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LearnMCP Server

by BretMeraki
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    Deploy Server
    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

    A3.5/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no ambiguity. For example, add_learning_sources, delete_learning_sources, and list_learning_sources handle source management, while get_learning_summary, get_processing_status, and process_learning_sources focus on content processing and status. The descriptions reinforce these distinct roles, making tool selection straightforward for an agent.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case, such as add_learning_sources, delete_learning_sources, and get_learning_summary. This uniformity enhances readability and predictability, allowing agents to easily infer functionality from the naming convention without confusion.

    Tool Count5/5

    With 6 tools, the server is well-scoped for managing learning sources and summaries. Each tool earns its place by covering essential operations like adding, deleting, listing, processing, and retrieving status and summaries. This count is neither too sparse nor bloated, fitting the domain appropriately.

    Completeness4/5

    The tool set provides comprehensive coverage for the learning source domain, including CRUD-like operations (add, delete, list), processing, and status/summary retrieval. A minor gap exists in update functionality for learning sources, but agents can work around this by deleting and re-adding. Overall, the surface supports core workflows effectively.

    Maintenance

    ActivityInactive
    ResponsivenessSyncing

    Resources