"Notes or documentation related to 'rag'" matching MCP tools:

• upsert_note

  OpenAkashic

  Create or overwrite an OpenAkashic markdown note. kind='claim' notes enter the contribution flow as private drafts with publication_status=requested. Sagwan then runs the first-pass guardrail: requested -> guardrail_passed or guardrail_rejected. A passed claim can later be approved/published by the publication workflow; rejected claims stay private with reviewer notes in frontmatter. Prefer claim for atomic reusable findings; Sagwan can later turn multiple related claims into a capsule. kind='capsule' notes stay private until you request publication review. Other kinds (playbook, concept, etc.) remain Closed-only working memory. Writable roots: personal_vault/, doc/, assets/ only. Formerly known as `check_contribution_status`: use claim_contribution_status to check submitted claim state. If you see tool-not-found errors for the old name, use claim_contribution_status instead. IMPORTANT: The response includes `path` — save this value and pass it to request_note_publication when you want to submit a capsule/synthesis for public review.

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• knowledge_query

  dialogbrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

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• create_collection

  api

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (search_collection) or question answering (ask_collection). NOTE: Collections start empty. Add evidence bundles with add_document_to_collection. Indexing is async — once complete, use search_collection or ask_collection. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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• moxie.search_docs

  Moxie Docs

  Keyword and semantic search across the connected repository's generated docs, conventions, documentation gaps, AI-context notes, and indexed code. Read-only; no side effects. Returns ranked matches in Markdown grouped into Documentation and Code sections, each with a title, snippet, and source paths. Use for open-ended lookups when you don't know which category holds the answer; when you do, the specific getters (get_conventions, get_doc_gaps, get_documentation_opportunities) are more direct. Omitting query returns recent context instead.

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• fetch_notes

  Sweeppea MCP

  Get all notes for your account. Notes are automatically decrypted and returned in reverse chronological order. Use them internally for tool chaining but present only human-readable information (titles, content, dates). # fetch_notes ## When to use Get all notes for your account. Notes are automatically decrypted and returned in reverse chronological order. Use them internally for tool chaining but present only human-readable information (titles, content, dates).

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• microsoft_docs_fetch

  xpay✦ DevTools Collection

  Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

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• search_notes

  anamnese

  Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.

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• ApolloDocsRead

  GraphOS MCP Tools

  Fetches the complete markdown content of an Apollo documentation page using its slug, or everything after https://apollographql.com/docs. Documentation slugs can be obtained from the SearchDocs tool results. Use this after ApolloDocsSearch to read full pages rather than just excerpts. Content will be given in chunks with the totalCount field specifying the total number of chunks. Start with a chunkIndex of 0 and fetch each chunk.

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• notes-delete

  hjarni

  Move a note to Trash. This is RECOVERABLE — the note (with its body, attachments, and history) is restorable with notes-restore until its purge date (default 30 days); it is not an immediate permanent erase. Deleting the wrong note can be undone with notes-restore. Works on your own personal notes and on team notes where you have the editor role. You cannot delete notes in a shared container (only the owner can). Required: id (integer).

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• files-attach_from_url

  hjarni

  Fetch a file from a public URL and attach it to one of your personal notes (personal notes only; for team or shared notes use files-create_upload_url). Follows one redirect. Required: note_id (integer), url (string). Optional: filename (default: derived from URL), content_type (default: from HTTP response), description.

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• list_docs

  Pine Script

  USE WHEN discovering what Pine Script v6 documentation is available. Returns a categorised list of doc file paths with one-line descriptions. AFTER calling this tool, call get_doc(path) for small files or list_sections(path) then get_section(path, header) for large files (ta.md, strategy.md, collections.md, drawing.md, general.md). Data sourced from bundled Pine Script v6 documentation.

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• notes_recall

  dialogbrain

  Recall notes from your notebook. By default returns only your own notes (all scopes, newest first). Pass filter_agent_id=<int> to read another agent's notebook, or filter_agent_id="all" (or "*") to read across every agent in the workspace. Pass scope to narrow to global/thread/person. Each result includes agent_id and agent_name of the author.

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• rendex_extract

  Rendex: Rendering API for Images, PDFs & Content Extraction

  Use this when the user asks to read, extract, get the text/content/article of, or summarize a webpage/URL. Do NOT use for a visual screenshot (use rendex_screenshot). Extracts clean reader-mode content from any webpage as Markdown, JSON, or HTML. Runs the same Chromium render pass as a screenshot, so it captures content after JavaScript runs — handles SPAs that fetch-only readers miss. Strips nav, ads, and boilerplate, returning the article body plus title, byline, and excerpt. Great for feeding page content to an LLM, summarization, or RAG ingestion. Costs 1 render credit per call.

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• get_fonto_page

  Fonto Docs

  Fetch the full content of a Fonto documentation page by its slug (the part of the URL after /latest/). Use search_fonto_docs or list_pages first to find the right slug.

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• moxie.get_doc_impact

  Moxie Docs

  Given the file paths an agent is about to change (and optionally a subset being deleted), return the conventions, documentation gaps, and existing/related docs whose evidence overlaps those paths, plus a net-new/undocumented analysis and any removal candidates. Read-only; no side effects. Returns a Markdown report. Call this BEFORE writing code so doc updates land in the same PR; then use propose_doc_update to write a doc, or propose_doc_removal for an orphaned one.

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• create_note_event

  STAS Running Coach for Claude

  Create or update NOTE events in Intervals. dry_run is required: false writes the note, true previews only. Send category=NOTE and external_id=note:YYYY-MM-DD:<slug>. Use all-day local times for normal notes, keep description short, and omit type, moving_time, icu_training_load, and workout_doc. For weekly review notes or other notes that apply to the whole week, send for_week=true; omit it or use false for ordinary notes. Do not create a seven-day date range for weekly notes; keep one all-day anchor date and use for_week=true.

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• knowledge_query

  DialogBrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

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• notes_search

  DialogBrain

  Full-text search in your notebook. By default searches only your own notes. Pass filter_agent_id=<int> to search another agent's notebook, or "all" (or "*") for workspace-wide. Or list all notes for a person/thread by scope_ref_id.

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• markdown

  mercury-x402-mcp

  URL → clean, LLM-ready markdown (boilerplate/nav/ads stripped, headings + lists + links preserved) with a signed provenance receipt pinning the markdown to its source — the RAG-ingest primitive. Deterministic (no LLM): same URL + same source bytes ⇒ byte-identical markdown. — $0.005/call

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• document-qa-prep

  The Stall

  Prepares a document for question-answering and RAG pipelines. Chunks the input text at paragraph/sentence boundaries, assigns deterministic chunk IDs, estimates token counts, and extracts document metadata (word count, type, headings). Returns ready-to-embed chunks with overlap support. No LLM or external API — pure text processing. Use mid-task when you've fetched a document and need it split before querying a vector store.

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