RRepoGEO

REPOGEO REPORT · LITE

esxr/langgraph-mcp

Default branch main · commit 16944242 · scanned 6/9/2026, 5:12:59 AM

GitHub: 583 stars · 109 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 esxr/langgraph-mcp, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the project's core identity immediately after the H1

    Why:

    CURRENT
    Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
    COPY-PASTE FIX
    This repository provides a **solution template** for building a Universal Assistant by combining LangGraph with the Model Context Protocol (MCP). It demonstrates a multi-agent pattern for integrating LLM applications with external data sources and tools.

Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
  • mediumhomepage#2
    Add a homepage link to the repository metadata

    Why:

    COPY-PASTE FIX
    https://your-project-homepage.com (replace with actual URL to a demo, documentation, or related blog post)

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 esxr/langgraph-mcp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI Functions · recommended 1×
  4. Microsoft Semantic Kernel · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How to integrate LLM applications with various external data sources and tools?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Functions
    4. Microsoft Semantic Kernel
    5. Haystack
    6. Zapier NLA

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

    Show full AI answer
  • CATEGORY QUERY
    What framework helps build complex AI applications with modular, graph-based workflows?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    3. LlamaIndex (run-llama/llama_index)
    4. Haystack (deepset-ai/haystack)
    5. Rasa (RasaHQ/rasa)
    6. Apache Airflow (apache/airflow)
    7. Prefect (PrefectHQ/prefect)

    AI recommended 7 alternatives but never named esxr/langgraph-mcp. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 esxr/langgraph-mcp?
    pass
    AI named esxr/langgraph-mcp explicitly

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

  • If a team adopts esxr/langgraph-mcp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named esxr/langgraph-mcp 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 esxr/langgraph-mcp solve, and who is the primary audience?
    pass
    AI named esxr/langgraph-mcp explicitly

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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