"Using local LLMs for code writing, reviewing, and rule generation" matching MCP servers:

An MCP server that exposes the llms.txt file and its referenced local or external resources from a project root to provide context for AI models. It automatically parses documentation links and URLs to make them accessible as additional MCP resources.

Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.

Content writing toolkit for headline generation, readability scoring, tone analysis, outline building, and plagiarism similarity checking.

Offline, read-only MCP server that predicts which custom Wazuh rules wazuh-analysisd will ignore at load (missing or later-loaded if_sid, if_group, duplicates, static fields) — calibrated 15/15 against Wazuh 4.14.7. Also lists Wazuh 5.0 migration blockers.

Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.

Enables agents to discover the llms.txt index covering any site, fetch its linked documentation pages as markdown, and search across parsed indexes by section heading, link title, and notes.

Enables AI-powered image generation using Azure DALL-E 3 and FLUX models with intelligent automatic model selection. Generates stunning photorealistic or creative images directly within LibreChat through simple text prompts.

Enables users to check Google organic, map pack, and local finder positions for keywords across specified locations, returning per keyword/location rows with ratings, competitors, and rank changes. It runs searches from each location via Apify and can track history with a state key.

Gemini-powered MCP server for automated code review, analysis, and documentation.

Checks a code repository without running it: Safe, Works and Complete scores with an A-F grade, catching leaked keys, dangerous calls, invented or lookalike packages, broken imports and missing project files. Standard-library Python; MCP tools scan, orphans and explain.

our ChatGPT and Claude chat history in one local SQLite file, imported from their data exports. Read-only tools to search and read past conversations; no network.

Let me evaluate the README content.

Local-first temporal knowledge graph with six read-only MCP retrieval tools. Supports Ollama and embedded LadybugDB; additions use a separate CLI proposal queue with human approval and explicit application.

Local tools that let your AI use Mail, Calendar, Contacts, Teams, Slack, WhatsApp, OneDrive, Google Drive, Notion, Outlook and Office files on Mac and Windows. Runs on your computer. Works with Claude, ChatGPT, Codex, Cursor and other MCP clients.

Enables users to simulate quantum circuits, evolve qubit state vectors, apply gates such as Hadamard and CNOT, perform Born-rule projective measurements with Monte Carlo shot histograms, and run Grover amplitude amplification through MCP-compatible clients.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.

Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.