MCP Hackathon Server
OfficialGSA MCP ハッカソン — サーバーテンプレート
Python で Model Context Protocol (MCP) サーバーを構築するためのすぐに使えるスターターで、IBM Cloud (watsonx Orchestrate) と Databricks 用のデプロイメントキットも含まれています。
FastMCP と uv で構築されています。MCP サーバーを構築したことがない場合は、QUICKSTART.md から始めてください。
MCP サーバーとは?
MCP サーバーは、ツール(モデルが呼び出せる関数)、プロンプト(再利用可能な会話のきっかけ)、リソース(モデルが読み取れるデータ)を、Claude Desktop、Claude Code、または watsonx Orchestrate のようなエージェントプラットフォームなどの AI クライアントに公開します。ツールを記述するのはあなたで、クライアントのモデルがいつ呼び出すかを決定します。
このテンプレートには、それぞれの例が1つずつ含まれた動作するサーバーが用意されているので、例を独自のサービスに置き換えてデプロイできます。
Related MCP server: Python MCP Server Template
リポジトリ構成
mcp-hackathon-template/
├── README.md # This file
├── QUICKSTART.md # 5-minute clone → run → connect walkthrough
├── main.py # Local entry point (uv run python main.py)
├── pyproject.toml # Package + dependencies (uv)
├── requirements.txt # Mirror of runtime deps (for buildpack hosts)
├── Dockerfile # Container image (streamable-HTTP, port 8080)
├── manifest.yaml # cloud.gov (Cloud Foundry) deploy
├── server.json # MCP registry metadata
├── .env.example # Copy to .env for local dev
├── .github/workflows/ci.yml # Lint + test on push/PR
├── src/
│ └── example_server/ # ← rename to your service
│ ├── app.py # Thin entry point: builds FastMCP, picks transport
│ ├── config.py # Settings from env vars / .env
│ ├── models.py # Pydantic models & enums for tool params
│ ├── utils.py # Shared helpers (HTTP client, pagination)
│ ├── routes.py # HTTP-only routes (/health, /version)
│ ├── tools/ # ONE FILE PER TOOL
│ │ ├── __init__.py # register_tools(mcp) aggregator
│ │ └── example_tool.py
│ ├── prompts/
│ │ ├── __init__.py # register_prompts(mcp) aggregator
│ │ └── example.py
│ └── resources/
│ ├── __init__.py # register_resources(mcp) aggregator
│ └── example.py
├── tests/ # Import + registration smoke tests
├── eval/ # Stub → build a Phoenix eval harness (see mcp-eval skill)
└── deploy/
├── README.md # Which deployment kit to use
├── ibm/ # watsonx Orchestrate: 3 kits (see below)
└── databricks/ # Databricks Apps kitはじめに
前提条件
uv —
pip install uvまたはbrew install uv
インストールと実行
cp .env.example .env
uv sync
uv run python main.pyサーバーは stdio モードで起動します。これは stdin/stdout を介して JSON-RPC をやり取りするもので、ローカルクライアント(Claude Desktop、Claude Code)がサーバーを起動する方法です。クライアントを接続するには QUICKSTART.md を参照してください。
確認
uv sync --group dev
uv run pytest tests/ -v # tests
uv run ruff check . # lint1ファイルに1ツールのパターン
各ツールは src/example_server/tools/ の下の独自のファイルに置かれ、register(mcp) 関数を公開します。tools/__init__.py は単一の register_tools(mcp) から各ツールを呼び出します。これにより、ツールリストをスキャンしやすくし、2つのファイルに触れるだけで統合を追加または削除できます。
ステップ1 — src/example_server/tools/my_tool.py を作成:
from typing import Annotated
from fastmcp import FastMCP
from example_server.utils import fetch_json
def register(mcp: FastMCP) -> None:
@mcp.tool(
name="example_get_thing",
annotations={
"title": "Get a thing",
"readOnlyHint": True,
"destructiveHint": False,
"idempotentHint": True,
"openWorldHint": True,
},
)
async def get_thing(thing_id: Annotated[str, "The ID to fetch."]) -> dict:
"""One-line summary. Document the data source, its update cadence,
and the return shape here — the model reads this docstring."""
return await fetch_json(f"https://api.example.gov/things/{thing_id}")ステップ2 — tools/__init__.py で配線:
from example_server.tools import example_tool, my_tool
def register_tools(mcp) -> None:
example_tool.register(mcp)
my_tool.register(mcp) # ← add this lineステップ3 — API キーを追加 config.py の型付きフィールドとして追加し、.env.example に環境変数を記載します。
プロンプト(prompts/)とリソース(resources/)も、まったく同じ register(mcp) + アグリゲーターパターンに従います。
パッケージ名の変更
サーバーを公開する前に、example_server を自分のサービス(例: census_mcp)に変更します:
フォルダ
src/example_server/をsrc/<your_name>/に変更します。pyproject.tomlの[project].name、[project.scripts]、[tool.hatch.build.targets.wheel].packagesを更新します。src/、tests/、main.py、Dockerfile、manifest.yaml全体でexample_serverを検索して置換します。
ツール設計のヒント(連邦データ)
散文ではなく構造化データを返す。 一貫したキーを持つ dict やリストを返し、モデルに説明させます。
鮮度を文書化する。 連邦データセットは遅延があります。docstring に更新頻度と「基準日」を明記してください。
ページネーションを公開する。
utils.pyのPaginationParams/paginate()を使用し、has_more/next_offsetを返します。明示的なタイムアウトを使用する。
utils.fetch_jsonはデフォルトで30秒です。実用的なエラーを返す。 生のスタックトレースではなく、
hintを含むエラー dict を返します。
デプロイ
ローカル開発では stdio を使用します。サーバーをエージェントプラットフォームと共有するには、デプロイして登録します。選択肢については deploy/README.md を参照してください:
IBM watsonx Orchestrate — deploy/ibm/(ローカル stdio ツールキット、Code Engine の Git からのビルド、ビルド済みイメージの3つのキット)。
Databricks Apps — deploy/databricks/。
どちらも同じサーバーコードを読み取ります。app.py は、プラットフォームがポートを注入すると自動的に HTTP を提供します。
評価
LLM がツールをどれだけうまく使えるかを測定することは、サーバー品質の真のテストです。このテンプレートには意図的に評価ハーネスは含まれていません。mcp-eval スキルを使用して評価ハーネスを構築する方法については eval/README.md を参照してください。
ライセンス
MIT。脆弱性開示ポリシーとハッカソンのセキュリティメモについては SECURITY.md を参照してください。
Available Tools
1 toolexample_search_datasetsSearch Datasets (example)BRead-onlyIdempotent
Search federal datasets matching a query string. (STUB — replace me.)
This stub shows the shape of a real tool without calling a live API.
Swap the body for an actual request using fetch_json(...); a worked
pattern is included below in a comment.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keywords to search dataset titles and descriptions. | |
| pagination | No | Optional limit/offset (defaults to limit=20, offset=0). | |
| response_format | No | Return machine-readable JSON (default) or human-readable Markdown. | json |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint: false) already establish the safety profile. The description adds one meaningful behavioral fact—'This stub... without calling a live API'—which is honest and useful context. However, it doesn't elaborate on return behavior, result ordering, or error conditions, leaving most of the behavioral burden on the annotations. No contradiction with the annotations is present.
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 text is short and front-loads the meaningful description, which is good. However, roughly three of four lines are implementer-facing stub notes ('(STUB — replace me.)', 'Swap the body...', 'a worked pattern is included below in a comment') that add no value to an agent selecting or invoking the tool. It's not verbose, but those sentences could earn their place better with functional detail.
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 presence of an output schema, return values don't need explanation. The schema plus annotations cover the mechanical calling contract well. However, the description leaves open real-world questions an agent might face, such as what 'federal datasets' covers, and pagination behavior. For an explicitly stubbed example tool, this is adequate, but not complete for a production tool.
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%, and the schema thoroughly documents all three parameters: the required 'query', the pagination object with limits and defaults, and the response_format enum with its default. Per calibration rules, the baseline is 3 when the schema covers everything. The description itself adds no parameter-level insight, which is acceptable at this coverage level.
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 opening sentence 'Search federal datasets matching a query string' uses a specific verb and resource, so an agent immediately knows what the tool does. The remaining stub text is implementer-oriented but does not obscure the purpose. It earns a 4 because it's clear on function, though the nonsensical stub placeholder text prevents a 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?
There is no guidance on when to use this tool versus alternatives, what input it expects conceptually (beyond schema), or any prerequisites or limitations. The stub text discusses implementation ('Swap the body for an actual request') rather than agent-facing usage. While the absence of sibling tools lowers the need for differentiation, the description still provides no real usage context.
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 tool update
v0.1.0- First observed
example_search_datasets
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or overlap. The sole tool has a clear, singular purpose.
With a single tool, there is no inconsistency in naming conventions. The name follows a verb_noun pattern (search_datasets).
A single stub tool is a trivial surface for a server, far below the typical 3-15 tool range. It does not constitute a meaningful tool set.
The server is explicitly a stub with no real functionality, offering only a placeholder search. It has no coverage of any actual domain or workflow.
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