Explorar comunidad

📇 🪟 - LLM quantization via tool call. Convert models to GGUF, GPTQ, and AWQ formats. Recommend optimal quant settings, evaluate quality, and push to Hugging Face Hub.

- Structural observability for AI conversations. Detects loops, stuck states, breakthroughs, and convergence across 17 channels without analyzing content.

📇 - AI-powered code refactor engine with 80+ MCP tools for code analysis, hotspot detection, complexity metrics, persistent memory, and automated refactoring plans.

📇 - An MCP server to convert almost any file or web content into Markdown

- Tools and templates to create validated and maintainable data charts and dashboards.

📇 🪟 - Data compression MCP server. 7 tools for gzip, brotli, deflate, and TurboQuant quantization. Auto-selects best algorithm. 60x compression on docs. Zero dependencies.

- Unit conversion and dimensional analysis backed by the bundled GNU units database (3000+ units, compound expressions, reduction to SI base units). Offline and deterministic. .

- Official MCP server enabling seamless orchestration of hyperparameter search and other optimization tasks with Optuna.

🪟 - Agent-operable ML experiment contract (cq.yaml + JSON contracts) with a built-in MCP server exposing 14 tools (resolve/inspect/run/validate/describe/compare/lineage) for running, validating, and tracing experiments across any framework (PyTorch / HF Trainer / Lightning / sklearn / XGBoost). Apac

- Enables autonomous data exploration on .csv-based datasets, providing intelligent insights with minimal effort.

- This Kaggle MCP Server makes Kaggle more accessible by letting you browse competitions, leaderboards, models, datasets, and kernels directly within MCP, streamlining discovery for data scientists and developers.

📇 🪟 - Decision intelligence MCP server with 19 algorithms (bandits, Monte Carlo, constraint optimization, forecasting, anomaly detection, risk analysis, graph algorithms), 28 MCP tools. Install via .

- Enables agents to query local information about dependencies in a Ruby project's .

🪟 - Model Context Protocol for R: enables AI agents to participate in interactive live R sessions.

- connects Jupyter Notebook to Claude AI, allowing Claude to directly interact with and control Jupyter Notebooks.

- The first NetworkX integration for Model Context Protocol, enabling graph analysis and visualization directly in AI conversations. Supports 13 operations including centrality algorithms, community detection, PageRank, and graph visualization.

📇 🪟 - Real-time LLM/VLM model comparison with benchmarks, pricing, and personalized recommendations from 5 data sources. No API key required.

🪟 - Deterministic batch tools so LLM agents stop next-token-guessing dates and math. Rich snapshot (18 fields), batch dispatcher (diff/until/since/add/weekday/business_days, natural-language parsing), Python eval with math+stats pre-loaded, and Pint-based unit conversion. One wiring for dates + math

- Airtight math for agents: 3.7M-theorem search, PSLQ constant ID, OEIS, real Lean kernel checks, applicability checklists. No LLM inside, no API key.

🪟 - Link multiple data sources (SQL, CSV, Parquet, etc.) and ask AI to analyze the data for insights and visualizations.

🪟 - MCP server for the Dingo: a comprehensive data quality evaluation tool. Server Enables interaction with Dingo's rule-based and LLM-based evaluation capabilities and rules&prompts listing.

📇 🪟 — Tools for creating and interacting with GrowthBook feature flags and experiments.

- A comprehensive Go-based MCP server for mathematical computations, implementing 13 mathematical tools across basic arithmetic, advanced functions, statistical analysis, unit conversions, and financial calculations.

- Predict anything with Chronulus AI forecasting and prediction agents.