (response `verification.status` = 'unverified'). Resolve each citation via `verify_fact_lineage` before publishing. Consumes the same `sections` + `citations` shape `create_report` emits, so the typical flow is two tool calls: `create_report` → `generate_research_brief_docx`. Tier: pro+.
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  • Returns every valid UK boundary type code mapped to its human-readable label. Call this before using any tool that accepts a `boundary_type` or `boundary_types` argument so you know which codes are legal. Passing an unlisted code to another tool raises a ValueError. Boundary type codes are stable Ordnance Survey identifiers. Common ones: - "CTY" → County - "LBO" → London Borough - "UTA" → Unitary Authority - "MTD" → Metropolitan District - "DIS" → District - "DIW" → District Ward - "CCTY" → Ceremonial County - "HCTY" → Historic County - "WMC" → Westminster Parliamentary Constituency - "GLC" → Greater London Constituency - "SWC" → Scotland/Wales Constituency - "PAR" → Parish - "CED" → County Electoral Division Returns: Dict mapping code → label for all supported boundary types, e.g. {"CTY": "County", "LBO": "London Borough", ...}
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Matching MCP Servers

Matching MCP Connectors

  • Clean SEC EDGAR company financials for AI agents — normalized income statement, balance sheet, cash flow, computed ratios, company profiles, filings, and 10-K/10-Q narrative sections (Risk Factors, MD&A) for 10,000+ US public companies. 5 tools: get_fundamentals, get_ratios, get_company, get_filings, get_sections. Free tier, no card.

  • Access SEC filings efficiently (10-K, 10-Q, etc), save time and tokens, and get cited answers.

  • Fetch the Auditable Research File behind one of the caller's own agent runs — the complete evidence chain an examiner asks for: the originating prompt, every tool the agent called in order, every `fact_id` it cited, every human approval, and which models were used. Assembled from the immutable audit ledger written as the run executed; nothing here is reconstructed or inferred. Name the subject EITHER way, and pass exactly one: `report_id` (a report you wrote or found — from `create_report`, `list_my_reports` or `search_reports`) or `run_id` (from `list_agent_runs`). Naming a REPORT is the richer call: it resolves the run behind that report AND adds two sections a run's ledger cannot carry — `human_review` (each figure a HUMAN verified, corrected, rejected or sourced externally, with who and when) and `sources` (the SEC filing, form, period and filed date behind each cited fact_id). It also echoes the resolved `run_id`. A run-keyed call omits both, because a run may produce several reports and 'the report for this run' has no honest answer; empty or absent there means NOT RESOLVED, never 'no sources'. `format: "pdf"` returns the SAME assembled file as a branded compliance PDF instead of inline JSON — a 15-minute presigned download URL (`url` + `filename`) for the human-facing artifact (cover with the completeness verdict, evidence chain table, provenance with clickable sec.gov links). The PDF is rendered fresh on every call — never cached — because an in-flight run's ledger can gain entries, and a stale 'complete' verdict is exactly the lie this document exists to prevent. ⚠️ ALWAYS READ `completeness` FIRST AND REPORT IT. `completeness.complete` is computed from the ledger, and `completeness.gaps` names every hole found — an irreversible action taken with no named approver, a state-changing action that cited no fact_id, an unrecorded model, a failed step. If you present this run as evidence, present the gaps too; a chain with holes that is quoted as if whole is the one thing this artifact exists to prevent. ⚠️ `found: false` IS NOT A FINDING ABOUT THE WORK. It is returned (not as an error) for an unknown id, an id belonging to another customer, and a report with no run on record — deliberately indistinguishable, so no caller can probe which. It means we hold no audit trail under that id. It does NOT mean the report is unaudited, unverified, or that the id does not exist, and it must never be reported that way. Tier: sp500+ (sample rejected).
    ConnectorNo auth
  • Create or update a standing agent — a saved {goal + tickers + schedule} that fires either a fixed step recipe (agent_type="workflow", free/deterministic) or an AI-directed team (agent_type="autonomous", charged — settles against the owner's BYO key first, falling back to the managed wallet only if funded). Upsert semantics: omit `agent_id` to CREATE a new agent; pass an existing `agent_id` to UPDATE it. There is no separate update_agent — this does both, matching save_watchlist/save_thesis's house style. `agent_type` is STRUCTURAL and immutable: always required, and on an update it is verified against the existing agent before anything is changed — passing a different agent_type than the agent already has is rejected (delete and recreate to change the type). `steps` (an array of {kind:"tool"|"sop", name, args, label?}) is required and non-empty when CREATING an agent_type="workflow" agent, and must be omitted for agent_type="autonomous" (use `managed_model` there instead, itself optional and only valid for agent_type="autonomous"). `when` picks the trigger: "manual" (fires only via run_agent or the Workspace UI) or "schedule" (requires a `schedule` object — cadence "weekly" needs day_of_week, "monthly" needs day_of_month). This tool does NOT itself fire a run — use run_agent for that. Tier: sp500+ (sample rejected).
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  • Soft-delete a signal by its id (from create_signal/list_signals): status flips to `deleted` and it is removed from the cron evaluator index so it stops firing. Signals are immutable — to change one, delete then create_signal. Idempotent. Tier: sp500+ (sample rejected).
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    Destructive
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  • The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.
    ConnectorNo auth
  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
    ConnectorNo auth
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,798 across 1517 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • Parsed SEC Form 4 insider trades for a ticker: owner, role, transaction code (P open-market purchase, S sale, A award, M exercise, F tax withholding), shares, price, dollar value, and a 10b5-1 plan flag. Code P is the only own-money buy signal; A/M/F are automatic compensation. Set form=3/5 for holdings statements or form=144 for notices of proposed sale (an intent-to-sell signal that precedes the Form 4). Pass ticker='latest' for the market-wide feed of the biggest open-market buys, or ticker='clusters' for companies where several insiders bought at once.
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • Newest-first listing of the caller's in-app inbox. Items are signal FIRES with a `dashboard` channel — written by the cron evaluator (or `test_signal`) — plus platform notifications written by the edge-gateway (agent run completions, morning briefs, skipped runs); use list_signals instead for the signal definitions themselves. By default dismissed items are hidden and read items are included. Cursor-paginated by `fired_at`. Sample tier rejected — signals are a paid-tier feature (sp500+).
    ConnectorNo auth
  • The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.
    ConnectorNo auth