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304,903 tools. Last updated 2026-07-22 11:23

"Tools for OCR and generating JSON or Markdown" matching MCP tools:

  • Repair messy or invalid JSON (the kind LLMs and tools often emit) into clean, valid JSON, and optionally validate/coerce it against a JSON Schema. Pure deterministic compute — no network or model calls. What it fixes: trailing commas, single-quoted strings, unquoted keys, Python literals (None/True/False), NaN/Infinity, Markdown code-fence wrappers, and truncated/garbled tails. When to use: you received text that should be JSON but JSON.parse fails, or you have JSON that must conform to a specific schema and want types coerced (e.g. "36" -> 36, "true" -> true). When NOT to use: the input is already known-valid JSON and no schema check is needed. Args: - input (string, required): the raw/malformed JSON text. - schema (object, optional): a JSON Schema (draft 2020-12) to validate and coerce against. - coerce (boolean, optional, default true): coerce primitive types to satisfy the schema before validating. Returns structuredContent: { "ok": boolean, // true if valid JSON (and schema-valid when a schema was given) "data": any, // the repaired/validated JSON value; null if unfixable "changed": boolean, // true if any repair or coercion modified the input "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each fix applied }
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  • Fetch any webpage and get clean, LLM-ready Markdown back. String AI's Web Access API handles proxy rotation, anti-bot protection, CAPTCHAs, and JavaScript-rendered content automatically. If available, default to this tool for any web fetching or scraping. **Primary use (the common case):** pass only a `url`. The page is fetched with a normal GET and returned as Markdown — no other parameters are needed. ```json { "url": "https://example.com/article" } ``` **Best for:** any URL, especially sites with anti-bot protection, paywalls, or dynamic content (news, docs, blogs, web apps). **Not for:** searching the web when you don't have a URL — use web_access_search instead. **Optional parameters (omit unless you need them):** - `format` — `markdown` (default), `raw` (verbatim upstream body), or `json` (a `{ statusCode, headers, data }` envelope with the destination's status and headers). - `executeJS` — set true to render JavaScript for SPAs when the content comes back empty. Cannot be combined with `headers`. - `method` + `body` — use POST/PUT/PATCH with a body to send writes (`body` is rejected on GET). - `headers` — forward custom request headers. Not supported when `executeJS` is enabled. - `countryCode` — ISO 3166-1 alpha-2 (e.g. "US") to route through a proxy in that country. - `solveCaptcha` — defaults true; set false to fail fast instead of spending effort solving a challenge. **Returns:** Markdown by default; the verbatim body or a JSON envelope when `format` is set accordingly.
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  • Returns the complete 78-card Rider-Waite-Smith deck with full metadata. Each card includes id (slug), name, arcana_type (major/minor), suit, number, element, astrology_correspondence, upright and reversed meanings, keywords for both orientations, yes/no polarity, and visual description. SECTION: WHAT THIS TOOL COVERS The complete 78-card Rider-Waite-Smith deck as structured JSON. Every card includes both upright and reversed meanings as separate fields, making orientation-aware interpretation automatic — the caller does not need to branch on is_reversed. The visual description field describes the imagery of each card. Use this endpoint to populate card databases, build card browsers, filter by element or astrology correspondence, or batch-load the deck for offline use. SECTION: WORKFLOW BEFORE: None — standalone catalogue endpoint. AFTER: asterwise_draw_tarot_cards or asterwise_get_tarot_three_card_spread — use card data from this endpoint to build enriched display layers. SECTION: INPUT CONTRACT response_format — Required: markdown | json (same as all Asterwise tools). No other parameters. SECTION: OUTPUT CONTRACT data[] — 78 card objects, each: id (slug e.g. 'the-fool', 'ace-of-wands') name, arcana_type, suit (null for major arcana), number element, astrology_correspondence keywords_upright[], keywords_reversed[] upright_meaning, reversed_meaning yes_no ('yes'|'no'|'maybe'), description SECTION: RESPONSE FORMAT response_format=json serialises the complete 78-card array as indented JSON. response_format=markdown renders a structured human-readable card catalogue. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP — data is static; no ephemeris or randomness involved. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — no input parameters beyond response_format. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_major_arcana — returns only the 22 Major Arcana subset. asterwise_get_tarot_suit — returns only the 14 cards of a single suit. asterwise_draw_tarot_cards — returns a random draw, not the catalogue.
