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Developer builds Slnmap for accurate .NET code analysis by AI agents

By PulseAugur Editorial · [1 sources] ·

A developer has created Slnmap, a tool designed to improve AI agent understanding of .NET codebases. Slnmap uses the Roslyn compiler to build a semantic graph of a solution, storing it locally and making it accessible via MCP for AI agents. This approach aims to provide more accurate answers to queries about code changes, unlike AI agents that rely on file grepping and often miss crucial connections like interface calls. Slnmap also includes an interactive HTML export feature for visualizing code dependencies, which can be shared with teammates. AI

IMPACT Enhances AI agent accuracy in understanding complex codebases, potentially improving developer productivity.

RANK_REASON Developer-created tool for improving AI agent code analysis.

Read on dev.to — MCP tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer builds Slnmap for accurate .NET code analysis by AI agents

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Developer-created tool for improving AI agent code analysis.
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product, infra
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High
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61 days old
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COVERAGE [1]

  1. dev.to — MCP tag TIER_1 English(EN) · Mahmoud Nabil ·

    I built an interactive HTML export for .NET dependency graphs, because my AI agent kept guessing wrong

    <p>Every time I asked Claude Code or Copilot something like <em>"what breaks if I rename this method"</em> on a mid-sized .NET solution, it would grep through files, miss call sites that go through interfaces, and burn a lot of tokens getting a partial answer. On a solution with …

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