MCP server that gives an AI coding agent deterministic, evidence-first capabilities for engineering Python + Selenium UI automation projects.
Two tools with excellent descriptions (194 - 250 chars, well above baseline 194 avg) that explain WHAT, WHEN, and prerequisites. Both are read-only discovery/status tools with clear, actionable guidance. However, input schemas are completely absent, both tools declare Input: {} with no parameter definitions visible in source. Naming is clear (verb_noun pattern: genxevo_agent_status, genxevo_discover_project). No output schemas documented. No error handling guidance. No parameter descriptions (N/A since no params, but output structure not specified). Risk annotations present (READ_ONLY) but no tool annotations (readOnlyHint, etc.) in MCP sense.
Report what the GenXEvo agent currently is: whether it is configured, which workspace roots it may read, what its security policy is, which Python interpreter is hosting the server, whether test execution is permitted, and exactly which capabilities this build provides versus which are planned for a later phase. Call this FIRST in any session, and again whenever a capability behaves unexpectedly. Note that the interpreter reported here runs GenXEvo itself and is usually NOT the interpreter the automation project's tests run on -- use genxevo_discover_project for that. Returns the GenXEvo ToolResult envelope: branch on the 'status' field, never on the prose in 'summary'. Read-only and always safe to repeat.
Scan the configured workspace and report the Python automation project that is actually there: pyproject.toml, requirements files, setup.cfg and lock files with their declared dependencies; the test runner the project itself states it uses (pytest or unittest, or 'unknown' -- never assumed); the test roots, established from files matching the project's own collection patterns rather than from any folder being called 'tests'; virtual environments with the interpreter version read from pyvenv.cfg; the browser automation library in use (Selenium, Playwright, Appium); candidate page-object directories, identified because their modules drive a browser and are not collected as tests; and CI configuration. Every conclusion carries the signals that produced it and an explicit confidence level, so you can tell a fact from a guess. GenXEvo never imports or executes anything in the project to obtain this -- setup.py and conftest.py are read as text only. Call this before reasoning about any test, locator or run. Read-only, makes no assumption about folder layout, and always safe to repeat. If 'status' is 'partialSuccess' the scan hit a limit or could not read something, and 'not found' may mean 'not looked at'.
Input schemas completely absent. Both tools declare Input: {} with no JSON Schema definitions visible.
Output schemas not documented. Descriptions mention returned fields (status, summary, signals, confidence) but no formal schema structure provided for LLM parsing.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) in MCP protocol sense. Risk field in source is informal; MCP spec requires structured annotations.
No error handling guidance. Descriptions do not explain failure modes (e.g., 'partialSuccess' mentioned but no recovery path documented).
Inferred effective spec: 2025-06-18+.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 49 | 2025-06-18+ | v2 |
Only 2 tools total. Server lacks breadth for practical automation workflows (no execution, no locator discovery, no test result parsing, no remediation).