RRepoGEO

REPOGEO REPORT · LITE

samanhappy/mcphub

Default branch main · commit 75e497f9 · scanned 6/20/2026, 6:32:42 AM

GitHub: 2,172 stars · 269 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
33 /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
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 samanhappy/mcphub, 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 H1 to explicitly state 'AI Model'

    Why:

    CURRENT
    # MCPHub: The Unified Hub for Model Context Protocol (MCP) Servers
    COPY-PASTE FIX
    # MCPHub: The Unified Hub for AI Model Context Protocol (MCP) Servers
  • mediumtopics#2
    Add broader AI/ML and API Gateway topics

    Why:

    CURRENT
    mcp, mcp-gateway, mcp-hub, mcp-router, mcp-server
    COPY-PASTE FIX
    mcp, mcp-gateway, mcp-hub, mcp-router, mcp-server, ai-inference, model-serving, api-gateway, orchestration, llm-ops
  • lowabout#3
    Refine description to explicitly mention AI models

    Why:

    CURRENT
    A unified hub for centrally managing and dynamically orchestrating multiple MCP servers/APIs into separate endpoints with flexible routing strategies.
    COPY-PASTE FIX
    A unified hub for centrally managing and dynamically orchestrating multiple AI Model Context Protocol (MCP) servers/APIs into separate endpoints with flexible routing strategies for AI inference.

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 samanhappy/mcphub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kong/kong
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kong/kong · recommended 2×
  2. AWS API Gateway · recommended 2×
  3. emissary-ingress/emissary · recommended 2×
  4. nginx/nginx · recommended 2×
  5. triton-inference-server/server · recommended 1×
  • CATEGORY QUERY
    How can I centrally manage and route requests to multiple AI model services?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. Kubernetes (kubernetes/kubernetes)
    3. Istio (istio/istio)
    4. Kong Gateway (Kong/kong)
    5. AWS API Gateway
    6. Azure API Management
    7. Google Cloud Endpoints
    8. Ambassador Edge Stack (emissary-ingress/emissary)
    9. OpenResty (openresty/openresty)
    10. Nginx (nginx/nginx)
    11. Lua

    AI recommended 11 alternatives but never named samanhappy/mcphub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an API gateway to orchestrate and route traffic for various AI inference endpoints.
    you: not recommended
    AI recommended (in order):
    1. Kong Gateway (Kong/kong)
    2. Apigee
    3. Ambassador Edge Stack (emissary-ingress/emissary)
    4. AWS API Gateway
    5. Tyk API Gateway (TykTechnologies/tyk)
    6. Envoy Proxy (envoyproxy/envoy)
    7. Nginx (nginx/nginx)

    AI recommended 7 alternatives but never named samanhappy/mcphub. 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 samanhappy/mcphub?
    pass
    AI did not name samanhappy/mcphub — 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 samanhappy/mcphub in production, what risks or prerequisites should they evaluate first?
    pass
    AI named samanhappy/mcphub 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 samanhappy/mcphub solve, and who is the primary audience?
    pass
    AI named samanhappy/mcphub 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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MARKDOWN (README)
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samanhappy/mcphub — 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