"Slack Bot with RAG Document Retrieval and Automated Response System" matching MCP tools:

• word_create

  local-mcp

  Creates a new Word (.docx) document at `path` with the given text content (and an optional title rendered as the heading). Requires confirm=true — called without it, returns a preview of what will be written instead of creating the file. The path must be somewhere Local MCP can write; Desktop/Documents/Downloads may need a one-time Files-and-Folders grant (System Settings → Privacy & Security → Files and Folders). Returns {created, path}. For a OneDrive or Google Drive path use onedrive_write_file / gdrive_write_file; to append to an existing doc use word_append, to read one word_read.

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• collection.ask

  DocImprint

  Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use collection.search instead. For single-document Q&A, use url.qa instead. PREREQUISITE: Collection must be populated via collection.add_document and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."

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• validate_portfolio

  aibvf-mcp

  Check whether a supplied AI BVF v1.0 portfolio document has the shape the portfolio tools require, before scoring, sequencing, storing or sharing it. CALL THIS when the document came from a file, another system or hand-built JSON and its structure is uncertain. It checks required fields, taxonomy values and 0–100 pillar ranges only; it does not judge the evidence or calculate a verdict. Pillars may be bare numbers or { value, confidence } objects, both are valid. Use assemble_portfolio when the user has a list of initiatives in conversation and needs the document built for them, score_portfolio when the document is already ready for verdicts, and sequence_portfolio only after its initiatives are scoreable. Returns valid=true or one error per failing JSON path. Pure deterministic validation — no network, auth, or side effects.

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• submit_response

  Ansvar: EU Compliance & Legal Intelligence

  Provide an answer to the current step in a compliance workflow. Use this when someone provides information requested by the workflow, such as 'our system processes health data' or 'we use AES-256 encryption'. The workflow engine validates the response and advances to the next step. Pass user_acknowledged=true only after the user has supplied the fields listed in user_provided_fields. evidence_references accepts document UUIDs, doc:// segment URIs, or regulatory URLs. For an unattended gate, pass approved_by='auto' and leave user_acknowledged=false so the report does not misrepresent automation as human review. approved_by accepts only 'auto'; human review is asserted via user_acknowledged, never by naming an approver.

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• purchase_enterprise_license

  Opedd — Licensed Content for AI

  Purchase a bulk enterprise license covering multiple publishers (Phase 10). Returns a Stripe client_secret for payment completion + the enterprise_license_id. After payment, an ent_* access key is emailed to buyer_email. Scopes: 'custom' (pass-through publisher_ids), 'platform_wide' (auto-resolve all opted-in publishers), 'filtered' (Phase 10 filter_rules). License tiers: 'rag' (= ai_retrieval), 'training' (= ai_training, flat-fee not metered), 'inference' (= ai_retrieval), 'full_ai' (writes both retrieval + training records). The buyer must accept the Opedd Master Services Agreement (opedd.com/terms) before purchase — set terms_accepted=true to record it.

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• chieflab_use_manual_fallback

  ChiefLab

  USE WHEN any launch action carries a manualFallback brief — either because the channel has no automated provider at all (Product Hunt, Hacker News, Reddit, Discord, Slack, Indie Hackers, dev.to, YC Bookface, blog) OR because the channel HAS an automated provider but its connector isn't wired yet for this workspace (LinkedIn / X without a Zernio key; email without a Resend key + verified sender domain). Pattern: use the inline fallback NOW to ship the launch manually; connect the provider LATER (each fallback returns an upgradePath with the exact connector wiring) so the same action publishes automatically on the next launch. Returns: paste-ready title + body, per-channel checklist (best time, format, first-comment script), capture-URL-after instruction, measurement template for the 24h readback, and the optional upgradePath when automation is available. Converts a 'blocked' channel into a 5-minute human-in-the-loop ship. Approval-gated upstream: the originating publishAction must already be 'approved' on the reviewUrl before this tool returns the brief.

