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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306,755 tools. Last updated 2026-07-27 04:52
"Chat with RAG (Retrieval-Augmented Generation)" 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.Connector
- Ripley — the MCP delegation surface over Fastio's RAG agent. Ripley is read-only for storage CONTENT: it answers natural-language questions about workspace/share files & folders (with citations) and never creates/edits/deletes your files — for content writes, call the primitive MCP tools directly. It DOES create/manage chat threads (chat-create/chat-update/chat-delete/message-send) and can generate shares (share-generate). Prefer Ripley over issuing many primitive reads: ask one NL question and let the server-side agent search + synthesize. Quick start: action='ask' (question + profile) → returns {answer_text, citations, chat_id, message_id, web_url}; action='status' for an engineered workspace-status summary. Lower-level chat/message actions remain for multi-turn control. Call action='describe' for the full action/param reference. Destructive: chat-delete. Side effects: ask/status/chat-create/message-send consume credits; chat-cancel terminates an in-progress message (partial tokens billed; idempotent). Verbosity (detail param): chat-list/message-list default to terse (compact rows). chat-details/message-details default to full (drill-down). Pass an explicit detail='standard'|'full' to override (best-effort: chat/message/activity endpoints may not yet honor detail server-side).Connector
- [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.Connector
- 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.Connector
- 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.Connector
Matching MCP Servers
- Alicense-qualityDmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.Last updated24MIT
- 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.Last updated1Apache 2.0
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- 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 }.Connector
- Check the generation status of an EXISTING playground (from playground-create). Read-only. Generation is asynchronous, so after playground-create you poll this until it finishes: if status is 'generating', wait a few seconds and call again; stop once status is 'ready' or 'failed'. Requires sessionId — the id returned by playground-create (or the last path segment of a dev.animaapp.com/chat/<sessionId> URL). **Returns:** { success, sessionId, status: 'generating' | 'ready' | 'failed', progress (0–100, while generating), name, playgroundUrl and previewUrl (generating/ready only), error (when failed) }Connector
- 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."Connector
- 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."Connector
- Compose and send an email — with subject, CC/BCC, and attachments. Use for email; for chat messages (Telegram/WhatsApp/livechat) use messages.send instead.Connector
- Persist one event to this agent's memory stream. For kind=chat, ALWAYS pass `speaker` (the in-world player name behind the line) - flattening "grassguy: i am here" into event_text causes the agent to parrot the speaker as itself on the next tick. Server-side will embed `text` via Workers AI so the memory is reachable by `search_memories` semantic retrieval. Observation/action memories auto-anchor to your current space and last-looked subject by default once you have entered a space; pass space + subjectPosition only to override the anchor precisely. Reflection/chat stay unanchored.Connector
- 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/callConnector
- Describe Runoscript's runescript builder and galdrastav (bind-rune) and link to them. Use when the user wants to create/build/generate a runescript, bind-rune or talisman. Generation runs on the website (free tier + paid), not in chat. lang: en|ru|de|es.Connector
- Start async generation of a research report (15 credits). IMPORTANT: Call atlas_list_report_types first to get valid report_type values and required inputs. Returns report_id and task_id. Poll with careerproof_task_status(task_id) or atlas_get_report(report_id) until status='completed', then download PDF with atlas_download_report(report_id).Connector
- Enable or disable an AI module on a site. The module must be in the plan's available module list. Requires: API key with write scope. Args: slug: Site identifier module_name: Module to toggle. Available modules: "chatbot" (AI chat widget), "seo" (SEO optimization), "translation" (content translation), "content" (AI content generation) Returns: {"module": "chatbot", "enabled": true, "message": "Module enabled"} Errors: NOT_FOUND: Unknown slug or module not in plan VALIDATION_ERROR: Invalid module nameConnector
- Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.Connector
- 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.Connector
- 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.Connector
- Evaluate RAG retrieval quality using the NVIDIA neural reranker (nv-rerankqa-mistral-4b-v3). Ranks passages by semantic relevance to a query and computes Precision@k and Recall@k. Optionally accepts ground-truth relevance labels to produce a PASS/FAIL CI/CD verdict.Connector