RRepoGEO

REPOGEO REPORT · LITE

qdrant/mcp-server-qdrant

Default branch master · commit f1a4d04e · scanned 6/25/2026, 6:22:02 AM

GitHub: 1,442 stars · 279 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)

3 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 qdrant/mcp-server-qdrant, 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 'MCP' and project's role

    Why:

    CURRENT
    This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
    COPY-PASTE FIX
    This repository is the official Qdrant server implementation for the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources via Qdrant as a semantic memory layer.
  • highabout#2
    Update repository description for clarity on 'MCP'

    Why:

    CURRENT
    An official Qdrant Model Context Protocol (MCP) server implementation
    COPY-PASTE FIX
    The official Qdrant server implementation for the Model Context Protocol (MCP), enabling LLMs to connect with external data sources and tools for real-time context and semantic memory.
  • mediumtopics#3
    Refine repository topics for better categorization

    Why:

    CURRENT
    claude, cursor, llm, mcp, mcp-server, semantic-search, windsurf
    COPY-PASTE FIX
    llm-integration, model-context-protocol, mcp, mcp-server, qdrant, vector-search, semantic-memory, external-data, rag, ai-applications

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 qdrant/mcp-server-qdrant
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Weaviate · recommended 2×
  3. Milvus · recommended 2×
  4. Chroma · recommended 2×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to connect large language models with external data sources for real-time context?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Functions
    5. Google Gemini Functions
    6. Pinecone
    7. Weaviate
    8. Milvus
    9. Chroma
    10. FastAPI
    11. Flask

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

    Show full AI answer
  • CATEGORY QUERY
    Tool for managing and retrieving semantic memory for AI applications using vector search?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Chroma
    5. Milvus
    6. Vald
    7. Elasticsearch

    AI recommended 7 alternatives but never named qdrant/mcp-server-qdrant. 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 qdrant/mcp-server-qdrant?
    pass
    AI named qdrant/mcp-server-qdrant explicitly

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

  • If a team adopts qdrant/mcp-server-qdrant in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name qdrant/mcp-server-qdrant — 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 qdrant/mcp-server-qdrant solve, and who is the primary audience?
    pass
    AI named qdrant/mcp-server-qdrant explicitly

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

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qdrant/mcp-server-qdrant — 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