RRepoGEO

REPOGEO REPORT · LITE

svnscha/mcp-windbg

Default branch main · commit 78504935 · scanned 5/22/2026, 9:32:03 AM

GitHub: 1,300 stars · 123 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface svnscha/mcp-windbg, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify 'MCP' in README to avoid Minecraft confusion

    Why:

    CURRENT
    A Model Context Protocol server that bridges AI models with WinDbg for crash dump analysis and remote debugging.
    COPY-PASTE FIX
    A **Model Context Protocol (MCP)** server that bridges AI models with WinDbg for crash dump analysis and remote debugging. This project is focused on Windows debugging, not Minecraft.
  • highreadme#2
    Reposition README opening to emphasize AI-powered debugger extension

    Why:

    CURRENT
    An AI-powered tool that bridges LLMs with WinDbg for crash dump analysis and live debugging. Execute debugger commands through natural language queries like "Show me the call stack and explain this access violation".
    COPY-PASTE FIX
    An AI-powered **debugger extension** that bridges Large Language Models (LLMs) with WinDbg for advanced crash dump analysis and live debugging. It allows you to execute debugger commands through natural language queries like "Show me the call stack and explain this access violation".
  • mediumreadme#3
    Explicitly state the target audience in the README

    Why:

    COPY-PASTE FIX
    Add this sentence to the 'Overview' or 'What is this?' section: "This tool is designed for developers, system administrators, and security researchers who use WinDbg for analyzing Windows crash dumps and live debugging sessions."

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface svnscha/mcp-windbg
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Microsoft Azure Machine Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Microsoft Azure Machine Learning · recommended 1×
  2. Azure Databricks · recommended 1×
  3. scikit-learn/scikit-learn · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How can I use AI to analyze Windows crash dumps more efficiently?
    you: not recommended
    AI recommended (in order):
    1. Microsoft Azure Machine Learning
    2. Azure Databricks
    3. scikit-learn (scikit-learn/scikit-learn)
    4. TensorFlow (tensorflow/tensorflow)
    5. PyTorch (pytorch/pytorch)
    6. Windbg
    7. pykd (pykd/pykd)
    8. ONNX Runtime (microsoft/onnxruntime)
    9. Elastic Stack
    10. Elasticsearch (elastic/elasticsearch)
    11. Kibana (elastic/kibana)
    12. Pandas (pandas-dev/pandas)
    13. NLTK (nltk/nltk)
    14. spaCy (explosion/spaCy)
    15. Splunk
    16. Splunk Machine Learning Toolkit (splunk/splunk-machine-learning-toolkit)

    AI recommended 16 alternatives but never named svnscha/mcp-windbg. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a tool to analyze crash reports using natural language with an AI assistant.
    you: not recommended
    AI recommended (in order):
    1. Sentry
    2. AppDynamics
    3. Datadog
    4. Dynatrace
    5. LogRocket
    6. Firebase Crashlytics

    AI recommended 6 alternatives but never named svnscha/mcp-windbg. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of svnscha/mcp-windbg?
    pass
    AI named svnscha/mcp-windbg explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts svnscha/mcp-windbg in production, what risks or prerequisites should they evaluate first?
    pass
    AI named svnscha/mcp-windbg explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo svnscha/mcp-windbg solve, and who is the primary audience?
    pass
    AI named svnscha/mcp-windbg explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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svnscha/mcp-windbg — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite