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chrisboden

MCP Server Template for Cursor IDE

by chrisboden
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

カーソル IDE 用 MCP サーバー テンプレート

モデルコンテキストプロトコル（MCP）を使用してCursor IDE用のカスタムツールを作成するためのシンプルなテンプレートです。このテンプレートから独自のリポジトリを作成し、ツールを変更してCursor IDEに接続します。

サーバーの気分反応

クイックスタート

  1. 「Herokuにデプロイ」ボタンをクリックします

    Herokuへのデプロイ

  2. デプロイ後、カーソルを構成します。

    • カーソル設定を開く→機能

    • 新しいMCPサーバーを追加する

    • Heroku URL を/sseパスとともに使用します (例: https://<your-app-name>.herokuapp.com/sse )

  3. カーソルでエージェントの気分をテストします。

    • エージェントに「サーバーの雰囲気についてお聞きして、どうなっているか教えてください」と依頼してください。

    • サーバーは明るいメッセージとハートで応答します❤️

Related MCP server: MCP Server Template for Cursor IDE

代替設定方法

サーバーは、Docker を使用するか、従来の Python セットアップを使用するか、Cursor IDE で直接実行するかの 3 つの方法で実行できます。

Dockerのセットアップ

このプロジェクトには、簡単に導入できる Docker サポートが含まれています。

  1. 初期設定:

# 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 Compose を使用してビルドおよび実行します。

# 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. サーバーは次の場所で利用できます:

  2. クイックテスト:

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

  1. カーソル IDE に接続します。

    • カーソル設定を開く→機能

    • 新しいMCPサーバーを追加する

    • タイプ: 「sse」を選択

    • URL: http://localhost:8000/sseと入力します

従来のセットアップ

まず、uv パッケージ マネージャーをインストールします。

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

stdio (デフォルト) または SSE トランスポートのいずれかを使用してサーバーを起動します。

# 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

インストール後、サーバーを Cursor IDE に直接接続できます。

  1. カーソル内のcursor-run-mcp-server.shファイルを右クリックします。

  2. 「パスをコピー」を選択して絶対パスをコピーします

  3. カーソル設定を開く（歯車アイコン）

  4. 機能タブに移動する

  5. 「MCPサーバー」までスクロールします。

  6. 「新しいMCPサーバーを追加」をクリックします

  7. フォームに記入してください:

    • 名前: 任意の名前を選択します (例: "my-mcp-server-1")

    • タイプ: 「stdio」を選択します (サーバーをローカルで実行するため、「sse」ではありません)

    • コマンド: 先ほどコピーしたcursor-run-mcp-server.shへの絶対パスを貼り付けます。例: /Users/kirillmarkin/weaviate-mcp-server/cursor-run-mcp-server.sh

環境変数

利用可能な環境変数（ .envで設定できます）:

  • MCP_SERVER_PORT (デフォルト: 8000) - サーバーを実行するポート

  • MCP_SERVER_HOST (デフォルト: 0.0.0.0) - サーバーをバインドするホスト

  • DEBUG (デフォルト: false) - デバッグモードを有効にする

  • MCP_USER_AGENT - ウェブサイト取得用のカスタム ユーザー エージェント

追加オプション

Smithery経由でインストール

鍛冶屋のバッジ

Smithery経由で Claude Desktop の Cursor IDE 用の MCP サーバー テンプレートを自動的にインストールするには:

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

Glamaサーバーのレビュー

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
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

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

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