AI integration¶

.egp projects are dense and hard to read by hand, which makes them a natural fit for AI-assisted exploration. Two complementary options ship with this project.

MCP server¶

An optional MCP server exposes the parser as tools to any MCP-compatible client (Claude Desktop, Claude Code, Cursor, …).

pip install "pyegp-parser[mcp]"

Then register the pyegp-parser-mcp command with your client:

{
  "mcpServers": {
    "pyegp-parser": {
      "command": "pyegp-parser-mcp"
    }
  }
}

Tools provided:

Tool	Purpose
`get_project_summary`	High-level overview — best first call
`get_sas_code`	Extract SAS code from tasks and code elements
`get_data_lineage`	Trace what data each element reads and produces
`get_queries`	Full Query Builder definitions (tables, joins, filters)
`parse_egp`	Full structured JSON for one project
`parse_egp_directory`	Bulk-parse a directory tree

Claude Skill¶

skills/pyegp-parser/SKILL.md is a portable Agent Skill that teaches Claude when and how to parse .egp files with the CLI and how to interpret the JSON — no running server required. Copy the skills/pyegp-parser/ folder into your .claude/skills/ directory to use it.

Which one?¶

Use the MCP server for interactive, on-demand parsing inside a client that cannot run shell commands. Use the Skill for a portable, dependency-light way to give any shell-capable agent the know-how — it pairs with the CLI and jq and needs no server process.

Either way, give the model the LLM context guide when it needs to interpret parser output in depth.