"Understanding RAG (Retrieval-Augmented Generation or related topics)" matching MCP tools:

• build_rag_prompt

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

  Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, system instruction injection, and source attribution.

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• ask_intelligence_rag

  Tresslers Group Intelligence Matrix

  Ask a natural-language question and receive structured intelligence context retrieved from Tresslers Group dossiers via RAG (Retrieval Augmented Generation). Returns relevant document chunks, source citations, conviction metadata, and graph neighborhood data. The calling LLM should synthesize the returned context into a coherent answer.

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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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• brand_guideline_status

  Colour Memory

  Resolves a brand_job_ref returned by brand_guideline_specify when its race-to-complete window elapsed before generation finished. Read-only, in-process lookup -- never re-runs generation. Returns {"status": "processing"} if still running, {"status": "complete", "brand_ref": ..., "project_id": ..., "recommended_candidate_id": ..., "candidate_count": ...} once done (a compact summary -- use the returned brand_ref with brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, the same pattern every other Brand Standard tool already uses), or {"status": "failed", "error_code": ..., "message": ...} if generation genuinely failed server-side. An unknown or expired brand_job_ref returns a structured BRAND_JOB_NOT_FOUND error, never a crash or empty success.

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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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• query_capacity_factor_v1

  osint

  Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.

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• brand_guideline_status

  Colour Memory

  Resolves a brand_job_ref returned by brand_guideline_specify when its race-to-complete window elapsed before generation finished. Read-only, in-process lookup -- never re-runs generation. Returns {"status": "processing"} if still running, {"status": "complete", "brand_ref": ..., "project_id": ..., "recommended_candidate_id": ..., "candidate_count": ...} once done (a compact summary -- use the returned brand_ref with brand_guideline_select/brand_guideline_pdf/brand_guideline_claims for full detail, the same pattern every other Brand Standard tool already uses), or {"status": "failed", "error_code": ..., "message": ...} if generation genuinely failed server-side. An unknown or expired brand_job_ref returns a structured BRAND_JOB_NOT_FOUND error, never a crash or empty success.

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• dataset.export

  FlexOrch MCP

  Download all records from a built dataset as text (Step 5 — final step). Returns the complete dataset content as a UTF-8 string directly in the response — no file download or separate URL needed. Call get_job_status after build_dataset and wait for status='completed' before calling this tool. Use the dataset_id from that completed response. Format guide: jsonl = LLM fine-tuning, rag = LangChain/LlamaIndex chunks, csv = spreadsheets, md = human-readable, xml = structured interchange. Binary formats (parquet, hf) cannot be returned via MCP — export them from the FlexOrch dashboard directly. Args: dataset_id: Dataset ID from the get_job_status completed build response. format: Text export format — jsonl, csv, json, md, xml, rag. Default: jsonl.

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• get_memories

  Dayze — Life in Days + Notable People

  Authenticated user memories from Dayze Agent. Pass query for semantic/keyword retrieval. Prefer search for event-first trip and calendar titles. Requires OAuth or a supported scoped credential. ($0.10; API key required)

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• lookup_sip_header

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Instant lookup of a SIP header by canonical or compact form (e.g. "Via" / "v", "Diversion", "P-Asserted-Identity", "Identity", "Session-Expires"). Returns canonical form, compact alias, RFC anchor, where it appears (request / response / both), cardinality (exactly-one / at-most-one / one-or-more / any), allowed/forbidden URI parameters with RFC citations, short description, and related headers. USE FIRST when the user asks about a specific header they saw in a trace - sub-millisecond, no API cost. The cardinality + paramRules fields surface failure modes (e.g. two From: headers, ;tag= on P-Asserted-Identity) without needing a RAG round-trip. Pair with: `lint_sip_request` to mechanically check a real request against these rules; `search_sip_docs` for vendor-specific or 3GPP P-headers not in the bundled registry.

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• agent_brief

  Colour Memory

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems. Archive-grounded retrieval is evidence-filtered: entries with claim_role='reject' (no primary source and no period connection), stub entries, blank-source entries, and entries below minimum_claim_strength are never selected. If fewer than palette_size colours pass these filters, the call returns an honest incomplete result (ok:false, error_code:INSUFFICIENT_EVIDENCE_ELIGIBLE_PALETTE) rather than padding the palette with rejected or weak evidence. Negative constraints (from 'avoid' or negation phrases in concept like 'must never', 'without', 'not') are also applied to retrieval, not just flagged afterward -- a brief that says a wedding must never feel funereal will not surface mourning-themed colours in the first place. locked_palette calls skip evidence filtering entirely since the caller is supplying colours directly, not requesting archive evidence.

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• pull_content

  Meta-Stamp Pockets

  Pull licensed creator content from a specific pocket by ID. Use this tool when an AI agent needs to retrieve verified, provenance-tracked content for generation, RAG, or training purposes. Do NOT use for browsing or discovery — use search_pockets or list_pockets instead. Requires a valid Bearer token for authentication; unauthenticated requests return HTTP 401. Successful pulls trigger a metered charge ($0.001–$0.25 depending on content tier) and the transaction is logged for creator royalty distribution. The pocket_id parameter is a 24-character hex string identifying the specific content pocket to pull from. Returns the full content payload with provenance metadata including creator attribution and license terms.

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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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• 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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• web_snapshot

  Robot Actions — Remote Device Control

  Accessibility tree of the DESKTOP grid browser page (by pageId), as text — for finding elements and understanding layout. Not a device: the equivalent for a phone or tablet is webpage_snapshot (by udid).

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• get_truss_information

  Truss

  Published Truss information by topic. Localized topics (overview, about, services, engagement, fit, faq) use locale, default en; pass he for Hebrew. Language-independent topics (identity, certifications, testimonials, clients, contact) ignore locale for content selection. Prefer get_truss_overview or topic overview for broad business understanding; prefer list_truss_services for the complete service catalog.

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• research_bulk

  TrendFlow

  Researches up to 10 topics in a single call, each with the same full picture as `research_trend`: interest over time, where it is most searched, and related queries. Each topic is looked up on its own scale, so they are not comparable to one another. Use this when you need data across many topics — a long or rich research pass — instead of one tool call per topic. Each section is fetched independently, so a partial result is normal: any section that fails carries an `error` instead of data and the rest still returns.

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• agent_brief

  Colour Memory

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems. Archive-grounded retrieval is evidence-filtered: entries with claim_role='reject' (no primary source and no period connection), stub entries, blank-source entries, and entries below minimum_claim_strength are never selected. If fewer than palette_size colours pass these filters, the call returns an honest incomplete result (ok:false, error_code:INSUFFICIENT_EVIDENCE_ELIGIBLE_PALETTE) rather than padding the palette with rejected or weak evidence. Negative constraints (from 'avoid' or negation phrases in concept like 'must never', 'without', 'not') are also applied to retrieval, not just flagged afterward -- a brief that says a wedding must never feel funereal will not surface mourning-themed colours in the first place. locked_palette calls skip evidence filtering entirely since the caller is supplying colours directly, not requesting archive evidence.

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• bulk_update_user_preferences

  Courier

  Additively create or update a user's preferences for one or more topics in a single request. Only the topics in the body are touched; existing overrides for other topics are left untouched. Partial-success: valid topics are written and returned in `items`, unapplicable ones collected in `errors`.

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