"Information about Automata Labs Team" matching MCP servers:

Enables orchestrating a reusable team of expert agents (PM, architecture, development, QA, security) to analyze a project's files and produce consolidated recommendations. It exposes tools to list experts, ask a single expert, or run the full team from MCP clients.

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

Persistent memory for AI coding agents: local-first semantic search, dependency graphs, git history.

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.

Topos scores code quality by analyzing the geometric and topological structure of program graphs, surfacing structural debt that conventional linters can't compute. Gives coding agents a medal-scored (SLOP → GOLD) feedback loop for writing cleaner, more composable code.

MCP server that lets Claude Code ask GPT Codex for adversarial planning, code review, debugging, research, and risk triage without leaving your project workflow.

Enables AI agents to screen GitHub repositories and pull requests for risk analysis, generating risk scores, findings, and merge-readiness signals.

Grants AI agents secure, verifiable access to SAP ABAP systems (ECC 6.0 and S/4HANA) so they can review code and transport orders, run ATC and ABAP Unit checks, trace where-used relationships, query tables with read-only ABAP SQL, diagnose incidents across dumps, jobs and application logs, and consult official SAP documentation. Writes are restricted to development systems with an explicit transport order, and every result reports which system it came from, returning typed errors instead of misleading empty results when a check cannot be completed.

Enables AI agents to self-govern by scanning code for hardcoded secrets, structural violations, and AI drift in real-time, providing fix packets for automatic remediation.

Enables coding agents such as Claude Code and Cursor to check a team's learned decisions from pull requests, reviews, and docs before writing code, showing whether breaking them is blocking or advisory. It also provides locally computed symbol impact/blast-radius analysis so source code never leaves the machine.

Enola. is a local Model Context Protocol (MCP) server. A deterministic map of your codebase for AI coding agents — the real architecture, extracted from your source, not guessed.

Enables querying indexed code across multiple repositories with file-level citations, reviewing pull requests, auditing dependencies, and exporting SBOMs and evidence packs through MCP tools.

An MCP server that gives AI assistants deep awareness of the PyGo framework, enabling code generation, schema querying, module inspection, and registry operations through natural language.

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

Enables any MCP client to request an independent second opinion from a fresh Claude Code session without blocking, useful for plans, architecture, debugging, security reviews, and final checks.

Semantic concept graph MCP server for codebases — what your code means, not just what it does.

A code graph that extends to any language via pluggable adapters, with task/QA traceability and real MCP tools — zero-config to try.

Enables coding agents to retrieve a bounded context packet linking relevant code with prior session evidence, decisions, failures, and fixes from local Codex and Claude Code history.

MCP server for persistent code exploration and diff-aware review, exposing explore and review tools to map call paths, blast radius, and generate explainable risk-scored reports.

Enables automatic auditing and pruning of uncommitted git diffs to enforce surgical-only AI code changes, removing bloat, drive-by refactors, and cosmetic formatter churn before commit.