"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.

An MCP server that enables AI agents to interact with Modal, allowing them to deploy apps and run functions in a serverless cloud environment.

An MCP server that allows users to deploy Python scripts to Modal directly from Claude, providing a link to the deployed application that can be shared with others.

A remote MCP bridge that lets MCP clients use Modal sandboxes for Linux execution, dependency installation, Git operations, GPU jobs, and invoking deployed Modal Functions.

Local, read-only Modal context for coding agents. Enables agents to inspect Modal apps, deployments, logs, containers, volumes, and sandboxes without granting mutation tools.

Enables MCP-aware agents to drive Modal serverless compute, including GPU workers and headless Chromium for web browsing with screenshots.

Facilitates running Python code in a sandbox and generating images using the FLUX model via an MCP server compatible with clients like Goose and the Claude Desktop App.

Enables AI agents to run arbitrary Python scripts on Modal's serverless infrastructure with on-demand CPU, memory, and GPU resources, providing scalable compute without maintaining always-on servers.

Provides real-time stock analysis tools including price lookup, comprehensive investment scoring, and company-to-ticker conversion through a MCP interface.

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

Enables real finite element analysis from natural language: accepts STEP geometry and plain-English questions, validates setups deterministically, meshes and solves with CalculiX on Modal, and returns engineering verdicts (stress, safety factor, deflection, modal frequencies) with rendered plots and an interactive 3D viewer.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

An MCP server that seats multiple LLMs as a council, letting your assistant query them in parallel or in sequence, relay answers for cross-critique, and merge conclusions within one conversation.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Enables AI agents to search, compare, and right-size Hugging Face models, including estimating whether a model fits in available GPU VRAM before downloading it.

Enables agents to query governed metrics through MCP, including describing metrics, running deterministic SQL against a warehouse, explaining join path preferences, and searching semantically ambiguous terms.

A bridge that connects the Model Context Protocol to the Gemini CLI via the Agent Client Protocol (ACP) for high-performance, stateful AI interactions. It enables MCP clients to leverage Gemini's autonomous capabilities, including multi-modal support and built-in tool execution, without cold-start overhead.

A multi-modal user intent collection MCP server that allows AI agents to ask users questions and receive text/image replies.

Enables querying Odoo's model registry (fields, inheritance, methods) without a database connection by loading the registry in-memory.