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chrisboden

MCP Server Template for Cursor IDE

by chrisboden
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

MCP Server Template for Cursor IDE

A simple template for creating custom tools for Cursor IDE using Model Context Protocol (MCP). Create your own repository from this template, modify the tools, and connect them to your Cursor IDE.

Server Mood Response

Quick Start

  1. Click "Deploy to Heroku" button

    Deploy to Heroku

  2. After deployment, configure Cursor:

    • Open Cursor Settings → Features

    • Add new MCP server

    • Use your Heroku URL with /sse path (e.g., https://<your-app-name>.herokuapp.com/sse)

  3. Test your agent's mood in Cursor:

    • Ask your agent "Please ask about our server mood and let me know how it is."

    • The server will respond with a cheerful message and a heart ❤️

Related MCP server: MCP Server Template for Cursor IDE

Alternative Setup Methods

You can run the server in three ways: using Docker, traditional Python setup, or directly in Cursor IDE.

Docker Setup

The project includes Docker support for easy deployment:

  1. Initial setup:

# 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. Build and run using 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 down

  1. The server will be available at:

  2. Quick test:

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

  1. Connect to Cursor IDE:

    • Open Cursor Settings → Features

    • Add new MCP server

    • Type: Select "sse"

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

Traditional Setup

First, install the uv package manager:

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

Start the server using either stdio (default) or SSE transport:

# 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

After installation, you can connect the server directly to Cursor IDE:

  1. Right-click on the cursor-run-mcp-server.sh file in Cursor

  2. Select "Copy Path" to copy the absolute path

  3. Open Cursor Settings (gear icon)

  4. Navigate to Features tab

  5. Scroll down to "MCP Servers"

  6. Click "Add new MCP server"

  7. Fill in the form:

    • Name: Choose any name (e.g., "my-mcp-server-1")

    • Type: Select "stdio" (not "sse" because we run the server locally)

    • Command: Paste the absolute path to cursor-run-mcp-server.sh that you copied earlier. For example: /Users/kirillmarkin/weaviate-mcp-server/cursor-run-mcp-server.sh

Environment Variables

Available environment variables (can be set in .env):

  • MCP_SERVER_PORT (default: 8000) - Port to run the server on

  • MCP_SERVER_HOST (default: 0.0.0.0) - Host to bind the server to

  • DEBUG (default: false) - Enable debug mode

  • MCP_USER_AGENT - Custom User-Agent for website fetching

Additional options

Installing via Smithery

To install MCP Server Template for Cursor IDE for Claude Desktop automatically via Smithery:

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

Glama server review

Available Tools

2 tools
mcp_fetchC

Fetches a website and returns its content

ParametersJSON Schema
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!

ParametersJSON Schema
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
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TDQS

B3/5.0

Scored across 2 tools

Disambiguation5/5

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.

Naming Consistency2/5

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.

Tool Count2/5

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.

Completeness2/5

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

ActivityInactive
ResponsivenessNo issues

Resources

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