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ai-backend-performance-mcp

by robinafaruqia
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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A3.8/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool targets a clearly distinct performance concern: project-wide analysis, database queries, indexes, async patterns, connection pooling, and dependencies. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    All tool names follow the identical 'analyze_<topic>' pattern, making it predictable and easy to infer the function of each tool from its name. No mixed conventions or inconsistent verbs.

    Tool Count5/5

    Six tools is well-scoped for a performance analysis server. Each tool addresses a distinct aspect of backend performance without redundancy, and the count feels neither thin nor overwhelming.

    Completeness4/5

    The tool covers major performance domains: database queries, indexes, async patterns, connection pooling, dependencies, and a project overview. Minor potential gaps like memory or caching analysis are absent, but the core performance concerns are well represented.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources