"RAG (Retrieval-Augmented Generation) MCP Integration for ChatGPT" matching MCP tools:

• ask_intelligence_ragA

  Tresslers Group Intelligence MatrixB

  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.

  ConnectorNo auth

• ask_intelligence_ragA

  Tresslers Intelligence MCP ServerA

  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.

  ConnectorNo auth

• build_rag_promptA

  IA-QA — 152 QA & Dev Tools for AI AgentsB

  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.

  ConnectorNo auth

• build_rag_promptA

  IA-QA Eval & RAG — 38 toolsA

  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.

  ConnectorNo auth

• rag_pipeline_planA

  Moretes Architect MCPB

  Plan a retrieval-augmented generation pipeline from corpus facts: chunking, embedding, indexing, authorization, evaluation and cost drivers, with open questions. No vendor is chosen for you.

  ConnectorNo auth

• rag_queryC

  Mazza Group AI APIsC

  Retrieval-augmented generation pipeline. Price: $1.50/call (x402, USDC on Base).

  ConnectorNo auth

• get_utility_service_statusA

  Ardaro Agent Runtime UtilitiesA

  Check the selected service's current runtime readiness, input limits, pricing and retention before use. Returns service and details; ready=false means the underlying service is unavailable. Free cached runtime read; no external probe, payment, analysis or historical document retrieval. Use get_utility_example for a fixed integration sample instead.

  ConnectorNo auth

• edit_design_htmlA

  UX PilotB

  Save full HTML that Claude, Cursor, ChatGPT, Codex, or another external AI edited for an existing UX Pilot design. Use patch_design_html instead for small deterministic edits such as text, class, padding, style, or attribute changes. Required workflow for full rewrites: first call get_design with includeHtml=true, edit the returned HTML outside UX Pilot, then call edit_design_html with the full replacement HTML. Defaults to mode='version' to create a child version preserving parentId history. Use mode='overwrite' only when the user wants to replace the existing design record directly. This does not invoke UX Pilot generation models and consumes no credits. Do not use import_html_design for editing an existing UX Pilot design.

  Connector

  Destructive

  OAuth

• engram_smarterA

  EngramA

  Brief a materially different task within the same session. Unlike engram_recall, this combines goal-focused memories with optional procedural checklists; unlike engram_get_context, it ranks evidence for the task instead of returning category samples. Returns Markdown with ranked workspace/public memory and retrieval hints. Use engram_start to begin work and engram_rehydrate for lost context. Example: {"repo":"engram","context":"Improve public-page delivery on Netlify while preserving localized SEO and onboarding."}. context states the whole goal; repo/file focus retrieval without reading local files. limit overrides depth; include_skills=false suppresses checklists. Records retrieval/session telemetry without editing knowledge. Consumes workspace daily call quota. OAuth: memory:read.

  ConnectorOAuth

• wiki_retrieveA

  DutifyA

  Search the wiki by MEANING and get back the most relevant PASSAGES (chunks) with citations and relevance scores. Use this to GROUND or ANSWER a question from wiki content: it returns quotable chunkText plus the source page/section so you can cite it. Each result has pageSlug, pageTitle, spaceKey, spaceName, chunkIndex, headingPath (heading-path breadcrumb), chunkText, score (0..1, higher = more relevant), status and updatedAt. This is retrieval-augmented generation (RAG) over wiki content, NOT a page finder — to merely LOCATE or LIST pages by title/keyword use wiki_search. Defaults to the top 5 chunks; pass limit to widen. Fails open to keyword retrieval if the embedding provider is unavailable. Page results include webUrl: a browser link requiring normal workspace access. Use it for citations; it does not make the page public. API-key scope: `wiki:search:read`.

  ConnectorNo auth

• wiki_retrieveA

  DutifyB

  Search the wiki by MEANING and get back the most relevant PASSAGES (chunks) with citations and relevance scores. Use this to GROUND or ANSWER a question from wiki content: it returns quotable chunkText plus the source page/section so you can cite it. Each result has pageSlug, pageTitle, spaceKey, spaceName, chunkIndex, headingPath (heading-path breadcrumb), chunkText, score (0..1, higher = more relevant), status and updatedAt. This is retrieval-augmented generation (RAG) over wiki content, NOT a page finder — to merely LOCATE or LIST pages by title/keyword use wiki_search. Defaults to the top 5 chunks; pass limit to widen. Fails open to keyword retrieval if the embedding provider is unavailable. Page results include webUrl: a browser link requiring normal workspace access. Use it for citations; it does not make the page public. API-key scope: `wiki:search:read`.

  ConnectorNo auth

• agent_briefA

  Colour MemoryA

  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.

  ConnectorNo auth

• agent_briefA

  Colour MemoryA

  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.

  ConnectorNo auth

• agent_briefA

  Colour MemoryA

  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.

