"Understanding Abstract Syntax Trees (AST)" matching MCP servers:

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

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.

An MCP server that exposes tools for issuing scoped agent credentials, delegating narrower child credentials, handling approvals, revoking task trees, and retrieving audit trails and evidence packets.

Production-grade MCP server providing deterministic evaluation, AST sandboxed execution, DNS routing audit, and runtime guardrails for autonomous LLM agents.

High-performance local MCP server and CLI written in Rust that scrubs 90%+ framework noise from AI debug logs with Tree-sitter AST validation and test rollback.

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.

A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

Windows computer-use MCP server: accessibility-first element actions (UIA), UI read as diffed text trees instead of screenshots, guarded raw input, and a cross-session arbiter that yields to the human. 19 tools.

Empower LLMs with fine-grained visual understanding — detect, localize, and describe anything in images with natural language prompts.

Maximizes AI agent context window by enabling compact code reading and editing, reducing tokens by 40% for deeper codebase understanding.

Provides seamless access to Grok AI's capabilities (chat completion, image understanding, and function calling) directly from Cline via the Model Context Protocol.

Agent-first programming language: agents produce JSON AST, the compiler validates, type-checks, effect-checks, verifies contracts via Z3/SMT, and compiles to WASM. 19 MCP tools for the full compile-and-execute loop.

Short Description (概要): An MCP server providing FEP (Free Energy Principle) matrix-pruning tools (resolve_fep_consensus) for multi-agent deadlock resolution and syntax-locked A2A consensus.

Enables MCP clients to compute shortest paths, topological dependency orderings, maximum network flows, minimum spanning trees, and PageRank-style centrality over graph topologies using only the Python standard library. It supports optimal multi-agent communication backbone and execution-plan analysis without external graph dependencies.

Enables MCP clients to run zero-dependency graph algorithms—including topological sorting, strongly connected component detection, shortest paths, maximum flow, minimum spanning trees, and PageRank—for agent dependency resolution and execution planning.

Enables agents to compute shortest paths, topological dependency order, maximum flow and minimum-cut bottlenecks, minimum spanning trees, and PageRank centrality over weighted graphs for routing and execution planning. It exposes these graph-analysis capabilities as MCP tools through a zero-dependency Python server.

Enables graph analysis over interconnected agent networks by computing centrality and authority scores, shortest paths, dependency orderings, maximum flows, and minimum spanning trees through MCP tools.

Enables AI agents to operate local desktops and Chromium browsers through MCP tools, unifying accessibility trees, physical input, screenshots, DOM/ARIA, visual grounding, and result verification.

Enables agents to compute shortest paths, topological orderings, maximum flow and minimum cut bottlenecks, minimum spanning trees, and centrality rankings for graph-based multi-agent routing. It runs as a zero-dependency MCP server using standard Python libraries.