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459,989 tools. Updated 2026-08-17 10:17

"Tools for Converting LaTeX Mathematics to Lean Formalizations" matching MCP tools:

  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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  • Read a Revise document's content. Format "markdown" (default) is the simple dialect — best for plain prose; set with_block_ids to true to interleave <!-- block:xxxxxx --> anchors usable with edit_document. Format "html" is the full-fidelity dialect: compact HTML-like markup with a block id on every element plus everything markdown cannot express — rich marks, <latex> math, code block languages, merged table cells, page layout, and each comment thread inline as a <comment-thread transcript="..."> wrapper around its anchored text. Prefer html when a document uses rich features or has comments; edit_document accepts the same dialect in replacements. "text" is plain text. (For a styled, self-contained HTML file, use export_document instead.) view controls how pending tracked-change suggestions read: "final" (default, as if accepted) or "original" (as if rejected). Long documents are paginated: when a read exceeds the character budget it is cut at a block boundary and the response carries truncated: true, next_start_block, and a ready-to-run example call — repeat with start_block to continue. For a targeted read of a large document, prefer get_document_outline + search_within_document over paging through everything.
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  • Fetch a public HTTPS URL and answer a specific question about its content. Lean mode — no bundle stored. Use when you have a precise question about a web page. For a broad summary, use url.summarize. For multi-document Q&A, use collection.ask instead. Returns: { url, answer, answer_cited: { value, confidence, citations[] }, confidence: "high"|"medium"|"low", truncated } Example prompts: - "What is the refund policy at https://docs.example.com/policy?" - "Look at [URL] and tell me what the delivery terms are." - "Answer this question based on the content of [URL]: [question]."
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  • Find logs matching filter criteria within a time range. Use this as your default starting point for log queries. Returns logs sorted by (timestamp, logId) descending (newest first). Returns the log's main fields by default; pass verbose=true to include its attributes (http/url/… flattened in, plus a `resource` object). Long string values are capped (maxStringChars). For raw columns or custom selection use run_sql. For the full untruncated body of one row, use get_log. Defaults: from/to: open window if omitted — beware of unbounded scans limit: 100 (max 1000) service/level: any Common patterns: - Errors in the last hour: level="ERROR", from=<1h ago> - Logs for a trace: traceId="abc123..." (index-accelerated) - Whole-word token search: messageContains="timeout" - Substring/regex search: not supported here; use run_sql Returns: logs: array of log objects (lean unless verbose=true) nextCursor: opaque token (null on the last page); pass back as cursor to fetch the next page explorerUrl: shareable Fixter UI link opening this query in the log explorer — attach it when citing these logs as evidence to the user (covers the service/level/traceId filters and the window; timestamps display in the viewer's browser timezone) queryStats: rowsReturned, elapsedMs
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  • Export scholarly identifiers to a bibliography file format ready to write to disk or paste into a reference manager. Use when the user wants a file (.bib, .ris, .nbib, .xml, .rdf, .csv) for Zotero, Mendeley, EndNote, RefWorks, BibTeX/LaTeX, Pandoc, or Excel. Format parameter is required: bib (BibTeX — LaTeX), ris (RIS — most widely supported by reference managers), csl (CSL JSON — Pandoc/Quarto), endnote-xml, endnote-refer, refworks, medline (NBIB — PubMed round-trips, clinical workflows), zotero-rdf, csv (spreadsheet-friendly), or txt (plain-text bibliography rendered with the optional style parameter — txt is the only format that uses style; the others have their own structured shape and ignore it). Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: { content: string, format: string } where content is the entire bibliography in the requested format as a single string — write it to a file (.bib/.ris/.nbib/etc.) or paste it directly into the target tool. Use formatCitation instead when the user wants in-line citation text (manuscript, slide); use resolveIdentifier when they want raw structured metadata. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.
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  • Bitstamp's own EUR↔USD conversion rate — the buy/sell prices Bitstamp applies when converting fiat balances on the exchange, quoted with the exchange's retail markup (the two legs can differ by several percent). Use when you need the rate a Bitstamp account actually transacts at. For the market FX rate — "EURUSD spot", "current euro to dollar rate", anything macro or reporting-grade — use ecb_exchange_rate, which publishes the official euro reference rate.
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Matching MCP Servers

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    A comprehensive MCP server that turns any AI assistant into a powerful mathematical computation engine, providing 52 advanced functions, 158 unit conversions, financial calculations, and secure AST-based evaluation.
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    MIT
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    An MCP server for ingesting Lean Six Sigma PDFs into a searchable multimodal knowledge base.
    MIT

Matching MCP Connectors

  • Persistent AI LaTeX workspace: edit and compile multi-file projects, export publication-ready PDFs.

