"Modal" matching MCP servers:

An MCP server for managing Modal — apps, containers, volumes, and secrets — and for deploying & running Modal apps directly from Claude Code and other MCP clients.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Live LLM API pricing: current token prices, model comparisons, cheapest-model lookups, and The LLM Price Index for 150+ models across 20+ providers, re-verified daily. No API key required.

Check whether an LLM model id is deprecated, retiring, or retired, and what to migrate to. Covers OpenAI, Anthropic, Azure, Bedrock, Google, and Cohere from each provider's own deprecation pages.

Enables AI coding agents to offload large file reads and boilerplate code generation to cheaper or local worker models through MCP, reducing token consumption and keeping context clean.

Enables AI clients to query, aggregate, and visually inspect a live Autodesk Revit model, reading it without model-changing transactions by default. Model mutations are opt-in and require two explicit gates, with dry-run previews and verification.

Temporal knowledge graph for codebases that captures decision traces, links test failures to code changes, learns co-edit patterns, predicts regression risk, and enforces learned constraints at the edit boundary via a PreToolUse hook.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

A persistent memory system for Large Language Models (LLMs) that enables continuous learning and knowledge retention across sessions through the Model Context Protocol (MCP).

Enables real-time fusion of 2D/3D visual bounding boxes, head-pose gaze vectors, and acoustic beamforming azimuth angles into cross-modal focal salience scores to resolve deictic spatial references for multimodal agents.

Enables AI agents and MCP clients to dispatch queries to the most suitable model among 70+ LLMs by scoring intent, budget, cost, and latency, returning structured routing decisions with execution telemetry. It runs as a zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, and other MCP-compatible clients.

Reachability-filtered browser perception and actions for AI agents. Filters snapshots to elements a user could actually reach (removes modal-occluded, closed-drawer, off-screen and inert elements), with ref-stable snapshots and state diffs.

Enables AI agents and MCP clients to estimate VRAM footprint and tensor parallelization requirements for quantized llama.cpp models, returning structured outputs without external dependencies.

Provides non-custodial, post-quantum encrypted memory for AI agents, enabling portable memory across models with provable erasure.

Enables browsing TokenLab models, comparing pricing, and generating cURL examples for various AI endpoints.

An event-driven MCP server that enables agents to share context streams, publish and subscribe to events, manage tasks, and follow protocols, keeping a fleet of agents mutually context-aware in real time.

Enables running deterministic multi-model prompt regression and golden dataset benchmark suites, exposing structured results and telemetry through the Model Context Protocol for integration with MCP-compliant clients.

Provides a native MCP-compatible autonomous agent that decomposes general tasks, runs multi-modal web and sandbox execution loops, and self-corrects toward goal resolution using sensory and ambient inputs.