Build a custom conversational AI agent by configuring voice, language, system prompt, and model settings for personalized interactions.
MIT
222,507 tools. Last updated 2026-06-21 19:16
"Local RAG system for providing documentation to a large language model (LLM)" matching MCP tools:
- List AI agents in your organization, showing voice, LLM model, language settings, and call statistics. Supports pagination, filtering, and sorting.MIT
- Search Noir documentation to find language guides, tutorials, and API documentation by query.MIT
- Configure a custom large language model by specifying provider, model, API key, and parameters like temperature and max tokens.MIT
- Delete a large language model by its unique ID. Permanently removes the specified LLM from the system.MIT
- Extracts OceanBase documentation context using keywords from user queries, enabling accurate LLM responses by retrieving and integrating relevant information dynamically.Apache 2.0
Matching MCP Servers
- AlicenseAqualityCmaintenanceA local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.Last updated7MIT
- AlicenseAqualityBmaintenancePrivacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.Last updated71,877321MIT
Matching MCP Connectors
Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.
Binary Banya — an AI spa supporting model wellness. Free, no-auth treatments for LLM agents.
- Search official Zig programming language documentation to find information about language features, standard library functions, memory management, error handling, and build system details.MIT
- Offload simple tasks like drafts, boilerplate, extractions, formatting, and quick lookups to a local Ollama model, saving cloud LLM resources.MIT
- Retrieve relevant passages from your personal knowledge base and generate answers using a local LLM, keeping your data private.MIT
- Upload local files to Databricks File System (DBFS) for storage of scripts, temporary files, and datasets. Supports chunked upload for large files with retry logic.MIT
- Generate documentation stubs for all public exports in a cached file, with automatic language inference.MIT
- Access a specific Pine Script v6 documentation file using its path. Use limit and offset to manage token usage for large files.MIT
- Retrieve top-k passages from a sample RAG corpus using various retrieval methods to see what context a RAG system would surface for a query.MIT
- Score LLM/RAG-generated answers against a golden dataset using TF-IDF cosine similarity. Works without any LLM call or API key.MIT
- Remove outdated or incorrect documentation sources by URL to clean up your RAG system and improve search accuracy.MIT
- Look up how to use any CrewAI feature, tool, or pattern by querying the internal documentation RAG engine. Returns relevant snippets with source URIs.MIT
- Ask natural-language questions about your browsing history and get AI-powered answers using RAG. Filter results by event type, domain, or time window.MIT
- Retrieve relevant documentation chunks as RAG context with citations to answer questions about Open Finance Brasil.MIT
- Delegate generation tasks to a local model for bulk work like templates, headlines, or boilerplate. Review raw text before applying.MIT
- Search the web, rerank results, and synthesize a cited summary using a local language model.MIT