"Information about guardrails" matching MCP servers:

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

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

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Enterprise AST codebase context reduction engine with up to 95% token savings, 20 tools, polyglot support, and zero-leak security guardrails. "Toprak Ahmet Aydoğmuş"

MCP server that provides npm package information for AI agents during TypeScript development.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Aegis keeps Claude Code, Codex CLI and Antigravity on your project's rules via a zero-dependency Model Context Protocol (MCP) server (aegis-mcp), AGENTS.md / CLAUDE.md bootstrap, git pre-commit hooks, and CI governance (aegis check). It serves each agent just-in-time rules for the files it is about to edit, then enforces AST and repository invariants before commit and in pull requests.

Enables AI agents to search and retrieve details about Odoo code elements (models, fields, views, etc.) with exact file locations using MCP tools.

Provides coding agents with a compact map of a TypeScript/TSX codebase, enabling direct answers about symbol locations, imports, dependencies, and references while reducing token usage.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.

Enables AI coding assistants to look up 43 structured uni-app / WeChat mini-program constraint rules by category and severity before writing code, then statically scan a project to catch inline rpx styles, WXSS wildcards, defineProps generics, missing subpackage flags, animation fill-mode bugs, and leaked secrets, internal IPs, AppIDs or server paths before release. Also provides pre-submission release checklists for app store review.

Enforces AST-based architectural guardrails on AI-generated code, blocking writes that violate maintainability, security, or complexity rules and prompting immediate refactoring.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Enables AI agents to run Shipcheck on local JavaScript/TypeScript repositories, scanning for launch risks like exposed env vars, unsigned webhooks, and missing security guardrails.

Exposes Lachesis's code navigation tools over MCP, letting LLM agents ask precise compiler-level questions about dataflow, taint, callers, and guards in source code.

An MCP server for gitingest. It allows MCP clients like Claude Desktop, Cursor, Cline etc to quickly extract information about Github repositories including repository summaries, project directory structure, file contents, etc

Provides tools for mapping and analyzing GitHub repositories. It allows users to set a GitHub Personal Access Token and retrieve detailed information about a specified repository, including its structure and summary statistics.

A local, read-only MCP server that provides AI coding agents with structural evidence about a repository, including dependency analysis and impact assessment, while naming the boundary of every answer without any LLM calls.

unerr is operational memory—a local runtime behind your agent's MCP enforcing shared code graphs, session memory & guardrails. No silent breakages. No context rot.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

Give your coding agent the dependency graph it is about to change: scan, diff, explain, audit.

Read-only MCP server that provides AI agents with context about Unity project relationships, including scenes, prefabs, scripts, GUIDs, and code impact analysis.

An MCP server that ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.

Enables AI coding agents to compile polyglot repositories into architectural mipmap containers and use stateful pre-edit guardrails, fog-of-war resolution gating, blast-radius analysis, and compact Markdown packets to inspect code and prevent unsafe edits.

Remote MCP server that gives coding agents verified, version-pinned answers about fast-moving frameworks, starting with Expo SDK 54.

Enables agents to query TypeScript and JavaScript code intelligence—type diagnostics after edits, go-to-definition, references, hover information, and document symbols—through MCP tools.

Helps AI read GitHub repository structure and important files. Want to quickly understand what a repo is about? Prompt it with "read https://github.com/adhikasp/mcp-git-ingest and determine how the code technically works".

Lets AI agents know things about files without reading them, supporting outline, search, slicing, SQL, log clustering, diffs, validation, and extraction for formats like PDF, DOCX, PPTX, XLSX, CSV, logs, and code.