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Hybrid MCP server exposing 11+ COBOL reverse-engineering tools (AST, ASG, CFG, DFG, copybook resolution, call-graph) for LLM-driven legacy modernization. Dual interface: MCP protocol (STDIO/SSE/Streamable HTTP) via FastMCP 2.0 + REST API via FastAPI. Python, PostgreSQL, LangGraph-ready.
mcp-server-language-converter is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 39/100. Hybrid MCP server exposing 11+ COBOL reverse-engineering tools (AST, ASG, CFG, DFG, copybook resolution, call-graph) for LLM-driven legacy modernization. Dual i. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding mcp-server-language-converter to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/legacy-ai-labs/mcp-server-language-converter.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "legacy-ai-labs/mcp-server-language-converter"
}Hybrid MCP server exposing 11+ COBOL reverse-engineering tools (AST, ASG, CFG, DFG, copybook resolution, call-graph) for LLM-driven legacy modernization. Dual interface: MCP protocol (STDIO/SSE/Streamable HTTP) via FastMCP 2.0 + REST API via FastAPI. Python, PostgreSQL, LangGraph-ready.