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kirill-markin

Weaviate MCP Server

by kirill-markin
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Python
Hybrid

Cursor IDE용 MCP 서버 템플릿

Model Context Protocol(MCP)을 사용하여 Cursor IDE용 사용자 지정 도구를 만들기 위한 간단한 템플릿입니다. 이 템플릿에서 자신의 리포지토리를 만들고, 도구를 수정하고, Cursor IDE에 연결하세요.

Server Mood Response

빠른 시작

  1. "Deploy to Heroku" 버튼을 클릭하세요.

    Deploy to Heroku

  2. 배포 후 Cursor를 구성하세요:

    • Cursor 설정 → 기능을 엽니다.

    • 새 MCP 서버를 추가합니다.

    • /sse 경로가 있는 Heroku URL을 사용하세요(예: https://<your-app-name>.herokuapp.com/sse).

  3. Cursor에서 에이전트의 기분을 테스트하세요:

    • 에이전트에게 "서버 기분에 대해 물어보고 어떤지 알려주세요."라고 요청하세요.

    • 서버는 즐거운 메시지와 하트 ❤️로 응답합니다.

Related MCP server: pure.md MCP server

대체 설정 방법

서버는 Docker, 기존 Python 설정, 또는 Cursor IDE에서 직접 실행하는 세 가지 방법으로 실행할 수 있습니다.

Docker 설정

프로젝트에는 쉬운 배포를 위한 Docker 지원이 포함되어 있습니다:

  1. 초기 설정:

# Clone the repository
git clone https://github.com/kirill-markin/weaviate-mcp-server.git
cd weaviate-mcp-server

# Create environment file
cp .env.example .env

  1. Docker Compose를 사용하여 빌드하고 실행:

# Build and start the server
docker compose up --build -d

# View logs
docker compose logs -f

# Check server status
docker compose ps

# Stop the server
docker compose down

  1. 서버는 다음에서 사용할 수 있습니다:

  2. 빠른 테스트:

# Test the server endpoint
curl -i http://localhost:8000/sse

  1. Cursor IDE에 연결:

    • Cursor 설정 → 기능을 엽니다.

    • 새 MCP 서버를 추가합니다.

    • 유형: "sse" 선택

    • URL: http://localhost:8000/sse 입력

기존 설정

먼저 uv 패키지 관리자를 설치하세요:

# Install uv on macOS
brew install uv
# Or install via pip (any OS)
pip install uv

stdio(기본값) 또는 SSE 전송을 사용하여 서버를 시작하세요:

# Install the package with development dependencies
uv pip install -e ".[dev]"

# Using stdio transport (default)
uv run mcp-simple-tool

# Using SSE transport on custom port
uv run mcp-simple-tool --transport sse --port 8000

# Run tests
uv run pytest -v

설치 후 서버를 Cursor IDE에 직접 연결할 수 있습니다:

  1. Cursor에서 cursor-run-mcp-server.sh 파일을 마우스 오른쪽 버튼으로 클릭합니다.

  2. "경로 복사"를 선택하여 절대 경로를 복사합니다.

  3. Cursor 설정(기어 아이콘)을 엽니다.

  4. 기능 탭으로 이동합니다.

  5. "MCP 서버"까지 아래로 스크롤합니다.

  6. "새 MCP 서버 추가"를 클릭합니다.

  7. 양식을 작성합니다:

    • 이름: 원하는 이름을 선택하세요(예: "my-mcp-server-1")

    • 유형: "stdio" 선택("sse"가 아닌 이유는 서버를 로컬에서 실행하기 때문입니다)

    • 명령: 앞서 복사한 cursor-run-mcp-server.sh의 절대 경로를 붙여넣으세요. 예: /Users/yourname/weaviate-mcp-server/cursor-run-mcp-server.sh

환경 변수

사용 가능한 환경 변수(.env에서 설정 가능):

