RRepoGEO

REPOGEO REPORT · LITE

awslabs/mcp

Default branch main · commit 689a2b0d · scanned 6/21/2026, 2:46:48 AM

GitHub: 9,303 stars · 1,593 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 awslabs/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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clarify its role for AI agents and its relationship to Agent Toolkit for AWS

    Why:

    CURRENT
    # Open source MCP servers for AWS

A suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.
    COPY-PASTE FIX
    # Open source Model Context Protocol (MCP) servers for AWS for AI Agents

This repository provides a suite of specialized Model Context Protocol (MCP) servers designed to help you integrate and manage AI agents effectively on AWS. While the Agent Toolkit for AWS is the recommended successor for production use, this repository remains active and accepts contributions, serving as a foundation for understanding and extending MCP for AI agent development.
  • mediumtopics#2
    Add relevant topics to improve discoverability for AI agent-related queries

    Why:

    CURRENT
    aws, mcp, mcp-client, mcp-clients, mcp-host, mcp-server, mcp-servers, mcp-tools, modelcontextprotocol
    COPY-PASTE FIX
    aws, mcp, mcp-client, mcp-clients, mcp-host, mcp-server, mcp-servers, mcp-tools, modelcontextprotocol, ai-agents, generative-ai, llm-agents, agent-toolkit
  • lowabout#3
    Update the repository description to explicitly mention AI agents

    Why:

    CURRENT
    Open source MCP Servers for AWS
    COPY-PASTE FIX
    Open source Model Context Protocol (MCP) servers for AWS, designed for building and managing AI agents.

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 awslabs/mcp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Amazon SageMaker
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon SageMaker · recommended 1×
  2. Amazon ECS (Elastic Container Service) with Fargate · recommended 1×
  3. Amazon EKS (Elastic Kubernetes Service) · recommended 1×
  4. AWS Lambda (with Container Images) · recommended 1×
  5. Amazon EC2 Instances (with Auto Scaling Groups) · recommended 1×
  • CATEGORY QUERY
    How can I deploy open source servers for AI agents on AWS infrastructure?
    you: not recommended
    AI recommended (in order):
    1. Amazon SageMaker
    2. Amazon ECS (Elastic Container Service) with Fargate
    3. Amazon EKS (Elastic Kubernetes Service)
    4. AWS Lambda (with Container Images)
    5. Amazon EC2 Instances (with Auto Scaling Groups)
    6. AWS App Runner

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

    Show full AI answer
  • CATEGORY QUERY
    What tools help manage model context protocol for building AI agents effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Assistants API
    5. Microsoft Semantic Kernel
    6. Pinecone
    7. Weaviate
    8. Chroma
    9. Qdrant
    10. Guidance

    AI recommended 10 alternatives but never named awslabs/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
    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 awslabs/mcp?
    pass
    AI named awslabs/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 awslabs/mcp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named awslabs/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 awslabs/mcp solve, and who is the primary audience?
    pass
    AI named awslabs/mcp explicitly

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

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awslabs/mcp — 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