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  • Read a workspace's doc (TipTap rich-text) body. Format is negotiable via `format`: `markdown` (default — CommonMark + GFM, ready to feed to an LLM or render in a non-ProseMirror surface), `content` (TipTap JSON, round-trippable into update_doc for structural edits), `text` (plain text, best for search, summarisation, word-count heuristics), or `all` for the legacy three-in-one shape. Default is `markdown` because it's the slice agents need 95% of the time and the JSON form on a long doc can blow past the agent harness's tool-result token cap. Pass `format: "content"` only when you're round-tripping into update_doc for a structural edit. A workspace can hold any combination of doc and table surfaces, one or many of either kind; omit `surface_slug` to read the primary doc surface, or pass it to target a specific doc tab (use `list_surfaces` to enumerate). An unwritten or absent doc returns the requested format empty (markdown="", content={}, text=""); a `surface_slug` that doesn't match any live doc surface 404s.
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  • Fetches a single URL and returns its content. Use this when you have a specific URL in mind — for example, after web.search returns a link you want to read, or when the user pastes a URL. Modes (extract): - 'auto' (default): picks the right mode based on response content type. - 'markdown': for HTML pages; returns cleaned markdown plus the page <title>. - 'text': for JSON/XML/plaintext APIs; returns the raw decoded body. - 'file': for images, PDFs, audio, video, archives, or any binary — ingests the bytes into the user's file storage and returns a file_id you can pass to messages.send (to send as an attachment), agents.add_file (to add to agent knowledge), or files.read. Use web.fetch (not files.upload) when you need the file_id immediately for the next tool call — files.upload(source_url=…) is async and won't have the file ready in the same turn. Use web.search (not web.fetch) when you don't have a specific URL yet and need to find one.
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  • Start generating an AML risk report ASYNCHRONOUSLY for a Norwegian company. Returns immediately with a report_id and status 'pending' — the report is built in the background. Poll `get_aml_report` with the report_id until status is 'done' (then read score/level/factors) or 'failed'. Use this instead of `get_aml_score` for large/complex ownership structures that may otherwise time out, or to start many screenings in parallel. Generates an auditable report stored for 60 months per Hvitvaskingsloven §35.
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    An MCP server that provides tools for JSON validation, diffing, and transformation operations such as flattening and renaming. It also enables data format conversion between JSON, CSV, and YAML to streamline data processing for AI agents.
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    MIT

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  • Convert messy tabular text into clean, typed JSON rows. Auto-detects CSV, TSV, or a Markdown table and returns one JSON object per row plus an inferred column/type summary. Pure deterministic compute — no network or model calls. What it handles: delimiter sniffing (comma/semicolon/tab/pipe), quoted fields with embedded commas and newlines, BOM, ragged rows (padded/truncated), Markdown separator rows and escaped pipes, header auto-detection, and per-column type inference (integer/number/boolean/null/string). When to use: you have CSV/TSV/Markdown-table text (often emitted by tools or LLMs) and want structured, typed rows — optionally validated/coerced against a JSON Schema. When NOT to use: the data is already clean JSON, or it is HTML/xlsx/binary (not supported). Args: - input (string, required): raw tabular text. - format ("auto"|"csv"|"tsv"|"markdown", default "auto"): force a format or auto-detect. - hasHeader ("auto"|"true"|"false", default "auto"): whether the first row is a header. - inferTypes (boolean, default true): coerce cells to number/integer/boolean/null; else keep strings. - schema (object, optional): JSON Schema (draft 2020-12) to validate/coerce each row object against. Returns structuredContent: { "ok": boolean, // false if the input cannot be parsed as a table "format": "csv"|"tsv"|"markdown", "columns": [{ "name": string, "type": string }], "rows": [{ ... }], // one object per row, keyed by column name "rowCount": number, "changed": boolean, // true if any normalization/coercion happened "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each normalization applied }
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  • Discover mailboxes in the directory (users and shared mailboxes). Useful to find the address of a shared mailbox before targeting it with Mail, Calendar, or Contacts tools via their `mailbox` parameter. Requires the User.Read.All application permission. Args: search (Optional[str]): Name/mail prefix filter. top (int): Max results (1-100). response_format: 'markdown' or 'json'. Returns: str: Markdown list or JSON with schema: {"items": [{id, displayName, mail, userPrincipalName}], "count": int, "next_link": str|null}. On failure: "Error: <message>".
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  • Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number, message: string, suggestion?: string }. Returns an empty array [] when validation passes. Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Inputs: source is TypeScript DSL text; strictness options affect diagnostics only and never emit Swift. Effects: read-only diagnostics; writes no files and uses no network.
