MCP Server Template for Cursor IDE
Plantilla de servidor MCP para Cursor IDE
Una plantilla sencilla para crear herramientas personalizadas para Cursor IDE mediante el Protocolo de Contexto de Modelo (MCP). Cree su propio repositorio a partir de esta plantilla, modifique las herramientas y conéctelas a su Cursor IDE.

Inicio rápido
Haga clic en el botón "Implementar en Heroku".
Después de la implementación, configure Cursor:
Abrir Configuración del cursor → Características
Agregar nuevo servidor MCP
Utilice su URL de Heroku con la ruta
/sse(por ejemplo,https://<your-app-name>.herokuapp.com/sse)
Pon a prueba el estado de ánimo de tu agente en Cursor:
Pregúntele a su agente: "Por favor, pregunte sobre el estado de ánimo de nuestro servidor y déjeme saber cómo está".
El servidor responderá con un mensaje alegre y un corazón ❤️
Related MCP server: MCP Server Template for Cursor IDE
Métodos de configuración alternativos
Puede ejecutar el servidor de tres maneras: usando Docker, la configuración tradicional de Python o directamente en Cursor IDE.
Configuración de Docker
El proyecto incluye soporte para Docker para una fácil implementación:
Configuración inicial:
# Clone the repository
git clone https://github.com/kirill-markin/weaviate-mcp-server.git
cd weaviate-mcp-server
# Create environment file
cp .env.example .envConstruya y ejecute usando 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 downEl servidor estará disponible en:
Punto final SSE: http://localhost:8000/sse
Prueba rápida:
# Test the server endpoint
curl -i http://localhost:8000/sseConectarse a Cursor IDE:
Abrir Configuración del cursor → Características
Agregar nuevo servidor MCP
Tipo: Seleccione "sse"
URL: Ingrese
http://localhost:8000/sse
Configuración tradicional
Primero, instale el administrador de paquetes uv:
# Install uv on macOS
brew install uv
# Or install via pip (any OS)
pip install uvInicie el servidor utilizando stdio (predeterminado) o el transporte SSE:
# 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 -vDespués de la instalación, puede conectar el servidor directamente a Cursor IDE:
Haga clic derecho en el archivo
cursor-run-mcp-server.shen CursorSeleccione "Copiar ruta" para copiar la ruta absoluta
Abrir la configuración del cursor (icono de engranaje)
Vaya a la pestaña Características
Desplácese hacia abajo hasta "Servidores MCP"
Haga clic en "Agregar nuevo servidor MCP"
Rellene el formulario:
Nombre: Elija cualquier nombre (por ejemplo, "mi-servidor-mcp-1")
Tipo: Seleccione "stdio" (no "sse" porque ejecutamos el servidor localmente)
Comando: Pegue la ruta absoluta de
cursor-run-mcp-server.shque copió anteriormente. Por ejemplo:/Users/kirillmarkin/weaviate-mcp-server/cursor-run-mcp-server.sh
Variables de entorno
Variables de entorno disponibles (se pueden configurar en .env ):
MCP_SERVER_PORT(predeterminado: 8000): puerto donde se ejecutará el servidorMCP_SERVER_HOST(predeterminado: 0.0.0.0): host al que vincular el servidorDEBUG(predeterminado: falso): habilita el modo de depuraciónMCP_USER_AGENT- Agente de usuario personalizado para la obtención de sitios web
Opciones adicionales
Instalación mediante herrería
Para instalar automáticamente la plantilla de servidor MCP para Cursor IDE para Claude Desktop a través de Smithery :
npx -y @smithery/cli install @kirill-markin/example-mcp-server --client claudeAnálisis del servidor 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?
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.
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.
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.
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.
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.
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!
| 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 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.
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.
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.
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.
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.
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.
2 tool updates
- First observed
mcp_fetch - First observed
mood
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one fetches website content, while the other returns a static mood response. There is no overlap or ambiguity between them.
The naming is inconsistent: 'mcp_fetch' uses a prefix and verb_noun pattern, while 'mood' is a single noun with no verb. This mixed convention lacks a predictable pattern.
With only two tools, the server feels thin and under-scoped for a template intended for Cursor IDE, suggesting it might not cover enough functionality for typical IDE use cases.
The tool surface is severely incomplete for an IDE template, lacking core operations like file manipulation, code analysis, or project management. The existing tools do not form a coherent workflow.
Maintenance
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