"Tools for OCR and Analyzing Text and Content in Images" matching MCP tools:

• add_text

  cnvs.app

  Create a NEW text node, or update an existing one (pass the same `id` to overwrite content/position in place — preferred over creating a duplicate). Supports cnvs markup (Markdown-ish) and Mermaid diagrams in the content. When using Mermaid, the ENTIRE content of this text node must be a single Mermaid diagram (one ```mermaid fenced block and nothing else — no heading, no prose before or after). If you need prose + a diagram, create two separate text nodes. `postit: true` renders as a yellow sticky; `diagram: true` renders as a framed box (2px border in the text colour, centred text) — the two are mutually exclusive. Coordinates are in board-world pixels, +x right, +y DOWN; pick a spot that does not overlap existing items (check `get_preview` first). Default width auto-fits content up to ~320 px; pass `width` for explicit wrapping (160–4096). Keep content under 100 000 chars.

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• get_doc

  Dock

  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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• web_fetch

  dialogbrain

  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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• upload_file

  BorealHost

  Upload a base64-encoded file to a site's container. Use this for binary files (images, archives, fonts, etc.). For text files, prefer write_file(). Requires: API key with write scope. Args: slug: Site identifier path: Relative path including filename (e.g. "images/logo.png") content_b64: Base64-encoded file content Returns: {"success": true, "path": "images/logo.png", "size": 45678} Errors: VALIDATION_ERROR: Invalid base64 encoding FORBIDDEN: Protected system path

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• render_mermaid

  Diagrams MCP

  Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.

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• files_read

  DialogBrain

  Read **text content** of an attached file. Works for: .txt, .md, .json, code files, and PDFs (after files.ingest extracts text). DO NOT call on binary files — for IMAGES use `files.get_base64`, for AUDIO/VIDEO it cannot be transcribed via this tool, and for non-PDF DOCUMENTS run `files.ingest` first, THEN files.read. Calling on a binary mime-type returns an error — saves you a turn to read the routing hint before deciding.

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• web_fetch

  DialogBrain

  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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• deployments_upload

  ShipStatic

  Deploy a static site to a live URL — free, no account or API key required. **File content is plain text by default.** Pass HTML/CSS/JS/JSON/SVG/etc. directly in each file's `content` as a regular string. Only set `encoding: "base64"` per-file for binary content (images, fonts) — do not base64-encode text. Returns the live URL plus a claim URL (the site expires in 3 days unless claimed) — always show both to the user. To make the site private, pass `password`; always show the password to the user if you set one.

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• files_read

  dialogbrain

  Read **text content** of an attached file. Works for: .txt, .md, .json, code files, and PDFs (after files.ingest extracts text). DO NOT call on binary files — for IMAGES use `files.get_base64`, for AUDIO/VIDEO it cannot be transcribed via this tool, and for non-PDF DOCUMENTS run `files.ingest` first, THEN files.read. Calling on a binary mime-type returns an error — saves you a turn to read the routing hint before deciding.

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• set_board_mode

  cnvs.app

  Choose whether this board is a freeform whiteboard ('draw', the default) or a kanban task board ('todo'). Mode is switchable WHENEVER the board is empty of real content: drawings (text/strokes/images) and tasks. Empty or seeded columns DON'T count (switching to 'draw' clears them), so a cleared board can be switched again, and you can flip draw<->todo freely until the first stroke/text/image or task lands. Setting 'todo' auto-seeds three starter columns (To do / In progress / Done). Returns `{ mode, columns }`. Use the task/column tools (`create_task`, `create_column`, …) once the board is in 'todo' mode.

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• nps_get_park

  national-parks-mcp-server

  Full trip-planning detail for one or more parks by parkCode: description, activities and topics, entrance fees and passes, operating hours by area/season, contacts, directions, a free-text weather overview, representative images, and the NPS page for everything else. Get codes from nps_find_parks. Up to ten codes are fetched in a single request. Use the fields parameter to trim the payload when you only need certain sections.

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• analyze_image

  sheetsdata-mcp

  Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')

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• extract_document

  Sats4AI - Bitcoin-Powered AI Tools

  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_receipt

  Sats4AI - Bitcoin-Powered AI Tools

  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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• atlas_create_jd

  CareerProof MCP

  Create a job description from text within a hiring context. Returns a JD object with 'id' and stored content. Use JD content as jd_text in atlas_fit_match, atlas_fit_rank, atlas_start_jd_fit_batch, and atlas_start_jd_analysis. Requires context_id from atlas_create_context or atlas_list_contexts. Free.

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• get_asset

  mcp

  Read the full content of an existing asset so you can inspect it before patching. Text assets (js, css, json, svg, txt, html) are returned as UTF-8 strings in `content` with `encoding: "utf-8"`. Binary assets (images, fonts, pdf) are returned as base64 in `content` with `encoding: "base64"`, but only if size ≤ 1MB; above that the content is omitted and you should use the `url` field to download directly. Always returns `version_hash` (first 8 hex chars of the SHA-256 of the content) for optimistic concurrency in patch_asset.

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• receive_fax

  Sats4AI - Bitcoin-Powered AI Tools

  When you're expecting a fax back — bank confirmation, court filing, signed document — open a 24h receive window at our shared number +1 320 299 1523. Matched by caller ID (last 10 digits of the sender), delivered to your email as soon as it arrives. Optional OCR add-on (+200 sats) returns a searchable text file alongside the PDF — useful for feeding the content to an agent or archiving. Optional callback_url POSTs an HMAC-signed webhook on delivery so your agent doesn't have to poll. No refund if no fax arrives within the window (prevents subscription squatting). If OCR fails, an LNURL-withdraw for 200 sats is included in the delivery email for partial refund. Pay with Bitcoin Lightning — no dedicated fax number rental, no monthly subscription, no account.

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• generate_image

  switch

  Generate one or more Switch images. Auto-routes to the right model based on subject (Nano Banana 2 default, GPT Image 2 for swimwear/beach, Switch Model/Ultra/Pro for sexier content, Nano Banana Pro for typography-heavy). Counts <= 8 render inline in chat; counts > 8 queue to your Switch Studio with progress polling. All images persist to your Studio library and folder. Pass an optional `style` (e.g. "wellness/warm_amber_tropical", "high_fashion_editorial/testino_glossy", "movie_scene/neon_noir_action") to apply a curated photographic stack from the apply_* skill tools.

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• json-extract

  The Stall

  Extracts and parses JSON from mixed-content text. Handles LLM output with JSON embedded in prose, code fences (```json), trailing commas, single-quoted strings, JS-style comments, and bare object keys (JSON5-style). Returns the parsed data, a cleaned JSON string, extraction method used, and any repair applied. Pure text processing — zero external API calls.

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• get_audit_full

  mcp

  One call that returns a completed audit's SUMMARY, full per-section DETAIL, and deduplicated prioritized FIXES together — so you don't have to chain get_audit → get_audit_detail → get_fixes. Use `expand` to trim the payload ('summary' | 'detail' | 'fixes' | 'all'; default 'all'). Same ownership, tier gating, and sanitization as those tools. For an in-progress audit it returns the status so you can keep polling. The detail layer includes Wave-B surfaced sections (author/E-E-A-T, freshness, images, hreflang, per-bot access, Action microformats, OpenAPI sub-metrics, Google Intelligence, product readability, site maturity, methodology) and Wave-C deterministic signals (content quality, video/locale, USE sub-metrics, trust claims) each with an availability state.

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