Convert local files or URLs in 22 formats (PDF, DOCX, PPTX, XLSX, EPUB, RTF) to heading-aware Markdown for RAG ingestion and LLM context.
MIT
484,048 tools. Updated 2026-08-28 02:36
"Local RAG system for providing documentation to a large language model (LLM)" matching MCP tools:
- Query Okareo documentation to understand concepts like Scenarios, Checks, Targets, Drivers, Evaluations, and Simulations. Choose conceptual mode for technical detail or user_legible for plain-language explanations.Apache 2.0
- Register a generation model for testing by selecting an LLM from the registry, enabling use with run_test.Apache 2.0
- Store facts, preferences, or notes in local long-term memory for reuse across sessions. Saves content to a local SQLite file without network or LLM calls, preserving user context.Apache 2.0
- Update a registered generation model to point to a different LLM from the registry, enabling model swaps.Apache 2.0
- Delegate text or chat completion to a local LLM to save costs and keep data private. Supports streaming via progress notifications.Creative Commons Attribution Non Commercial No Derivatives 4.0 International
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.7MIT
- AlicenseAqualityCmaintenanceMCP server connecting Claude Code to LM Studio, delegating token-expensive tasks to a local model while keeping the cloud model in control. It reduces cloud context usage by reading files locally and returning only the processed results.4MIT
Matching MCP Connectors
Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.
Fetch URLs and return clean Markdown for RAG — nav, ads and boilerplate stripped.
- Check if a machine learning model fits on local hardware by estimating weight and KV-cache size, then comparing against available GPU VRAM or system RAM.Creative Commons Attribution Non Commercial No Derivatives 4.0 International
- Create a new AI voice agent by configuring the language model, text-to-speech, and speech-to-text settings. Define the system prompt and voice to deploy a custom virtual assistant.MIT
- Convert a natural-language show description into a validated cue list (scenes) for TouchDesigner via LLM or grammar parser. Optionally build a cue sequencer rig.MIT
- Verify that an LLM response engaged with the provided source document using the Semantic Grounding Index score. Ideal for auditing RAG pipeline provenance.Apache 2.0
- Check the current status of the RAG documentation index, including total chunks, configured sources, and last update times.Apache 2.0
- Register a GitHub repository as a documentation source for RAG indexing. Specify repo, branch, and file patterns, then run ingestion to make the docs searchable.Apache 2.0
- Search Python library documentation using natural language queries to find specific functions, classes, and usage examples from consolidated sources.MIT
- Get precise answers to any question about Lamatic.ai documentation by searching all indexed docs with RAG.MIT
- Generate an OPC UA YAML model from a natural language description using AI. Automatically detects companion specs and validates the model.MIT
- Verify Playwright MCP server configuration and connectivity by performing a local system health check. Returns system information and configuration status for browser automation.
- Ask natural language questions to get answers with cited sources from the Onyx knowledge base using RAG.MIT
- Retrieve LLM-readable documentation for a module, including rules and implementation notes, to guide coding against the module contract.MIT
- Rephrase an existing root cause analysis verdict into plain language using a local LLM, without altering facts or citations.MIT
- Configure a custom large language model by specifying provider, model, API key, and parameters like temperature and max tokens.MIT