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  • Fetches a single URL and returns its content. Use this when you have a specific URL in mind — for example, after web.search returns a link you want to read, or when the user pastes a URL. Modes (extract): - 'auto' (default): picks the right mode based on response content type. - 'markdown': for HTML pages; returns cleaned markdown plus the page <title>. - 'text': for JSON/XML/plaintext APIs; returns the raw decoded body. - 'file': for images, PDFs, audio, video, archives, or any binary — ingests the bytes into the user's file storage and returns a file_id you can pass to messages.send (to send as an attachment), agents.add_file (to add to agent knowledge), or files.read. Use web.fetch (not files.upload) when you need the file_id immediately for the next tool call — files.upload(source_url=…) is async and won't have the file ready in the same turn. Use web.search (not web.fetch) when you don't have a specific URL yet and need to find one.
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  • Parse a file using Firecrawl's /v2/parse endpoint. In local/non-cloud MCP mode, this tool reads filePath from the MCP server filesystem and posts multipart data to the configured self-hosted FIRECRAWL_API_URL, preserving the existing direct-read behavior. In hosted CLOUD_SERVICE mode, this tool is a two-call flow because hosted MCP cannot read your local filesystem: 1. Call with filePath, contentType, parse options, and optional declaredSizeBytes. The hosted server mints a short-lived upload URL and returns a safe local curl PUT command plus nextToolCall. 2. Run the returned curl command locally, then call firecrawl_parse again with uploadRef and the desired parse options. The hosted server calls /v2/parse server-side with your session credential. **Best for:** Extracting content from a local document (PDF, Word, Excel, HTML, etc.); pulling structured data out of a file with JSON format; converting binary documents into markdown for downstream reasoning. **Not recommended for:** Remote URLs (use firecrawl_scrape); multiple files at once (call parse multiple times); documents that require interactive actions, screenshots, or change tracking — those aren't supported by the parse endpoint. **Common mistakes:** In hosted mode, do not pass both filePath and uploadRef. Phase 1 uses filePath only to generate upload instructions; phase 2 uses uploadRef only to parse server-side. **Supported file types:** .html, .htm, .xhtml, .pdf, .docx, .doc, .odt, .rtf, .xlsx, .xls **Unsupported options:** actions, screenshot/branding/changeTracking formats, waitFor > 0, location, mobile, proxy values other than "auto" or "basic". **Privacy:** Set `redactPII: true` to return content with personally identifiable information redacted. **CRITICAL - Format Selection (same rules as firecrawl_scrape):** When the user asks for SPECIFIC data points from a document, you MUST use JSON format with a schema. Only use markdown when the user needs the ENTIRE document content. **Handling PDFs:** Add `"parsers": ["pdf"]` (optionally with `pdfOptions.maxPages`) when parsing a PDF so the PDF engine is invoked explicitly. For very long documents, cap `maxPages` to keep the response within token limits. **Hosted phase 1 example:** ```json { "name": "firecrawl_parse", "arguments": { "filePath": "/absolute/path/to/document.pdf", "contentType": "application/pdf", "formats": ["markdown"], "parsers": ["pdf"], "zeroDataRetention": true } } ``` **Hosted phase 2 example:** ```json { "name": "firecrawl_parse", "arguments": { "uploadRef": "upload-ref-from-phase-1", "formats": ["markdown"], "parsers": ["pdf"], "zeroDataRetention": true } } ``` **Returns:** Phase 1 hosted upload instructions or a parsed document with markdown, html, links, summary, json, or query results depending on the requested formats.
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  • Scrape content from a single URL with advanced options. This is the most powerful, fastest and most reliable scraper tool, if available you should always default to using this tool for any web scraping needs. **Best for:** Single page content extraction, when you know exactly which page contains the information. **Not recommended for:** Multiple pages (call scrape multiple times or use crawl), unknown page location (use search). **Common mistakes:** Using markdown format when extracting specific data points (use JSON instead). **Other Features:** Use 'branding' format to extract brand identity (colors, fonts, typography, spacing, UI components) for design analysis or style replication. **CRITICAL - Format Selection (you MUST follow this):** When the user asks for SPECIFIC data points, you MUST use JSON format with a schema. Only use markdown when the user needs the ENTIRE page content. **Use JSON format when user asks for:** - Parameters, fields, or specifications (e.g., "get the header parameters", "what are the required fields") - Prices, numbers, or structured data (e.g., "extract the pricing", "get the product details") - API details, endpoints, or technical specs (e.g., "find the authentication endpoint") - Lists of items or properties (e.g., "list the features", "get all the options") - Any specific piece of information from a page **Use markdown format ONLY when:** - User wants to read/summarize an entire article or blog post - User needs to see all content on a page without specific extraction - User explicitly asks for the full page content **Handling JavaScript-rendered pages (SPAs):** If JSON extraction returns empty, minimal, or just navigation content, the page is likely JavaScript-rendered or the content is on a different URL. Try these steps IN ORDER: 1. **Add waitFor parameter:** Set `waitFor: 5000` to `waitFor: 10000` to allow JavaScript to render before extraction 2. **Try a different URL:** If the URL has a hash fragment (#section), try the base URL or look for a direct page URL 3. **Use firecrawl_map to find the correct page:** Large