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

An MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents, enabling them to analyze CPU consumption, call trees, GC pressure, and async bottlenecks.

Enables local LLMs to perform enterprise-grade code auditing, AST security screening, architectural planning, and real-time Discord observability with zero cloud API costs.

Enables AI agents to trace Python code execution in real time via PEP 669, exposing call trees, variable deltas, and async context through MCP tools.

This server runs inside an Rsbuild or Rspack dev server to expose MCP tools for inspecting and editing live Vue component state, component trees, Vue Router info, and Pinia stores.

Record Python/Rust/TypeScript program runs to local SQLite; query call trees, exceptions, and value flow over MCP — refuses rather than guesses. Not the unrelated Telegram sensorium-mcp.

Integrates AI assistants with Graylog to query and analyze log data using Elasticsearch syntax and stream-specific filtering. It enables users to perform advanced searches, retrieve log statistics, and manage Graylog streams through natural language.

Enables AI assistants to inspect a Crossplane control plane read-only, discovering managed, composite and claim resources, tracing composition trees, and explaining failures with events and reasons. It also surfaces package health, composition definitions and validation, environment and runtime configs, and overall diagnostics across multiple clusters.

Provides structured MCP tools for local runtime observability and debugging of React Native/Expo apps on Android, enabling evidence-first diagnostics such as screenshots, UI trees, per-request network capture, performance metrics, and CDP-based console/heap/profiling exports.