  ConnectorNo auth

• get_generation_statusA

  Tailwind AppA

  Use this to poll a Pin generation started by generate_pins_from_url. Generation typically completes in 1-3 minutes: poll every ~15 seconds, and don't give up before ~30 minutes — retryable failures can hold processing for several minutes, and a hung generation can stay processing for ~25 minutes before the terminal generation-request-timed-out status appears. A 404 within ~10 seconds of starting the generation is a transient indexing artifact — retry it, don't stop. When status is success, call list_posts with status draft and filter by the matching generationId to find the generated Pins.

  ConnectorOAuth

• page_ragA

  FreeTier Agent APIB

  3 free MCP calls per day (all tools together), then $0.0025 USDC via x402 or card credits (paid in-band: params._meta["x402/payment"]). Page RAG: Any public web page -> clean LLM-ready Markdown + RAG chunks in one call: main content via Mozilla Readability (no nav/ads/footers), absolute links, title/description/language/canonical/published+modified dates/JSON-LD types, word + token estimates, sha256 content hash for change detection, and heading-aware chunks (configurable size + overlap, each with its heading path). Plain HTTP, robots.txt respected, Markdown/plain text passed through. Flags pages that likely need JavaScript. When to use: Use page-rag to feed a web page into an LLM or a vector store. Use page-meta or page-check for SEO/meta tags and HTTP status only. page-markdown is the older, simpler converter; page-rag is recommended.

  ConnectorNo auth

• troubleshoot_response_codeA

  SipflowA

  [cost: rag (one embed + one vector search) | read-only, network: outbound to embed model only | rate-limited per IP] Like `lookup_response_code` but augmented: returns the static RFC entry PLUS the top vendor-specific RAG hits for the exact code (and any free-text context the user pasted). When the static entry carries known vendor-specific reason-phrase variants (e.g. 484 + opensips → 'Invalid FROM' from `parse_from.c`), those phrases are folded into the embed query so the right vendor docs surface. Use when the user asks 'why did <vendor> reject this with <code>?' and you want vendor-grounded common causes, not just the RFC text. Especially helpful for fax-rejection paths - 488 / 415 / 606 on a T.38 reinvite (`m=image udptl t38`) is one of the most common 488 variants and the tool surfaces FreeSWITCH `mod_spandsp` / Cisco CUBE / AudioCodes T.38 docs alongside the RFC text. Pair with: `lookup_response_code` first (cheaper); `lint_sip_request` when the code is 4xx and they have the offending request; `compare_sdp_offer_answer` for 488/415 caused by a T.38 reinvite SDP mismatch; `validate_stir_shaken_identity` when the code is 438; `stir_attestation_explainer` for STIR-shaped codes (428/436/437/438/608); `dns_diagnose_sip_target` when the code is 503 / 408 and routing is suspect.

  ConnectorNo auth

• query_knowledgeA

  mcp-serverA

  Search the company's connected knowledge across every source — Drive, SharePoint, Confluence, Slack, Notion — with cited synthesized answers, lifecycle awareness, and refusal-on-weak-context. Returns a written answer with [n] citations plus the ranked source chunks. Modes: `fast` (1,500 kT — retrieval-only, no synthesis), `standard` (12,500 kT — default; synthesized answer over the top retrieval set), `deep` (25,000 kT — wider retrieval + premium synthesis for complex questions). Pick the cheapest tier that answers the question. Responses are capped at 25,000 output tokens per Claude Connectors policy; if truncated, structured metadata carries `truncated: true` and `query_id` so the agent can call `get_source_detail` for full provenance.

  ConnectorNo auth

• avizo_docsA

  avizo-mcpA

  Without arguments: lists every documentation page on avizo.ro/docs (quickstart, alert cadence, hierarchical escalation, SmartBill integration, API reference, troubleshooting) with slug and description. With {slug}: returns that page as markdown — the exact content the site renders. For programmatic integration read {slug: "api-reference"}.

  ConnectorNo auth

• list_integrationsA

  WebsitePublisher AIA

  List every integration for a project and whether it is configured (vault secrets present, ready to call) or still needs setup. Compact by default: id, name, category and endpoint names. For the input fields of one integration call get_integration_schema — that also returns its usage guidance. Use detail:true only when you really need every endpoint description of every integration (large response). Full catalog of every platform building block, no auth needed: https://www.websitepublisher.ai/integrations.txt.

  ConnectorAPI key

• adkit_helpA

  Ads Manager by AdKit: Google Ads, Meta Ads, TikTok Ads, LinkedIn AdsA

  Read-only reference documentation for the other AdKit tools: field names, accepted values, and per-platform limits. Sections: "manage" (campaigns, accounts, and reporting across Meta, Google, TikTok, Reddit, X, and LinkedIn, plus Microsoft and ChatGPT Ads raw access and drafts), "integrations" (read-only GA4 data), "library" (browse ads, advertisers, and inspiration boards), and "studio" (AI static ad generation). Open a section with path, for example adkit_help({ path: "manage google campaigns create" }), or search it with q, for example adkit_help({ q: "keyword planner" }). It runs nothing and changes nothing.

  ConnectorAPI key