RRepoGEO

REPOGEO REPORT · LITE

mark3labs/mcp-go

Default branch main · commit 481f0567 · scanned 5/28/2026, 9:01:43 AM

GitHub: 8,749 stars · 835 forks

AI VISIBILITY SCORE
28 /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
2 / 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 mark3labs/mcp-go, 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
  • hightopics#1
    Add relevant topics for categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, large-language-models, ai, generative-ai, protocol, go, golang, external-tools, data-integration, function-calling, agent-frameworks
  • highreadme#2
    Add a clear, explicit definition of 'Model Context Protocol (MCP)' to the README's opening

    Why:

    CURRENT
    <strong>A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.</strong>
    COPY-PASTE FIX
    # mcp-go: Go Implementation of the Model Context Protocol (MCP)
The Model Context Protocol (MCP) is a standard for enabling Large Language Models (LLMs) to seamlessly integrate with external data sources and custom tools, facilitating advanced function calling and contextual understanding.
  • mediumreadme#3
    Add a 'Why mcp-go?' or 'How is this different?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why mcp-go?
Unlike generic LLM API wrappers or full-fledged agent frameworks, mcp-go provides a robust, Go-native implementation of the Model Context Protocol. This protocol-first approach ensures standardized, seamless integration between your LLM applications and any external data source or custom tool, focusing on reliable function calling and context management.

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 mark3labs/mcp-go
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
sashabaranov/go-openai
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. sashabaranov/go-openai · recommended 2×
  2. LangChain Go · recommended 1×
  3. Go's Standard Library · recommended 1×
  4. net/http · recommended 1×
  5. encoding/json · recommended 1×
  • CATEGORY QUERY
    How can I integrate external data sources and custom tools with LLM applications in Go?
    you: not recommended
    AI recommended (in order):
    1. LangChain Go
    2. Go's Standard Library
    3. net/http
    4. encoding/json
    5. context
    6. sync
    7. OpenAI Go Library
    8. sashabaranov/go-openai (sashabaranov/go-openai)
    9. gRPC
    10. sqlx
    11. gorm

    AI recommended 11 alternatives but never named mark3labs/mcp-go. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Go library to enable large language models to call external functions.
    you: not recommended
    AI recommended (in order):
    1. Go-OpenAI/OpenAI-go (Go-OpenAI/OpenAI-go)
    2. GoogleCloudPlatform/golang-samples/generative-ai/gemini (GoogleCloudPlatform/golang-samples/generative-ai/gemini)
    3. tmc/langchaingo (tmc/langchaingo)
    4. sashabaranov/go-openai (sashabaranov/go-openai)
    5. Your Own Custom Implementation

    AI recommended 5 alternatives but never named mark3labs/mcp-go. 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 mark3labs/mcp-go?
    pass
    AI named mark3labs/mcp-go explicitly

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

  • If a team adopts mark3labs/mcp-go in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name mark3labs/mcp-go — likely talking about a different project

    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 mark3labs/mcp-go solve, and who is the primary audience?
    pass
    AI named mark3labs/mcp-go explicitly

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

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mark3labs/mcp-go — 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