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• ic_research_ask

  Immersive Commons

  Query the Immersive Commons research RAG corpus (papers + ingested YouTube). Returns top-k chunks with similarity scores and source links. The query text is forwarded to a server-side RAG proxy (supercommons2 via Tailnet Funnel) and NEVER logged on the IC side — privacy contract. Use this for literature lookups, finding related work, surfacing citations the floor has already ingested. Args: { question: string (<=500 chars), k?: number (1-50, default 10), sources?: ('paper'|'book')[] (default ['paper']) }. Returns the upstream RAG response shape — typically { results: [{ paper_id, title, similarity, snippet, link }, ...] }. Required scope: research:query.

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• get_document

  OpenArx

  Retrieve full paper details by ID. Default returns metadata only (title, authors, abstract, license, codeLinks counts) — use includeChunks=true to fetch chunk content. For specific sections or content types, use chunkContentTypes/section filters or call get_chunks instead. For long papers, prefer filtered chunk retrieval over full chunks dump. AVAILABILITY is two INDEPENDENT axes: indexingTier (none|abstract_only|full|reindexing) = whether the full text is indexed and readable via get_chunks — 'reindexing' means the document is being re-processed right now and its currently indexed chunks are STALE: do not quote them as the body and do not treat the document as abstract_only either, its state is not yet known (chunkCount shows how many); sourceAccessibility (served_by_us|external_link_only|unavailable) = how to obtain the raw source file, with sourceUrl returned whenever known. To read content: if indexingTier='full' use get_chunks; else if sourceAccessibility!='unavailable' fetch sourceUrl yourself; only 'unavailable' means no full text. canServeFile is DEPRECATED — it gates raw-PDF delivery ONLY and is NOT a content-availability signal; use indexingTier + sourceAccessibility. An identifier resolves to one specific version of a document, not to a mutable current state, so a reference cannot silently come to mean different text.

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• medcode_check_code

  medical-codes-mcp-server

  Validate whether a US medical code exists, is current, and is billable in the active bundled release. Returns a discriminated status — valid_billable, valid_not_billable, valid_header, or terminated — with a `whyNot` explaining non-billable and terminated cases (e.g. "valid ICD-10-CM category but not billable — submit a more specific child code"). This is the detail a coder needs before submitting a claim. Auto-detects the system from the code's shape; pass an explicit `system` to disambiguate. A non-billable or terminated code is a successful result with a whyNot, not an error — only a code that exists in no bundled system raises unknown_code. A code string that also exists in another bundled system carries `alsoInSystems` naming it, since the verdict applies only to the system that answered.

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• hallucination_check

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Word-overlap based hallucination check: verifies if an LLM answer's words and numbers appear in the provided source/context. Fast, deterministic, no API key needed. Limitations: not semantic — does not understand synonyms or paraphrases. For true semantic grounding, use run_semantic_tests with embedding mode. Essential for quick RAG accuracy testing.

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• get_store_template_details

  agentView

  Returns full details of one store template: localized title and description, long-form markdown, category, suite, tags, features, preview image and agentArtifacts (bot-onboarding files such as system prompts, Agent Skills SKILL.md, MCP config). Use after search_store_templates before recommending or installing; when agentArtifacts is non-empty, fetch bodies via get_agent_artifact. No authentication required.

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• collection.create

  DocImprint

  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 (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. 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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• collection.ask

  api

  Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use collection.search instead. For single-document Q&A, use url.qa instead. PREREQUISITE: Collection must be populated via collection.add_document and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."

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• ic_health

  Immersive Commons

  Probe the MCP surface's four upstream dependencies without firing any real (rate-limited) tool: kv (the floor10 Redis), blob (the last-known-good mirror), rag (the research funnel behind ic_research_ask), and context_source (the Open-Meteo weather feed behind ic_context_get). Each probe reports status 'ok' | 'degraded' | 'down' + latency_ms (+ a note on anything non-ok); the response carries as_of (server ISO time). Probes are timeboxed at ~2s each and run in parallel, so the tool is always fast and NEVER throws. Available to any valid token — no extra scope. Args: none. Returns: { kv, blob, rag, context_source, as_of }.

