"Resources for Coding in Python" matching MCP servers:

A server providing programming guidelines and coding standards for multiple languages, enabling access and search of best practices for C#, Go, JavaScript, Python, and TypeScript.

Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.

Automatically enforces team coding standards in AI-assisted development by providing an MCP server that AI assistants can query for language-specific standards, style guides, and best practices.

Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

Semantic code graph MCP server for coding agents

Serves token-efficient code context for AI coding assistants by exposing Python signatures, call graphs, and budget-packed task context as MCP tools, enabling precise codebase queries without reading entire files.

Helps AI assistants optimize Linux workloads on Arm64 by parsing perf report output, recommending NEON SIMD intrinsics for hot loops, and auditing Python dependency manifests for arm64 wheel availability — all offline and structured.

Enables coding agents to perform safe, project-wide Python refactoring (rename, move, extract, inline, change signature, organize imports, etc.) with a dry-run safety contract and LSP-coordinate addressing.

Code Intelligence Platform for AI Coding Assistants

Enables AI assistants to understand and navigate codebases through structural analysis. Provides code mapping, symbol search, and impact analysis using ast-grep for accurate parsing of Python, JavaScript, TypeScript, and Go projects.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

embedded code search engine CLI -AST based that just works - improves speed and efficiency for coding agent

Enables coding agents to identify likely runtime failures in JavaScript and TypeScript by ranking risky files, tracing dependencies, analyzing logs, and obtaining independent model reviews with line-level reasons.

truth is a deterministic fact-checker for the claims an AI coding agent makes about its own work.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Agent-native "safe to ship?" security gate for AI-generated code. Uses real parsers and inter-rocedural taint analysis (JS/TS, Python, Go) to flag the classes AI coding agents get wrong — secrets, SQL injection, SS, SSRF, path traversal, command injection, weak JWT/CORS — and ranks findings by confidence. Exposes a scan tool over MCP.

Structural memory for coding agents — 60% fewer tokens, refactor-safe, runs entirely on your machine.

Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.

Deterministic navigation maps over code AND markdown for AI agents: one MCP server, two lenses (tree-sitter TS/JS/Python + markdown). Map a whole project, read one function or one doc-section, ~99% less context. Merges codelens + docslens.

In-memory polyglot AST pruning and deterministic context firewall for coding agents, eliminating up to 72.4% token bloat with zero telemetry egress.