"Debugging and Navigating Code in a Project" matching MCP servers:

MCP server providing diagnostic tools to analyze software architecture, security, REST API compliance, and dependencies for multiple programming languages. Enables AI agents to run scans and audits on codebases.

Enables automated security code auditing using LLM and MCP, including AST parsing, taint analysis, dataflow tracing, and automated PoC generation for multi-language codebases.

Multi-language SAST and AI-powered fuzzing MCP server for Claude Code integration, enabling static and dynamic security analysis of code.

Enables AI coding assistants to run static analysis on GitHub repositories, raw code snippets, or local directories to detect cloud cost leaks in Terraform/AWS configs, PostgreSQL migration table-lock hazards, and OWASP/Next.js API security gaps. Each finding returns structured severity, line-level detail, and drop-in fixes.

Enables security testing of MCP guardrails and SAST scanners by exposing intentionally vulnerable code patterns that are inert by default and make no network calls.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Enables coding agents and CI/CD pipelines to audit untrusted code by detecting Python sandbox escapes, SQL injection taint, ReDoS regex vulnerabilities, and CORS misconfigurations, while generating Seccomp BPF filters for safer execution.

Enables MCP-compatible agents to audit an entire repository through one question, detecting stacks and running parallel checks for dead code, dependencies, security, CVEs, complexity, and lint across Laravel/PHP, Python, TypeScript/React, and Flutter/Dart. It returns normalized, severity-sorted findings with false positives flagged and secret values kept out of the output.

Provides 28 MCP tools across 16 analysis engines for comprehensive Python code quality assessment, including complexity scoring, security scanning, dead code detection, dependency auditing, and test quality analysis.

An AI-native code security audit MCP server that supports multi-language AST analysis, call graph analysis, vulnerability detection, and AI deep audit.

Enables AI-powered automated testing, security scanning, code review, and maintenance tasks directly within Claude Code or desktop.

Enables authorized web/API security analysis as a stateful, multi-stage workflow with persistent session state, human-controlled validation, candidate versus confirmed finding tracking, and stop conditions.

Enables security analysts to carry structured investigation state across sessions by storing typed observations, hypotheses, tasks, evidence and versioned decisions in a local SQLite graph with full-text search. It also enforces authorized scope and request controls, redacted and content-hashed evidence, duplicate-test signatures and validation gates that leave findings in draft until reproducibly confirmed, then produces technical and executive reports for Antigravity, Codex and other stdio MCP clients.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

Code review as an MCP server — structured code quality reviews with OWASP security scanning.

Free deterministic security scan of a public git repository (GitHub, GitLab, Codeberg, Bitbucket): vulnerable dependencies via OSV.dev, secret patterns, and config lint, returned as structured JSON. Tools: scan_repository(url), audit_pricing(). Runs locally over stdio (python3 web/mcp_stdio.py or the Dockerfile) and is also hosted at https://project-feldspar.com/mcp. MIT licence, stdlib-only. Buil

An MCP server that provides passive and low-impact active reconnaissance tools for authorized bug bounty and security assessments, enabling LLMs to perform structured recon and generate reports.

Enables autonomous AI agents and penetration testers to conduct authorized security audits with persistent cross-session memory, zero-trust secret scrubbing, dynamic OWASP/ASVS checklists, and hallucination-free exploit PoC generation from captured traffic.

Enables MCP clients to run security and code review on pull requests and diffs, exposing review_pr and review_diff capabilities with local-first analyzers and LLM explanations.

Autonomous penetration testing powered by a local LLM, automating tool chaining for recon, vulnerability scanning, exploit research, and reporting.