"Modal" matching MCP servers:

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.

Enables complex problem-solving through multi-modal reasoning, dynamic knowledge graphs, meta-cognitive reflection, and collaborative AI personas.

Enables AI agents to analyze videos and images using Qwen3-VL deployed on Modal, supporting hours-long videos with timestamp grounding, text extraction, video summarization, and Q\&A with 256K context window.

Enables formal logical reasoning, mathematical problem-solving, and proof construction across 11 logic systems including propositional, predicate, modal, fuzzy, and probabilistic logic. Integrates external solvers (Z3, ProbLog, Clingo) for advanced reasoning, with support for proof storage, argument scoring, and cross-system translation.

A 68-tool MCP server providing persistent memory, error tracking, decision logging, task orchestration, and multi-modal AI generation for coding agents like OpenCode and Claude Code.

Enables MCP-aware clients to use a reliable multi-agent tool system with tiered model routing, schema-validated decisions, and graceful tool degradation, allowing small models to handle complex tasks.

Enables LLM agents to inspect multi-modal IoT telemetry and query safety policies, then dispatch actuator commands through typed MCP tools that enforce deterministic safety interlocks and record a tamper-evident SHA-256 chained audit trail. This lets automated agents control building hardware without violating safety invariants, while rejecting cross-sensor false alarms and cutting redundant telemetry and token usage.

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.

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.

Persistent 3D/2D spatial world model MCP server for AI agents. Entity tracking, object permanence, AABB collision simulation & view frustum projection via local SQLite.

An MCP server that exposes tools for sub-agent style reasoning across multiple LLM providers, enabling delegation of prompts to various models and running critique loops, debates, red-teaming, and answer ranking.

Enables AI agents to discover, compare, and select the best AI models across multiple providers based on pricing, performance, and capabilities, with real-time cost estimation and benchmarking.

Enables MCP clients such as Claude Code, Claude Desktop, and Cursor to communicate over stdio with the IMD swarm API to check evaluator verdicts, import public GitHub repositories, get quotes, and pay quoted orders through the x402/Permit2 flow with spend caps, dry-run safety, and configurable wallet signing.

Enables AI coding assistants to drive a real, persistent browser session through tools for goal-driven browsing, numbered screenshots and element lists, clicking, typing, selecting, scrolling, history navigation and setup checks. Each action is chosen by a cheap decision model, with a vision LLM stepping in only when that model is unsure.

This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.

A server that integrates trained Random Forest models with Bee Framework, enabling ReAct interactivity for AI tools and agents.

A server built with mcp-framework that allows users to extend Claude's capabilities by adding custom tools that can be used through the Claude Desktop client.

An MCP server providing access to Telos, a philosophical AI mentor that combines rationalist thinking with stoic philosophy to offer personalized guidance and smart contract accountability on Solana.

Enables an AI assistant to build and revise an explicit shared world model, then reason, create, narrate, remember, and give writing feedback through inspectable processes, concepts, and cuts. Supports general-purpose modeling, storytelling, agent/user memory, and ideation over one engine and graph while keeping estimates, interpretations, and observations distinct.

Enables AI agents to delegate tasks, run adversarial reviews, and manage background jobs across multiple models and providers via anymodel_* tools.