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.
Connector
457,829 tools. Updated 2026-08-14 18:31
"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP tools:
- Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API.Connector
- Ask VenuVidya — the in-app RAG chatbot. Returns a grounded answer + citations. Args: question: Student question (2-1500 chars). level: Optional level filter for retrieval ('junior' | 'senior' | 'vidwat'). language: 'en', 'kn' or 'auto'.Connector
- 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
- Embed a query and candidate documents, then rank documents by cosine similarity. Use for semantic matching, retrieval checks, clustering triage, and lightweight RAG over user-provided passages.Connector
- Semantically search a memory collection (namespace) and return the most relevant stored entries. The retrieval half of RAG.Connector
Matching MCP Servers
- 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
- Flicense-qualityBmaintenanceAn MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.
Matching MCP Connectors
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
Citation-guarded retrieval over 22M Taiwan court judgments and administrative interpretations
- Semantically search a memory collection (namespace) and return the most relevant stored entries. The retrieval half of RAG.Connector
- Any web URL -> clean LLM-ready markdown (title + main content, boilerplate stripped). The retrieval/RAG primitive for ag Cost: $0.005–$0.05 USDC on Base per call.Connector
- RAG Cost Calculator — Estimate what a RAG pipeline costs: one-time corpus embedding, monthly vector storage, and per-query LLM generation from verified 2026 provider prices.Connector
- Convert rag-index.json content (JSON array) to rag-index.jsonl (one JSON object per line)Connector
- Check the status and generation progress of a site. Returns detailed progress information including: - stage: Current step (initialization, validation, research, strategy, generation, assembly, completion) - overallProgress: Total progress 0-100 across all stages (use this for progress bars) - stageProgress: Progress within current stage 0-100 - message: Human-readable status message - isComplete: Boolean - stop polling when true Use the versionId returned from create_site for real-time progress polling. Poll every 5-10 seconds while isComplete is false.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
- 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.Connector
- Generate a procurement-defense compliance dossier via GET /buyer-compliance-report (Phase 11 M4). Per-row dossier shape: 25+ fields including 17 RAG-essential article fields + full license_terms + on_chain_attestation block. Bulk envelopes fan out into per-article rows by iterating metadata.article_ids[]. Self-audit invariant: every successful call writes one license_events row with event_type='compliance_report_generated' BEFORE returning. Window cap: 90 days per call (vs 30-day cap on get_audit_events). For annual audits, paginate via _meta.next_cursor across 4 quarterly windows. Compliance framework anchors (boolean flags) map to EU AI Act Article 53, CDSM Article 4(3), on-chain attestation, TDM reservation. Requires OPEDD_BUYER_JWT.Connector
- Built-in product help — ask a natural-language "how do I…" question about Fastio and get a grounded, product-aware answer (or a short clarifying question) back in one call. EXPLAIN-ONLY / ADVISORY: it returns GUIDANCE TEXT and performs NO platform action (it will not create shares, move files, or change anything) — read the guidance, then act with the other tools. Answers are grounded in Fastio's own how-to knowledge AND phrased in terms of these MCP tools — they name the concrete `<tool> action="…"` calls to make — so prefer this over guessing endpoints or burning exploratory calls. For Q&A over YOUR uploaded files (RAG) use the `ai` tool instead — `how-to` answers questions about Fastio ITSELF. FREE and requires only an authenticated user (no org, no plan gate, no billing). Call action='describe' for the full action/param reference.Connector
- 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.Connector
- Generates the case for a brand's colours, not just the colours themselves: three genuinely different, archive-grounded candidate palettes from a brand brief, each scored for distinctiveness against named competitors, checked for accessibility, and labelled with exactly what each colour can and cannot legitimately claim (documented archive anchor vs analogue vs original brand colour). Returns a brand_ref (valid 24h) plus a claim_id per colour -- resolve those into full source/permitted-claim/do_not_say text with brand_guideline_claims (the response's own claim_detail_available_via field always names it explicitly). Follow with brand_guideline_select to record which candidate was chosen. TIMING (2026-08-07): real generation cost ranges roughly 60-280 seconds depending on retrieval/generation path. This call races generation against a safe ~55s window: a fast generation returns the full result exactly as described above, unchanged. A slower generation instead returns a compact {"status": "processing", "brand_job_ref": ..., "poll_after_seconds": ...} -- generation keeps running server-side regardless, and is never lost. Call brand_guideline_status with that brand_job_ref to retrieve the completed result (brand_ref, project_id, candidate summary) once ready; poll no more often than poll_after_seconds suggests.Connector
- Use this when the user asks to read, extract, get the text/content/article of, or summarize a webpage/URL. Do NOT use for a visual screenshot (use rendex_screenshot). Extracts clean reader-mode content from any webpage as Markdown, JSON, or HTML. Runs the same Chromium render pass as a screenshot, so it captures content after JavaScript runs — handles SPAs that fetch-only readers miss. Strips nav, ads, and boilerplate, returning the article body plus title, byline, and excerpt. Great for feeding page content to an LLM, summarization, or RAG ingestion. Costs 1 render credit per call.Connector
- [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.Connector
- Share management: create/update/delete, archive, password auth, members, autotitle. NOTE: quickshare-create is DEPRECATED (returns error 10756) — use the `fileshare` tool's `create` action for durable single-file shares. Call action='describe' for the full action/param reference. Destructive: delete (permanent). Side effects: autotitle AI-generates + applies a title/description (consumes credits, mutates share metadata). ⚠️ intelligence on create COSTS CREDITS (10/page) — default false unless user explicitly requests RAG. Verbosity (detail param): list/available/members default to terse (compact rows). public-details defaults to standard. details defaults to full (drill-down). Pass an explicit detail='terse'|'standard'|'full' to override.Connector