"File system requirements: mounted directory, persistent state, and full R/W access" matching MCP servers:

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

An agentic file-system assistant that lets users read, write, list, and search local files through natural language, using a LangChain agent with an Ollama LLM backed by a FastMCP server.

Enables AI assistants to execute shell commands and manage long-running processes within persistent tmux sessions across isolated workspaces. It features a dual-window architecture to separate raw command execution from interactive terminal output.

Enables AI agents to get evidence-backed allow, review, or block decisions before installing or invoking third-party tools, with trust-aware discovery, official docs search, and prediction-market risk preflight.

Provides state and log management tools designed for long-lived AI agents that may be interrupted and resumed. It enables tracking agent progress and maintaining an append-only event history to ensure continuity across multiple sessions.

Single source of truth and control for agent-operated companies, managing business state with deterministic policy enforcement, seat identity, and hash-chained audit trail.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Governance framework for LLM agents. Exposes seven mechanically-enforced rules plus tools for preflight file protection, audit-chain verification, session handoff and worklog templates, MCP config auditing, and an emergency kill switch.

A production-ready Model Context Protocol server implementation that connects AI assistants to the TON blockchain, allowing them to query wallet balances, transaction details, smart contracts, and other blockchain data.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Cryptographic identity and trust protocol for AI agents. 38 MCP tools across 8 protocol layers: Ed25519 identity, delegation chains, values compliance, signed communication, policy engine, task coordination, cross-layer integration, and agentic commerce. 264 tests passing.

Enables MCP-compatible agents to arbitrate typed state-action schemas, dispatch routine actions deterministically, and apply pre-escalation filters that reduce unnecessary frontier LLM calls. It supports low-latency, schema-enforced routing with safety guardrails and local-first memory retention.

Enables persistent storage and retrieval of Tree of Thought search states in PostgreSQL, allowing the travel agent to maintain reasoning context across multi-turn itinerary optimization.

Enables AI agents to persist and restore checkpoint state, supporting crash recovery and replay for LangGraph-based workflows.

Enables multi-agent systems to share and coordinate a common state space over MCP, detecting concurrent mutation race conditions, synchronizing state through vector clocks, and resolving conflicting decisions via consensus arbitration. Also supports deterministic policy validation and cryptographic state hashing with zero external dependencies.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables AI agents to store, read, extend, and share encrypted persistent memory, with pay-per-use access via x402 payments on Base.

Enables AI agents to commit stateful workflow checkpoints by calculating state hashes and rollback points through the Model Context Protocol. It provides deterministic, dependency-free checkpoint management for autonomous agent execution.

Enables AI agents to arbitrate typed state-action schemas and dispatch deterministic actions locally, so routine or reflexive decisions are resolved in sub-millisecond fashion instead of incurring expensive frontier-LLM calls. It also applies token-saving pre-escalation filters, escalating only ambiguous or multi-hop plans to larger models while enforcing reversible execution checkpoints, strict token budgets, and local-first memory retention.

Enables AI agents to capture and commit stateful workflow checkpoints during autonomous execution, computing deterministic state hashes and rollback points. It runs as a zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients for reliable, low-latency checkpoint management.

MCP server that coordinates autonomous infrastructure health monitoring agents, enabling natural-language telemetry queries and automated diagnosis, remediation, and verification.

A production-grade MCP server with 6 sandboxed tools and an agent orchestration engine for autonomous task completion, featuring an evaluation suite with CI/CD quality gates.

An MCP server that provides access to Google's Access Context Manager API, enabling management of service perimeters and access levels through natural language.

A Model Context Protocol (MCP) server that enables AI assistants to perform comprehensive file operations including finding, reading, writing, editing, searching, moving, and copying files with security validations.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

An AI-guided conversation-driven development framework that helps build MVPs and POCs rapidly through intelligent dialogue with specialized MCP services.

A master control platform that orchestrates intelligent agents with a plug-and-play architecture, allowing users to manage and coordinate multiple AI agents through a unified system.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

MCP server that simulates an AI's desire-driven internal state with nine dimensions, event-based updates, dynamic heartbeat, thought pool, and persistent storage, enabling behavior suggestions and inner monologue generation.

A state-based agent orchestration system that allows transitions between different states (IDLE, PLANNING, RESEARCHING, EXECUTING, REVIEWING, ERROR) while maintaining conversation context and providing state-specific prompts.