RRepoGEO

REPOGEO REPORT · LITE

Dicklesworthstone/mcp_agent_mail

Default branch main · commit 35e774fa · scanned 6/29/2026, 7:16:59 AM

GitHub: 2,011 stars · 211 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
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 Dicklesworthstone/mcp_agent_mail, 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 README opening to clarify AI agent coordination

    Why:

    CURRENT
    # MCP Agent Mail
    
    > "It's like gmail for your coding agents!"
    
    A mail-like coordination layer for coding agents, exposed as an HTTP-only FastMCP server. It gives agents memorable identities, an inbox/outbox, searchable message history, and voluntary file reservation "leases" to avoid stepping on each other.
    COPY-PASTE FIX
    # MCP Agent Mail: Asynchronous Coordination for AI Coding Agents
    
    > "It's like gmail for your coding agents!"
    
    This project provides a dedicated, asynchronous coordination layer specifically for AI coding agents, exposed as an HTTP-only FastMCP server. It gives agents memorable identities, an inbox/outbox, searchable message history, and voluntary file reservation "leases" to avoid stepping on each other in a shared codebase.
  • mediumlicense#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License
    
    This project includes a LICENSE file that outlines the terms of use. Please refer to the LICENSE file directly for specific details regarding its custom or compound licensing terms.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the project's official homepage URL (e.g., a documentation site or the repository URL itself) to the 'About' section of the GitHub repository.

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 Dicklesworthstone/mcp_agent_mail
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Git
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Git · recommended 1×
  2. GitHub · recommended 1×
  3. GitLab · recommended 1×
  4. Bitbucket · recommended 1×
  5. iterative/dvc · recommended 1×
  • CATEGORY QUERY
    How can AI agents coordinate work and prevent file overwrites in a shared codebase?
    you: not recommended
    AI recommended (in order):
    1. Git
    2. GitHub
    3. GitLab
    4. Bitbucket
    5. DVC (iterative/dvc)
    6. Apache Kafka (apache/kafka)
    7. RabbitMQ (rabbitmq/rabbitmq-server)
    8. ZooKeeper (apache/zookeeper)
    9. etcd (etcd-io/etcd)
    10. LangChain (langchain-ai/langchain)
    11. LlamaIndex (run-llama/llama_index)

    AI recommended 11 alternatives but never named Dicklesworthstone/mcp_agent_mail. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good way for multiple AI agents to communicate and manage message history?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Redis
    3. PostgreSQL
    4. SQLAlchemy
    5. MongoDB
    6. ChromaDB
    7. Weaviate
    8. LlamaIndex
    9. pgvector
    10. Microsoft Semantic Kernel
    11. Azure AI Search
    12. Qdrant
    13. RabbitMQ
    14. Apache Kafka
    15. Redis Pub/Sub
    16. Cassandra
    17. Haystack
    18. Elasticsearch

    AI recommended 18 alternatives but never named Dicklesworthstone/mcp_agent_mail. 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 Dicklesworthstone/mcp_agent_mail?
    pass
    AI did not name Dicklesworthstone/mcp_agent_mail — 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?

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

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

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Dicklesworthstone/mcp_agent_mail — 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