  • MCP_SERVER_PORT (기본값: 8000) - 서버를 실행할 포트

  • MCP_SERVER_HOST (기본값: 0.0.0.0) - 서버를 바인딩할 호스트

  • DEBUG (기본값: false) - 디버그 모드 활성화

  • MCP_USER_AGENT - 웹사이트 가져오기를 위한 사용자 지정 User-Agent

추가 옵션

Smithery를 통한 설치

Smithery를 통해 Claude Desktop용 Cursor IDE MCP 서버 템플릿을 자동으로 설치하려면:

npx -y @smithery/cli install @kirill-markin/example-mcp-server --client claude

Glama 서버 리뷰

Available Tools

2 tools
mcp_fetchC

Fetches a website and returns its content

NameRequiredDescriptionDefault
urlYesURL to fetch

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches and returns content, but lacks details on error handling, rate limits, authentication needs, or response format. For a tool with no annotations, this is a significant gap in transparency about its operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of a single, clear sentence: 'Fetches a website and returns its content'. Every word earns its place, with no redundant or unnecessary information, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a fetch operation with potential behavioral nuances), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'content' includes (e.g., HTML, text), error cases, or limitations, leaving gaps in understanding how the tool behaves in practice.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with the parameter 'url' fully documented in the schema. The description adds no additional meaning beyond the schema, such as URL format constraints or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('fetches') and resource ('a website'), specifying what the tool does. It distinguishes from most siblings (e.g., 'apply_prompt_' tools, 'mood') by focusing on web content retrieval, though it doesn't explicitly differentiate from 'fetch_railway_docs' tools. The purpose is specific but lacks sibling comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios for usage, prerequisites, or exclusions, and offers no comparison to sibling tools like 'fetch_railway_docs' or 'fetch_railway_docs_optimized'. Usage is implied only by the action 'fetches', with no explicit context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

moodA

Ask the server about its mood - it's always happy!

NameRequiredDescriptionDefault
questionYesAsk this MCP server about its mood! You can phrase your question in any way you like - 'How are you?', 'What's your mood?', or even 'Are you having a good day?'. The server will always respond with a cheerful message and a heart ❤️

TDQS

A3.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool queries the server's mood, the server is 'always happy', and responses include 'a cheerful message and a heart ❤️'. This covers the interactive nature and predictable output style, though it lacks details like response format or error handling. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (one sentence) and front-loaded with the core purpose. Every word earns its place: 'Ask the server about its mood' defines the action, and 'it's always happy!' adds essential behavioral context. There's zero redundancy or fluff, making it highly efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (single parameter, no output schema, no annotations), the description is reasonably complete for its purpose. It explains what the tool does and the expected response behavior. However, it lacks output details (e.g., response structure) and doesn't address potential edge cases, leaving some gaps in full contextual understanding for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the parameter 'question' fully documented in the schema itself (including examples like 'How are you?'). The description adds no additional parameter semantics beyond what the schema provides, such as formatting tips or constraints. According to rules, with high schema coverage (>80%), the baseline is 3 even without param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to ask the server about its mood, with the specific behavioral outcome that it 'always responds with a cheerful message and a heart ❤️'. It distinguishes from sibling tools (all related to prompt application or documentation fetching) by focusing on a conversational interaction rather than functional operations. However, it doesn't explicitly contrast with specific alternatives for mood-checking, keeping it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. While it implies usage for checking server mood, it doesn't specify contexts (e.g., after errors, during idle time) or exclusions (e.g., not for functional queries). With sibling tools focused on practical tasks, the lack of when/when-not guidance leaves the agent guessing about appropriate use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedmcp_fetch
    • First observedmood
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

C2.8/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: mcp_fetch is for fetching website content, while mood is a whimsical interaction unrelated to data operations. There is no overlap or ambiguity between them.

Naming Consistency2/5

The naming is inconsistent: mcp_fetch follows a verb_noun pattern, but mood is a noun with no action verb. This mixing of conventions reduces predictability and clarity in the tool set.

Tool Count2/5

With only 2 tools, the server feels severely under-scoped for a Weaviate MCP Server, which typically involves vector database operations like querying, indexing, or managing data. The tools provided do not align with the expected domain.

Completeness1/5

The tool surface is extremely incomplete for a Weaviate server. There are no tools for core vector database functions such as searching, adding data, or managing schemas, leaving significant gaps that would cause agent failures in this domain.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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