Ask questions with RAG-enhanced context from xAI Collections; a lazy cache speeds up repeated queries by up to 100,000x.
127,308 tools. Last updated 2026-05-05 13:37
"Information or Uses for a Rag" matching MCP tools:
- Retrieve detailed information about a specific RAG project within the Calibre ebook library, including its configuration, contents, and organization for semantic search and contextual conversations.
- Retrieve relevance-ranked web content, including text chunks, tables, and code blocks, for AI grounding and RAG pipelines. Enables direct reasoning over page substance without manual fetching.
- Execute a complete RAG workflow in a single step to answer user questions: generate embeddings, perform semantic search, and answer using only retrieved context.
- Generate dialect-correct ALTER TABLE migration SQL and rollback from a plain-English intent. Output uses your database's exact dialect; never executed.
- Generate project-consistent identifier names from a natural language description. Uses actual conventions like prefixes and patterns to suggest names that fit the existing codebase style. Use when naming functions, variables, or parameters.MIT
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Connect your AI agent to 20,000+ executives (CEO, CFO, COO) of all major companies, 1M+ verified quotes, and full interview transcripts. S&P 500, NASDAQ, AI startups, Federal Reserve officials. 4 MCP tools, pay only for what you use. $5/1,000 results, no minimum, no commitment, cancel anytime. You only pay for returned valid results. You must create an API key at https://mcp.ceointerviews.ai and then authenticate here using header Authorization: Bearer <token> For access to our full data API please access https://ceointerviews.ai
- Apply iterative improvements to the last image generated or edited in your session. Automatically uses the previous image without requiring file paths, enabling continuous refinement through text-based change descriptions.MIT
- Compare two servers side-by-side to highlight category-level score differences or individual check details. Uses cached snapshots or live SSH audit.
- Query Vectara's RAG system to retrieve search results and generate contextual responses using specified corpus keys and API parameters for accurate information extraction.Apache 2.0
- Stores a knowledge fragment with source and evidence tier metadata for future retrieval via semantic RAG queries.MIT
- Retrieve detailed information about a specific Teamwork Desk ticket type by its ID. Use for auditing type usage, troubleshooting categorization, or automation integration.MIT
- Loads episodic context for a new task by combining RAG store, vault history, and sealed handoff data. Returns JSON with rag_matches, vault_history, handoff, and continuity type.MIT
- Purchase and register a new domain name. Automatically uses default payment or WHOIS details if not provided. Includes validation for TLD-specific registration data. Ideal for domain registration and management.MIT
- Retrieve detailed information for a specific photo or video by providing its ID and type.MIT
- Get detailed information about a specific status in Teamwork Desk by its ID for auditing, troubleshooting, or automation integration.MIT
- Retrieve detailed configuration and component information for a specific ZenML stack using its name, ID, or prefix.MIT
- Analyze component relationships by retrieving dependency trees to identify what components a specific element uses or what components depend on it.MIT
- Retrieve detailed information about a specific feature flag by ID or unified key to understand its configuration and status.MIT
- Retrieve detailed information about a specific block by its number or hash across supported blockchain networks using the Bankless Onchain MCP Server.MIT
- Retrieve delivery order details by entering the order code to track status, view information, and manage UU跑腿 (UU Errands) service requests.MIT