"Software or tools to extract text from images on Windows" matching MCP tools:

• createImage

  Ludo AI Game Assets

  Generate game-art images from a text prompt alone, selecting an image_type (e.g. sprite) and optionally art_style, perspective, and aspect_ratio. Synchronous: the call blocks until generation finishes and returns an array of image results, each with a url; request n (1-8) to control how many variations come back. Because it generates purely from text it takes no source image, so there is no upload size limit to trip. Credits are charged only on success, scaled to the number of images produced. Use createImage to make new images from scratch; use generateWithStyle to match a reference image's art style, editImage to modify an existing image, and removeBackground to cut out a subject. Pass an optional request_id to tag the results so you can retrieve them later via getImageResults. Requires an API key (user scope). Credits: This endpoint consumes 0.5 credits per result.

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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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• editImage

  Ludo AI Game Assets

  Modify an existing image according to text instructions: supply a source image (URL or base64) and a prompt describing the changes (e.g. "add clouds", "warmer color scheme"), with an optional reference_image for extra style or content guidance. Synchronous: the call blocks and returns an array of image results, each with a url; request n (1-4) to control the number of edited variations. Provided images are uploaded and validated, and any image larger than 15MB is rejected with HTTP 400. Credits are charged only on success, scaled to the number of images produced. Use editImage to transform a specific existing image; use createImage to generate from text alone, generateWithStyle to borrow a reference's art style, and removeBackground for the dedicated background-removal case. Pass an optional request_id to tag the results so you can retrieve them later via getImageResults. Requires an API key (user scope). Credits: This endpoint consumes 0.5 credits per result.

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• ocr_image

  Toolora MCP Server

  Use this tool when the user shares an image that contains text they need extracted, read, or processed. Triggers: 'read the text in this image', 'extract text from this screenshot', 'what does this scanned page say', 'transcribe this handwritten note'. Accepts base64-encoded PNG/JPEG/WEBP/BMP/TIFF. Returns extracted text, confidence score, and word count. Prefer this over vision model text extraction for accuracy on scanned docs. Free, no API key, no signup; the image is processed in memory and never stored.

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• how_munimji_helps

  HelloBooks AI Agents

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• url.extract

  DocImprint

  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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• url.extract

  api

  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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• createVideoFromReferences

  Ludo AI Game Assets

  Generate a video from 1-5 reference images and a text prompt (references-to-video). Unlike createVideo, which animates a single source image, this composes a new scene that borrows characters, objects, and style from the reference images. Each image can be a URL or base64. Synchronous: the call blocks until rendering finishes and returns the video URL and its actual duration in seconds. Choose the output shape with `aspect_ratio` ("default" lets the model decide). The chosen `model` and `duration` must be compatible (incompatible combinations return HTTP 400). Credits are charged only on success, based on the produced duration and never more than the duration you requested. Pass an optional `request_id` to tag the result so you can locate it later via `getVideoResults`. Related tools: `createVideo` for image-to-video, `editVideo` to modify a generated video. Requires an API key (user scope). Credits: cost varies by model and duration (credits/sec): Eagle 1.5/s, Eagle with Audio 2/s; see this endpoint's full pricing table in the API docs.

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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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• extract

  Sofya

  Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 5 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")

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• pentest_lookup_group

  pentest-mcp-server

  Look up a MITRE ATT&CK threat group (intrusion set) or software entry by name or ID for authorized penetration testing and threat intelligence. Returns the group or software record: ATT&CK ID, display name, known aliases, type (group vs. software), description, and the techniques it uses with procedure-level context from public ATT&CK reporting. Accepts exact ATT&CK IDs (G0007 for threat groups, S0002 for software) or keyword/name search (e.g., "APT28", "Mimikatz", "Lazarus Group"). Equally useful for defenders building detection coverage around specific adversary tradecraft.

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• document.extract_structured

  api

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

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• extract_claims

  groundcheck

  PURPOSE: Split text into independently checkable ATOMIC factual claims — the cheap first step of a verification loop (extract -> ground -> attest). Returns {claims: [...], count, input_sha256} plus a signed receipt bound to the input hash. GUIDELINES: Call when you want to see WHICH claims a document makes before paying to ground them, to budget a verification pass (extract everything, then verify_claim only the claims that matter to your decision), or to prove later exactly which claims were pulled from exactly which text (the receipt binds both). Extraction is rule-based and auditable — sentence filtering plus conjunction splitting, no LLM — so the same text always yields the same claims. Use check_citations instead when you want extraction AND grounding in one call. PARAMETERS: text — the prose to decompose. max_claims — 1..50, default 20. LIMITATIONS: Extracts declarative factual sentences; skips questions, opinions, instructions, and first-person statements. Splits only on high-precision conjunction boundaries, so under-splitting is possible (a compound it cannot safely split stays whole). Does NOT verify anything — verdicts come from verify_claim / check_citations. Paid per call (x402), cheapest tool on this server. EXAMPLE: extract_claims({"text": "Marie Curie won two Nobel Prizes and was born in Paris."}) -> {count: 2, claims: ["Marie Curie won two Nobel Prizes", "was born in Paris."]}