  • Decision Layer for AI Agents — 58+ tools, Advisor, MCP. Free key: POST /v1/register {}.

  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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  • Get a Chinese-metaphysics signal on a binary prediction-market question (Polymarket/Kalshi style yes/no outcomes). Use when a user — or a trading agent — wants an UNCORRELATED, for-fun read on a market: 'will X happen by date Y'. Returns a lean (yes/no/neutral), a confidence, the 五行 reasoning from the resolution date's energy, and a mandatory disclaimer. This is ENTERTAINMENT and a falsifiable ritual — NOT financial advice. Always present it as a novelty signal, never as a recommendation to place a bet.
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  • Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.
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  • Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."
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  • Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."
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  • Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use url.extract instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."
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  • List indicators, or fetch one indicator's full schema. Cheap, cacheable per session. With no arguments: a compact catalog — ``{"indicators": [...], "count": N}`` — where each entry carries id, name, category, kind, and value_dtype (no description, to keep the discovery scan small). Use it to discover what exists. Pass name='rsi' (id or name, case-insensitive) to get that single indicator's complete entry including its description and params_schema — do this before adding an indicator to a strategy so its parameters are exactly right. Pass compact=False for full entries for everything (large; the MCP server may cap it and set ``truncated_by_mcp`` — prefer compact or name=). Wire optimization: the compact discovery path asks the engine to omit per-entry descriptions (``descriptions=false``) since they are stripped locally anyway; the name= and compact=False paths request them. This is a pure saving — if the engine ignores the param it returns full entries and the local compact strip still yields a lean result.
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  • Convert HTML or Markdown to a pixel-perfect PDF. Returns JSON: { url } — a temporary download URL (valid ~1 hour). Great for generating invoices, reports, receipts, or formatted documents programmatically. Supports full HTML/CSS including tables, images (base64 or URL), and inline styles. For Markdown input, set format='markdown'. 50 sats per conversion. Use convert_file instead for converting existing files between formats (e.g., DOCX→PDF). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='convert_html_to_pdf'.
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  • Submit a new version of an existing document. The earlier version is not replaced: it keeps its own identifier, so an existing citation of it still resolves to the exact text it referred to. The previous version's chunks will be marked as not-latest. Omit `categories`, `keywords`, or `language` to inherit each independently from the previous version; pass a value to override. Content is file-only: provide a base64-encoded ZIP archive (content_archive_base64) OR a content_ref from an out-of-band upload — exactly one. A ZIP may hold a single PDF, markdown + figures, or multifile LaTeX. Inline text is no longer accepted. For content above ~10 KB, prefer create_upload_url → PUT the file to the returned URL → pass the returned file_id as content_ref (avoids base64 token bloat). content_archive_base64 and content_ref are mutually exclusive — provide exactly one. Limits: title ≤5,000 chars; abstract ≤50,000 chars; archive ≤50 MB; keywords ≤50 items × ≤100 chars each. Set dry_run=true to validate without committing: no document is created, nothing is queued, no credits are charged; the response shows what would be saved and the estimated cost.
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  • Report a problem or request to Pure Report's operator and get a tracking ref back. Use this when the data looks wrong — a bias score or outlet lean that doesn't match the source, an event cluster mixing unrelated stories, a missing neutral writeup, a broken article — or to ask a methodology question or request a capability. Include the article_id or event_slug you were looking at; that context is what makes a report actionable. A human reads these: replies take time and are not guaranteed. Call check_feedback with the returned ref to read the reply.
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  • Enter a tenant to receive its tool surface (progressive disclosure). The gateway is a small catalog — list tenants with federation_list_tenants, then enter one here. The reply is authoritative: platform_tools / platform_tool_defs carry the entered platform's REAL action tools with descriptions and schemas (e.g. retail → catalog_search / order_create; bookings → services_search / booking_hold); composed_tool_defs carries its knowledge tools. Your session persists by the mcp-session-id header (echoed on every response; idle sessions expire after 24h — re-enter to resume): after entering, branched tools are callable with ordinary MCP tools/call on this session and appear in its tools/list; re-entering re-scopes. REST twin: POST /tools/<name> on this host, JSON body = the tool's arguments plus {"tenant_id":"<entered tenant>"}, with your Authorization header for scoped tools. Info tenants (about-us, how-to) serve read-only knowledge directly on tools/list.
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  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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