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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650,440 tools. Updated 2026-10-13 07:35
"Information about 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.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
- Get detailed information about a specific ad request, including pool selections if targeting mode is manual.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
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
Matching MCP Connectors
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- Keep a generation permanently. Outputs made through this connector expire about 24 hours after generation and are NOT stored in the user's BudgetPixel assets by default. Call this when the user wants to keep, reuse later, or find a result in their gallery / workshops — it copies the output into their assets (this counts against their storage like a website generation). Pass the job_id (and type for video / music / sound_effect). Safe to call twice: an already-saved job just reports already_saved.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
- Use this when the user asks about Seerxo credits or usage. Shows the authenticated account's remaining monthly generation quota. Free.ConnectorNo auth
- Use this when the user wants papers on a topic, such as "find recent open-access papers on retrieval-augmented generation evaluation" or "systematic reviews on intermittent fasting". Pass topic words, and optionally from_year, open_access_only and a limit of up to 10. Asks Europe PMC and Crossref together and returns one merged list with each paper's authors, year, venue, DOI, citation count, free full-text link when the index confirms one, and the index it came from. Does not search arXiv: use search_arxiv for preprints. Does not check retractions or reference lists.ConnectorNo auth
- Check the current user's token status and account information. This tool retrieves information about: - User ID - Paid account status - Remaining generation tokens (for non-paid users) - Account validity Returns: A JSON string containing token status and account informationConnectorOAuth
- Partitions raw text documents into uniform sliding-window segments with configurable character overlap. Returns an array of formatted text chunks. Use when preparing unstructured documents for vector database embeddings and RAG retrieval pipelines. Do not use for syntactic token counting or semantic sentence segmentation.ConnectorNo auth
- Show the user's available Metodic credits (the balance that generation tools spend — for team members this is the team's shared balance). Call when the user asks about credits, cost, or whether they can afford a generation.ConnectorNo auth
- 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.ConnectorNo auth
- Retrieve detailed information about a specific campaign including its status, budget, participants, and delivery metrics.ConnectorNo auth
- Retrieve detailed information about a specific scheduled message, including its status, send time, and message content.ConnectorNo auth
- Inspect the authenticated owner's private RAG usage, limits, and eligibility.ConnectorNo auth
- List only the authenticated owner's private RAG collections.ConnectorNo auth
- Get information about who built this MCP server and why.ConnectorNo auth
- Perform fast Google web searches. Returns organic search results, related searches, and pagination. Suitable for quick information retrieval when advanced filtering is not needed. Requires a search query in `q`.ConnectorOAuth
- Get detailed information about a specific AI model, including its inputs, outputs, parameters, and plan requirements.ConnectorOAuth