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robertcprice

GLM-4.7 MCP Server

by robertcprice
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Python
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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 13 tools

    Disambiguation4/5

    Most tools have clearly distinct roles (e.g., summarize vs. explain vs. implement), but a few pairs are close: glm_review and glm_find_bugs both analyze code, and glm_explain could be seen as a specialized glm_ask. Overall, the boundaries are clear enough that an agent will usually pick the right tool, with only minor potential for confusion.

    Naming Consistency4/5

    All tools share the 'glm_' prefix and use snake_case, and most follow a verb or verb_noun pattern (e.g., glm_summarize, glm_implement, glm_write_tests). The one notable deviation is glm_status, which uses a noun instead of an action, slightly breaking the pattern. Otherwise, the naming is consistent and readable.

    Tool Count5/5

    With 13 tools, the set is well-scoped for a GLM-powered coding assistant. Each tool serves a distinct function—question answering, summarization, explanation, analysis, review, bug finding, implementation, refactoring, test generation, documentation, README generation, status checking, and cost comparison—and none feel redundant.

    Completeness4/5

    The tool surface covers the full development lifecycle: from asking questions and explaining concepts to analyzing, implementing, refactoring, testing, and documenting code. Minor gaps include a dedicated 'fix' tool (though implement/refactor can handle it) and a generic conversation tool (though glm_ask covers it). No critical workflows are missing.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources