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aicb-roslyn-mcp

by gregordadera
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      olgaito59557
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      maintenance
      Enables AI coding agents and IDEs to query a local structural code graph and retrieve focused, token-bounded context on code entities, Git relationships, callers/callees, impact, paths, and recorded mistakes for more efficient sessions.
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      markw65142
      A
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      maintenance
      Enables AI coding agents to query a local structural code graph for focused context, including relevant source entities, Git relationships, call paths, impact, and recorded mistakes. It exposes this context so agents can investigate code and retrieve bounded context before reading full files.
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    • engram-serve

      davis06507
      A
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      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to query a local structural code graph and retrieve focused, token-bounded context about source entities, Git relationships, and selected project memory.
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    • engram-serve

      SompornBryant780
      A
      license
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      quality
      A
      maintenance
      Enables AI coding agents to retrieve bounded, focused context from a local structural code graph, including source entities, Git relationships, and recorded project memory. It supports exploring structure, inspecting callers, callees, and impact, and carrying prior mistakes into later sessions without reading full files.
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      tSchneider20
      A
      license
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      quality
      A
      maintenance
      Enables AI coding agents to query a local structural code graph and receive focused, token-bounded context about source entities, relationships, Git co-change, and selected project memory without reading full files.
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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

    A3.9/5.0

    Scored across 54 tools

    Disambiguation3/5

    Core clusters overlap: find_usages/impact_of_change/call_graph/review_context all answer impact questions, and get_context/explain_symbol/pack_for_task/prepare_task all assemble context. The descriptions explicitly distinguish them, but only after long cross-referencing text, so an agent can still misselect among near-neighbors.

    Naming Consistency3/5

    Every name is snake_case, but there is no single verb_noun convention: find_usages/get_context/list_insights sit beside noun phrases such as impact_of_change, coverage_gaps, symbol_signature and call_graph. It is readable, but the pattern is mixed rather than predictable.

    Tool Count1/5

    54 tools is an extreme surface for an agent-facing MCP server; even for a broad Roslyn analysis domain, many long-tail queries could be consolidated or exposed through profiles. The count imposes a high selection and prompt-cost burden on every use.

    Completeness4/5

    The surface covers session lifecycle, diagnostics, symbol/impact analysis, tests, metrics, insights, configuration, snapshots/diffs, XAML/binding/resource edges, DI and side effects. Minor gaps remain (e.g. no direct project/solution listing or apply operation, and some referenced diff tools are not in this pool), but core workflows are well covered.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

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