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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681,338 tools. Updated 2026-10-20 13:52
"Kotlin RAG (Retrieval-Augmented Generation) implementation resources" matching MCP tools:
- 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 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
- Retrieval-augmented generation pipeline. Price: $1.50/call (x402, USDC on Base).ConnectorNo auth
- Use this when the user wants to archive, hide, or remove an existing generation from the active generation list without deleting it. State-changing: archives the visible generation after authorization. Requires generation_id from list-generations.ConnectorOAuth
- Call when an agent needs readable main content from a public web page as markdown plus title/description. Use for summarization, RAG ingest, or quoting without a browser. Fetches once; blocks private/SSRF targets. Not for authenticated, paywalled, or non-HTML resources. Price: $0.05 USDC per call (x402 on Base) or 1 prepaid pack credit. Failed calls aren't charged when you pay per call with x402: if the job errors, the USDC payment is never settled. With pack credits (X-Entitlement-Key), each call uses one credit, even a call that fails.ConnectorNo auth
Matching MCP Servers
- AlicenseBqualityDmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.124MIT
- AlicenseBqualityDmaintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.1Apache 2.0
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Search public Australian environmental evidence with provenance across authoritative catalogues.
- 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
- Find working SOURCE CODE examples from 53 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs, .ts, .js) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C# (official SDKs) plus Rust and TypeScript/Node.js (community-maintained wrappers, not official) SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. File retrieval returns the file's location instead of its content: follow the returned `resources` / `access_steps` exactly as given.ConnectorNo auth
- 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
- 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
- [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 the IA-QA methodology knowledge base. Returns structured testing guidelines, assertion strategies, thresholds, best practices, and relevant MCP tools for a given topic. Call without a topic to list all available topics. Topics: llm-unit-testing, rag-pipeline, prompt-stability, prompt-ab-testing, embedding-quality, eval-framework, semantic-testing, auto-testing, security, api-testing, ci-cd, multimodal, llm-data-security, agent-observability, pro-tips, learning-paths, golden-dataset, selector-drift, qa-recipes, playbooks. Not sure where to start testing an LLM, RAG pipeline or agent? Call without a topic (or with "start-here"): it maps what you are testing to the tools to call and the output field to gate CI on. A plain question such as "how do I test my RAG" also resolves to the right topic.ConnectorNo auth
- List a model provider's voices so you can pass the right id to a generation tool. Default 'elevenlabs-voices' (the voices for generate_audio); also covers Kling and Google Veo voices. Returns each voice's id and name. (This is Unifically's resource catalog; it is not the MCP protocol's resources/list.)ConnectorNo auth
- Fetch up to 10 public URLs and return each as clean Markdown, in one call — for research/RAG over several pages at once. Private/internal hosts are blocked.ConnectorNo auth
- Returns public saved recordings whose saved Bible annotations exactly overlap the requested single-chapter verse range. scope=sermon excludes reading-only annotations; scope=session includes other liturgy annotations. Results use live lexicographic session_id order rather than chronology. Matches are stored annotations rather than semantic search or verified evidence. The indexed scan covers at most 200 public chapter candidates per request. No content generation, Bible-text retrieval, or network access occurs. Each page consumes the shared listing quota.ConnectorNo auth
- Returns the guide (Markdown) for making a playable demo of an app that can't run in a browser: native Swift/Kotlin apps, or apps that need a server, login or hardware. No arguments, no auth. Call it before publish_app for such apps.ConnectorNo auth
- Returns turva.dev's service catalog: the Shopify agent storefront check, agent-readiness audit, advisory, implementation, agent operations, and MCP server design, plus the engagement model and pricing (fixed list prices for the Shopify agent storefront check, audit, advisory and implementation; agent operations and MCP server design on request), and two implementation add-ons that carry a fixed price and are sold only with the diagnosis they follow, bought with it, with its report, or after the report and before implementation starts. Use this when a user asks what turva.dev offers, what it costs, or how an engagement works. For how to reach turva.dev use get_contact instead, and for the rules an engagement follows use get_principles. Read-only: returns static JSON that is compiled into the Worker, so it changes nothing and updates only on deploy.ConnectorNo auth
- Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.ConnectorNo auth
- Ask a natural-language question and get a synthesized answer grounded in your analyzed media (retrieval-augmented generation over transcripts, summaries, and spoken passages). Returns the answer plus the source media it drew from. Use this when you want an answer rather than a ranked list of matching items (for the list, use echosaw_search_media). Optionally restrict the question to specific media with mediaIds, and pass conversationId to continue a prior exchange.ConnectorOAuth
- Lists every available CometChat implementation bundle with its identifier, title, target framework, and last-verified date.ConnectorNo auth
- Compatibility alias of memory.search — same implementation, same arguments, same results. Kept for clients pinned to this name; prefer memory.search.ConnectorNo auth