"Sublime Text" matching MCP tools:

• scholarfetch_author_papers

  ScholarFetch

  Expand one author into a deduplicated paper list. This is the main author->paper traversal tool and supports research filters. Use `author_id` when you already know the exact author, or `author_name` plus `candidate_index` after `scholarfetch_author_candidates`. Supported comma-separated `filters`: year>=YYYY, year<=YYYY, year=YYYY, has:abstract, has:doi, has:pdf, venue:<text>, title:<text>, doi:<text>. If you pass `engines`, it must include `openalex`.

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• accessibility_font

  Colour Memory

  Given a background hex and a palette of candidate foreground colours, return them ranked by contrast ratio with WCAG grades and specific recommendations for body text, large text, and UI components.

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• extract_url

  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. Use summarize_url for summaries, qa_url for Q&A, translate_url for translation. Returns: { url, title, word_count, text, final_url (after redirects) }

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• parliament_member_debates

  UK Legal Research

  USE THIS TOOL WHEN you have a member_id and want contributions where THAT member used a specific topic phrase verbatim (text-body search). CALL parliament_find_member(name) FIRST to obtain the integer member_id. This is a name-based text-body search — it matches contributions whose TEXT contains the topic phrase. A member who spoke in a debate but didn't use your phrase verbatim is filtered out. For verbatim retrieval of every contribution by a member in a known debate (regardless of vocabulary), use parliament_get_debate_contributions(debate_ext_id, member_id=...) instead. Each contribution's text field is capped at 3000 characters.

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• accessibility_rules

  Colour Memory

  Convert a palette WCAG matrix into actionable design-system rules. Returns safe pairs, AA-only pairs, large-text-only pairs, decorative-only pairs, best text colour per background, and component usage rules. Deterministic, no LLM cost.

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• extract_pdf

  paper-mcp

  Extract a PDF to clean Markdown/LaTeX text via MinerU (great for papers behind no open-access full text — give the user's PDF and get readable text back). Provide pdf_url (downloaded server-side, SSRF-guarded) OR pdf_base64. formula/table toggle math/table reconstruction. Returns {task_id, status, cached, content, chars}: a recently-seen (cached) or small PDF comes back with `content` in one call; a fresh PDF (MinerU is GPU-heavy, minutes) returns status='running' + a task_id — then call extract_pdf_result(task_id) to fetch the text.

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• law_parliament_member_debates

  UK Business Tools - Ledgerhall

  USE THIS TOOL WHEN you have a member_id and want contributions where THAT member used a specific topic phrase verbatim (text-body search). CALL parliament_find_member(name) FIRST to obtain the integer member_id. This is a name-based text-body search — it matches contributions whose TEXT contains the topic phrase. A member who spoke in a debate but didn't use your phrase verbatim is filtered out. For verbatim retrieval of every contribution by a member in a known debate (regardless of vocabulary), use parliament_get_debate_contributions(debate_ext_id, member_id=...) instead. Each contribution's text field is capped at 3000 characters.

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• summarize_document

  api

  Summarize document text into a prose summary and key points. Typical workflow: call extract_text or extract_url first, then pass the text here. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" }

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• extract_tables

  DocImprint

  Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data. For plain prose, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables.

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• put_notification_content

  Courier

  Replace the elemental content of a V2 notification template. Overwrites all elements. Use channel elements to target specific channels. Multi-channel example: elements: [{ type: "channel", channel: "email", elements: [{ type: "meta", title: "Hello" }, { type: "text", content: "Email body" }] }, { type: "channel", channel: "push", elements: [{ type: "meta", title: "Hello" }, { type: "text", content: "Push body" }] }, { type: "channel", channel: "inbox", elements: [{ type: "text", content: "Inbox plain text only" }] }].

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• lookup_hash

  GoCreative Agent API

  Hash a string (md5/sha1/sha256). Pass ?text=...&algo=... as query. Use for checksum and integrity agents. Example call: {"query_string": "text=hello&algo=sha256"} Cost: $0.005–$0.05 USDC on Base per call.

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• hires_get_candidate_resume

  100Hires - AI ATS & Recruitment Software

  Get the primary resume for a candidate. Returns uuid, absolute download url (use Bearer auth), relative_time, file metadata, type. Use include='text_content' to also get the parsed plain-text content in a `text` field without downloading the file.

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• chunk_text

  Toolora MCP Server

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

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• extract_url

  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. Use summarize_url for summaries, qa_url for Q&A, translate_url for translation. Returns: { url, title, word_count, text, final_url (after redirects) }

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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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• chunk_text

  Toolora

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

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• get_blog_post

  mcp

  Retrieve the full text of a single Christian Perez blog post by slug. Returns title, publication date, tags, series, excerpt, and the full post body as plain text. Use search_blog first if you don't already know the slug.

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• base64

  Microapp

  Encode plain text to base64 or decode base64 back to text. Use mode='encode' for plain→base64, mode='decode' for base64→plain. UTF-8 safe.

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• read_brand_document

  Heista

  Read one indexed brand document. Returns the indexed metadata (doc_type, summary, key_topics, entities, key_quotes) plus the document content, routed by mime: text/markdown, text/plain, text/csv are inlined as text; application/pdf and other text-shaped mimes return an Anthropic Files API file_id (attach via document source { type:"file", file_id } on the next turn); DOCX/PPTX/XLSX return a requires_code_execution marker (caller must enable code_execution_20260120 and attach via container_upload). Use AFTER list_brand_documents to pick the right document. Free, read-only.

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• box_get_file_text

  Box

  Download and return the text content of a Box (cloud storage) file by its file id. Best for plain-text, Markdown, CSV, and other text documents; binary formats (Office docs, PDFs, images) will return unreadable bytes. Content is capped at ~100,000 characters and flagged when truncated.

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