"Target" matching MCP servers:

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.

An installable MCP server that reviews diffs/PRs against a target repository's own coding conventions, learned via RAG over its code, docs, and lint config, citing specific file:line sources.

Audits local datasets and ML training code for target, temporal, and entity leakage, PII exposure, and invalid evaluation design, returning structured evidence and pass/review/block verdicts before model training.

Drives test coverage toward target thresholds by coordinating workspace test execution with code-tree-mcp uncovered-symbol analysis.

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

Provides audit_plugin_health and prepare_semantic_review tools for deterministic inspection of Codex plugins and Agent Skills, generating evidence-backed reports without executing or transmitting target code.

Attaches to a running C++ process and exposes tools to inspect live memory via a resident GDB subprocess, enabling LLMs to read fields, follow pointers, and access STL containers without modifying target code.

A Model Context Protocol server for static .NET assembly analysis, offering decompilation, IL disassembly, metadata inspection, and protection detection tools without executing target assemblies.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.