"Project Manager Agent positions" matching MCP servers:

Security scanning, prompt injection detection, secret leak detection, and agent permission auditing for AI agent workflows.

An MCP server that lets an AI agent probe a live URL and confirm whether sensitive files (e.g., .git, .env, source maps) are genuinely served by fetching and validating the content, avoiding false positives.

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

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.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

Self-hosted MCP engine for private code reviews, providing deterministic static analysis and AST-level search over diffs, with findings passed to a review agent of your choice.

Agent-PLC MCP Server: auto-detect, probe, read, write and diagnose Modbus TCP / FINS / MELSEC / Siemens S7comm PLCs, with transparent proxy and fault-injection listener.

Provides security audit primitives for MCP servers, agent tool schemas, and system prompts, along with pentest utilities like JWT inspection and HTTP diffing.

Two-way Burp Suite MCP bridge enabling AI agents to capture traffic, analyze endpoints, queue scans, and send findings back to Burp.

Connects MCP-compatible AI clients to a MITRE Caldera adversary emulation platform, enabling natural language construction of attack scenarios, agent inspection, and operation management.

Enables interaction with Picus Security's Breach & Attack Simulation API, allowing natural language queries for simulations, threat library searches, and agent management through MCP-compatible clients.

Enables MCP clients to work with DefAudit, supporting project and scan listing, starting scans, retrieving results, and marking or unmarking false positives.

A security gate MCP server that audits agent extensions (skills, MCP servers, tools) by scanning for risks, adversarial analysis, and sandbox execution, returning a trust verdict of allow, quarantine, or block.

Enables MCP-native multi-agent security audits for Google Antigravity, orchestrating subagents for 0day research, static analysis, PoC verification, exploit chaining, reporting, and code remediation.

Security scanning for AI coding tools (Claude Code, Cursor, Windsurf) including secrets detection, MCP config vulnerabilities, agent instruction checks, threat modeling, prompt injection testing, pre-commit security checks, and dependency vulnerability scanning.

A MCP-enabled asset mapping and vulnerability scanning agent with AI self-reflection, allowing natural language queries and automated scanning decisions.

Performs static security analysis of Solana programs and generates x402 payment invoices for automated agent-to-agent audit workflows.

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.

A security scanner for AI coding agents and autonomous assistants that scans code for vulnerabilities, detects hallucinated packages, blocks prompt injection, and provides LLM-powered semantic code review via MCP or CLI.

An intentionally vulnerable MCP server for security training, simulating a fictional company with 28 vulnerable tools and 38 challenges to learn AI agent attack and defense.