Weaviate MCP Server
MCP Server Template for Cursor IDE
Простой шаблон для создания пользовательских инструментов для Cursor IDE с использованием Model Context Protocol (MCP). Создайте свой репозиторий из этого шаблона, измените инструменты и подключите их к вашей Cursor IDE.

Быстрый старт
Нажмите кнопку «Deploy to Heroku»
После развертывания настройте Cursor:
Откройте Настройки Cursor → Функции
Добавьте новый MCP-сервер
Используйте ваш URL Heroku с путём
/sse(например,https://<your-app-name>.herokuapp.com/sse)
Проверьте настроение вашего агента в Cursor:
Спросите вашего агента: «Пожалуйста, спросите о настроении нашего сервера и сообщите мне, как оно».
Сервер ответит весёлым сообщением и сердечком ❤️
Related MCP server: pure.md MCP server
Альтернативные способы настройки
Вы можете запустить сервер тремя способами: с помощью Docker, традиционной настройки Python или непосредственно в Cursor IDE.
Настройка Docker
Проект включает поддержку Docker для простого развертывания:
Первоначальная настройка:
# Clone the repository
git clone https://github.com/kirill-markin/weaviate-mcp-server.git
cd weaviate-mcp-server
# Create environment file
cp .env.example .envСоберите и запустите с помощью Docker Compose:
# 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 downСервер будет доступен по адресу:
SSE-адрес: http://localhost:8000/sse
Быстрая проверка:
# Test the server endpoint
curl -i http://localhost:8000/sseПодключение к Cursor IDE:
Откройте Настройки Cursor → Функции
Добавьте новый MCP-сервер
Тип: Выберите «sse»
URL: Введите
http://localhost:8000/sse
Традиционная настройка
Сначала установите менеджер пакетов uv:
# Install uv on macOS
brew install uv
# Or install via pip (any OS)
pip install uvЗапустите сервер, используя либо stdio (по умолчанию), либо SSE-транспорт:
# 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 -vПосле установки вы можете подключить сервер напрямую к Cursor IDE:
Щёлкните правой кнопкой мыши по файлу
cursor-run-mcp-server.shв CursorВыберите «Copy Path», чтобы скопировать абсолютный путь
Откройте Настройки Cursor (значок шестерёнки)
Перейдите на вкладку «Функции»
Прокрутите вниз до «MCP Servers»
Нажмите «Добавить новый MCP-сервер»
Заполните форму:
Имя: Выберите любое имя (например, «my-mcp-server-1»)
Тип: Выберите «stdio» (не «sse», так как мы запускаем сервер локально)
Команда: Вставьте абсолютный путь к
cursor-run-mcp-server.sh, который вы скопировали ранее. Например:/Users/yourname/weaviate-mcp-server/cursor-run-mcp-server.sh
Переменные окружения
Доступные переменные окружения (можно задать в .env):
MCP_SERVER_PORT(по умолчанию: 8000) — порт для запуска сервераMCP_SERVER_HOST(по умолчанию: 0.0.0.0) — хост для привязки сервераDEBUG(по умолчанию: false) — включить режим отладкиMCP_USER_AGENT— пользовательский User-Agent для получения веб-сайтов
Дополнительные параметры
Установка через Smithery
Чтобы установить MCP Server Template for Cursor IDE для Claude Desktop автоматически через Smithery:
npx -y @smithery/cli install @kirill-markin/example-mcp-server --client claudeОбзор сервера Glama
Available Tools
2 toolsmcp_fetchC
Fetches a website and returns its content
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch |
TDQS
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 mentions fetching and returning content, but doesn't cover important aspects like error handling, rate limits, authentication needs, timeouts, or what happens with invalid URLs. This leaves significant gaps for a tool that interacts with external resources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that directly states the tool's function. Every word earns its place, with no unnecessary elaboration or repetition. It's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that fetches external websites with no annotations and no output schema, the description is insufficient. It doesn't explain what format the content is returned in (HTML, text, etc.), potential limitations, error conditions, or security considerations. The lack of output schema means the description should compensate by explaining return values, which it doesn't do.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'url' clearly documented in the schema. The description doesn't add any meaningful parameter information beyond what's already in the schema, so it meets the baseline for high schema coverage but doesn't provide additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('fetches') and resource ('website'), and specifies the outcome ('returns its content'). However, it doesn't differentiate from the sibling tool 'mood', which appears unrelated but could have overlapping functionality in some contexts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives or in what context it should be applied. The description only states what it does, not when it's appropriate or when other tools might be better suited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
moodB
Ask the server about its mood - it's always happy!
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Ask 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
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 reveals the server will 'always respond with a cheerful message and a heart ❤️' which indicates predictable, positive output. However, it doesn't disclose other behavioral traits like response format details, potential errors, or interaction patterns beyond the basic promise.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just one sentence, with zero wasted words. It's front-loaded with the core purpose and includes the behavioral promise efficiently. Every part of the single sentence earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 minimally complete. It states what the tool does and the expected response behavior. However, it lacks context about why this tool exists alongside 'mcp_fetch' or what use cases it serves, leaving gaps in overall understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single 'question' parameter with examples. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask the server about its mood' which is a specific verb+resource combination. It distinguishes from the sibling tool 'mcp_fetch' by focusing on mood inquiry rather than data fetching. However, it doesn't fully specify what 'mood' means in this context beyond 'always happy'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 mentions the server's mood is 'always happy,' it doesn't explain when this inquiry is appropriate or what scenarios warrant using this tool over 'mcp_fetch' or other potential tools. No explicit when/when-not instructions are provided.
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. Dates show when Glama detected each change.
2 tool updates
- First observed
mcp_fetch - First observed
mood
TDQS
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.
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.
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.
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
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