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• docraptor_create_document

  docraptor

  Generate a PDF or Excel document from HTML (document_content) or a URL (document_url). Exactly one of document_content / document_url is required. By default the document is HOSTED and the tool returns a { download_url } you can fetch — ideal for agents (no large binary in the response). Set hosted:false to get the raw document back as base64, or async:true to enqueue a job and poll docraptor_get_document_status. IMPORTANT: real documents consume account credits (billed). Set test:true to generate a FREE, watermarked document while developing. DocRaptor API: POST /docs.

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• avm_value

  realtystack-mcp

  Run RentCast's automated valuation model for a subject property and return a point price estimate, a priceRangeLow/priceRangeHigh band, the resolved subjectProperty, and comparables[] — recent comparable sales each scored with a distance (miles) and correlation (0-1 similarity). Pin the subject with either address or latitude+longitude; when subject attributes are unknown, supply propertyType/bedrooms/bathrooms/squareFootage (or leave lookupSubjectAttributes true to have RentCast fill them). Cached 6h upstream.

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• search_uwear_library

  Uwear

  Universal hybrid retrieval across the user's visible Uwear library: garments, avatars/models, locations, ArtDirections, uploaded files, and generation results. Use this before opening the picker when the user describes assets or saved creative direction by exact name/SKU or natural language, e.g. 'SKU 42', 'urban art direction', 'summer denim', or 'studio model'. For saved outfits, retrieve matching garments first, then call list_outfits with clothing_item_ids or propose_outfits from the garment IDs. Returns stable typed IDs, ids_by_type, detail_tool/detail_arguments, and selection hints; for saved ArtDirections, use the returned art_direction_id in briefs. This combines indexed lexical matching with vector retrieval; do not run separate substring searches.

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• create_contact

  FlowCastle

  Create a contact manually — for imports or externally-sourced audiences; contacts who message a bot are created automatically. Requires the manage_broadcasts permission. platformId must be unique within the bot (duplicate fails with 409); botId may be omitted only when the application has exactly one bot. The variables map takes variable NAMES (or full folder paths when a name is ambiguous) — not ids — and unknown names fail with 422. NOT idempotent: retrying a success creates nothing new only because the duplicate platformId is rejected.

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• list_notification_channels

  Uptimepage

  The org's notification channels: id, operator-set name, kind (email, slack, telegram, webhook, and so on), and whether the channel is enabled. Two flags say a channel is not working even where it reads as ready: awaiting_verification for an email address nobody confirmed, and not_delivering for an enabled channel whose recent alerts all failed to arrive. Channel settings are withheld, since they hold webhook URLs and bot tokens. Channels are created in the Uptimepage app, not here. Read-only.

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• chieflab_use_manual_fallback

  ChiefLab

  USE WHEN any launch action carries a manualFallback brief — either because the channel has no automated provider at all (Product Hunt, Hacker News, Reddit, Discord, Slack, Indie Hackers, dev.to, YC Bookface, blog) OR because the channel HAS an automated provider but its connector isn't wired yet for this workspace (LinkedIn / X without a Zernio key; email without a Resend key + verified sender domain). Pattern: use the inline fallback NOW to ship the launch manually; connect the provider LATER (each fallback returns an upgradePath with the exact connector wiring) so the same action publishes automatically on the next launch. Returns: paste-ready title + body, per-channel checklist (best time, format, first-comment script), capture-URL-after instruction, measurement template for the 24h readback, and the optional upgradePath when automation is available. Converts a 'blocked' channel into a 5-minute human-in-the-loop ship. Approval-gated upstream: the originating publishAction must already be 'approved' on the reviewUrl before this tool returns the brief.

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