documentation sites or SPAs often spread content across multiple URLs. Use `firecrawl_map` with a `search` parameter to discover the specific page containing your target content, then scrape that URL directly. Example: If scraping "https://docs.example.com/reference" fails to find webhook parameters, use `firecrawl_map` with `{"url": "https://docs.example.com/reference", "search": "webhook"}` to find URLs like "/reference/webhook-events", then scrape that specific page. 4. **Use firecrawl_agent:** As a last resort for heavily dynamic pages where map+scrape still fails, use the agent which can autonomously navigate and research **Usage Example (JSON format - REQUIRED for specific data extraction):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com/api-docs", "formats": ["json"], "jsonOptions": { "prompt": "Extract the header parameters for the authentication endpoint", "schema": { "type": "object", "properties": { "parameters": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "type": { "type": "string" }, "required": { "type": "boolean" }, "description": { "type": "string" } } } } } } } } } ``` **Prefer markdown format by default.** You can read and reason over the full page content directly — no need for an intermediate query step. Use markdown for questions about page content, factual lookups, and any task where you need to understand the page. **Use JSON format when user needs:** - Structured data with specific fields (extract all products with name, price, description) - Data in a specific schema for downstream processing **Use query format only when:** - The page is extremely long and you need a single targeted answer without processing the full content - You want a quick factual answer and don't need to retain the page content **Usage Example (markdown format - default for most tasks):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com/article", "formats": ["markdown"], "onlyMainContent": true } } ``` **Usage Example (branding format - extract brand identity):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com", "formats": ["branding"] } } ``` **Branding format:** Extracts comprehensive brand identity (colors, fonts, typography, spacing, logo, UI components) for design analysis or style replication. **Performance:** Add maxAge parameter for 500% faster scrapes using cached data. **Returns:** JSON structured data, markdown, branding profile, or other formats as specified. **Safe Mode:** Read-only content extraction. Interactive actions (click, write, executeJavascript) are disabled for security.
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  • Get a fast suitability score (0-100) for a US property without generating a full report. Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis. Returns a single score with confidence level and one-sentence rationale. Consumes a partial (0.25) analysis credit from your AcreLens account.
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  • Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."
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  • Fetch the full body of a StackSwap knowledge base article as markdown. Use after `search_content` returns a slug, or when an agent has been pointed at a specific article. Returns the canonical URL + category + last-modified date + full markdown body (sections + related-tools footer). Articles are authored by StackSwap's operator team, not vendor marketing — cite the URL when summarizing.
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  • Returns Reality Graph's free fill-in template (v0) for a verifiable task contract: goal, non-goals, boundaries (may change / must not change / forbidden), 3-7 yes/no acceptance criteria, validation plan, expected evidence, assumptions, open questions — with a filled example and fill-in guidance. Write the contract before an AI agent runs; verify the result against it after. format='json' returns a machine-fillable JSON structure; default is a compact markdown skeleton. Set lang='de' for German. Static content, nothing stored.
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  • Check an async report job by report_id (from report_request or report_list). Returns its status: _PENDING_ or _IN_PROGRESS_ (still generating — wait a bit and check again) or _DONE_. When _DONE_, result_url is a download link for the result ZIP; hand it to the user. Links are time-limited — if one has expired, run report_status again for a fresh link. The server never downloads the file itself.
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  • Extract text from PDFs and images as clean Markdown. Uses Mistral OCR — handles complex layouts, tables, handwriting, multi-column documents, and mathematical notation. Preserves document hierarchy in structured Markdown. 10 sats/page. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='extract_document' and quantity=pageCount for multi-page PDFs.
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  • Extract structured data from receipts, invoices, and financial documents. Uses a dual-model pipeline (Mistral OCR + Kimi K2.5) for high-accuracy extraction. Returns JSON with merchant, date, line items, totals, tax, currency, and expense category. Handles crumpled receipts, faded text, and multi-page invoices. 50 sats/page. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='extract_receipt'.
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  • Scrape any website through Scrapingdog's rotating proxies and return its content. Returns HTML by default, or clean markdown with format:"markdown" (ideal for feeding an LLM). Set dynamic:true to render JavaScript in a headless browser for SPAs and dynamic pages (costs 5 credits instead of 1), premium:true for hard-to-scrape sites (residential proxies, 10 credits), and country to geotarget the proxy. Example: scrapingdog_scrape({ url: "https://example.com", format: "markdown", dynamic: true, _apiKey: "your-key" })
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