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• niche_voice_profile_ingest

  Niche — Editorial Intelligence

  Extract voice primitives (register / sentence rhythm / lexicon preferences / punctuation habits) from post-shaped text and persist onto the user's VoiceProfile. The voice primitives thread into content generation so generated copy matches the user's actual writing voice. Two input shapes: pass `posts` (list of pre-collected text snippets, ≥80 chars each) or pass `url` (the server scrapes post-shaped snippets from the page: Substack / Medium / blog / X profile). Inline posts win when both are given. Inline post-shaped snippets need to be the user's own writing, not press articles or marketing copy. Returns the extracted primitives + a diff of what changed on the stored VoiceProfile.

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• ocr_image

  Toolora

  Use this tool when the user shares an image that contains text they need extracted, read, or processed. Triggers: 'read the text in this image', 'extract text from this screenshot', 'what does this scanned page say', 'transcribe this handwritten note'. Accepts base64-encoded PNG/JPEG/WEBP/BMP/TIFF. Returns extracted text, confidence score, and word count. Prefer this over vision model text extraction for accuracy on scanned docs. Free, no API key, no signup; the image is processed in memory and never stored.

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• editVideo

  Ludo AI Game Assets

  Edit a previously generated video with a text prompt and optional reference images (video-to-video). Pass the video `url` you received from `createVideo`, `createVideoFromReferences`, or an earlier edit — it must be a video you generated within the last 7 days; arbitrary external videos are not accepted. Optionally add up to 5 reference `images` (URL or base64) to guide the edit. Synchronous: the call blocks until rendering finishes and returns the new video URL and its actual duration in seconds. Credits are charged only on success, based on the produced duration and never more than the duration you requested. Pass an optional `request_id` to tag the result so you can locate it later via `getVideoResults`. Related tools: `createVideo` to generate the source clip, `createVideoFromReferences` for reference-driven generation. Requires an API key (user scope). Credits: cost varies by model and duration (credits/sec): Eagle 2/s; see this endpoint's full pricing table in the API docs.

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• get_full_text

  Pubmed

  Fetch the FULL TEXT of a biomedical paper from PubMed Central (the open-access subset) by PubMed ID. PREFER OVER get_abstract when you need methods/results/discussion, not just the abstract — "read the full paper", "what methods did <PMID> use", "extract details from the paper". Resolves the PMID to its PMC id and returns the article body text (capped ~40k chars). Only open-access articles are in PMC — returns has_full_text:false (use get_abstract) otherwise.

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• chrome_read_tab

  local-mcp

  Reads the rendered text content of a Google Chrome tab. Identify the tab either by `url_match` (substring match against URL; first hit wins) or by `window_index` + `tab_index` (from chrome_list_tabs). Text is capped at `max_bytes` (default 100 KB). Pass `include_html: true` to also get the raw HTML source. Pass `include_links: true` to extract all links with their href and text. Requires 'Allow JavaScript from Apple Events' (Chrome → View → Developer); run chrome_setup_check if reads come back empty.

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• onedrive_read_file

  local-mcp

  Reads a text file from your OneDrive synced folder. Supports .txt, .md, .csv, .json, .xml, .log and several code file types. Auto-detects UTF-8, falls back to Latin-1/Windows-1252 for legacy files (common in Latin American banking .TXT padrones). For files elsewhere on this Mac, use file_read.

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• clipform_search_media

  Clipform

  Search images or stock video clips. Pass one query or many (max 10) - multiple queries run in one call instead of separate tool calls. Use results to feed into clipform_generate_video for narrated slideshow videos, or upload directly as still images via clipform_upload_media_asset then clipform_attach_node_media. All results are pre-cleared for commercial use. Results include a description (alt text where the provider has it) - use it to pick visually distinct images. Example: { queries: [{ query: "saturn rings" }, { query: "mars surface", count: 3 }] } returns portrait images for both.

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