MCP Hackathon Server
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Hackathon ServerList the example tools and prompts included in this server."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
GSA MCP Hackathon — Server Template
A ready-to-run starter for building a Model Context Protocol (MCP) server in Python, plus deployment kits for IBM Cloud (watsonx Orchestrate) and Databricks.
Built with FastMCP and uv. If you have never built an MCP server before, start with QUICKSTART.md.
What is an MCP server?
An MCP server exposes tools (functions the model can call), prompts (reusable conversation starters), and resources (data the model can read) to an AI client such as Claude Desktop, Claude Code, or an agent platform like watsonx Orchestrate. You write the tools; the client's model decides when to call them.
This template gives you a working server with one example of each, so you can replace the examples with your own service and deploy.
Related MCP server: Python MCP Server Template
Repo structure
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 kitGetting started
Prerequisites
uv —
pip install uvorbrew install uv
Install and run
cp .env.example .env
uv sync
uv run python main.pyThe server starts in stdio mode — it talks JSON-RPC over stdin/stdout, which is how local clients (Claude Desktop, Claude Code) launch it. See QUICKSTART.md to connect a client.
Verify
uv sync --group dev
uv run pytest tests/ -v # tests
uv run ruff check . # lintThe one-tool-per-file pattern
Each tool lives in its own file under src/example_server/tools/ and exposes a register(mcp) function. tools/__init__.py calls each one from a single register_tools(mcp). This keeps the tool list scannable and lets you add or remove an integration by touching two files.
Step 1 — create src/example_server/tools/my_tool.py:
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}")Step 2 — wire it up in tools/__init__.py:
from example_server.tools import example_tool, my_tool
def register_tools(mcp) -> None:
example_tool.register(mcp)
my_tool.register(mcp) # ← add this lineStep 3 — add any API key as a typed field in config.py and document the env var in .env.example.
Prompts (prompts/) and resources (resources/) follow the exact same register(mcp) + aggregator pattern.
Rename the package
Before publishing your server, rename example_server to your service (e.g. census_mcp):
Rename the folder
src/example_server/→src/<your_name>/.Update
pyproject.toml: the[project].name,[project.scripts], and[tool.hatch.build.targets.wheel].packages.Find-and-replace
example_serveracrosssrc/,tests/,main.py,Dockerfile, andmanifest.yaml.
Tool design tips (federal data)
Return structured data, not prose. Return dicts/lists with consistent keys and let the model narrate.
Document freshness. Federal datasets lag; state the update frequency and "as-of" date in the docstring.
Expose pagination. Use
PaginationParams/paginate()fromutils.py, and returnhas_more/next_offset.Use explicit timeouts.
utils.fetch_jsondefaults to 30s.Actionable errors. Return an error dict with a
hint, not a raw stack trace.
Deploying
Local development uses stdio. To share your server with an agent platform, deploy it and register it. See deploy/README.md for a chooser, then:
IBM watsonx Orchestrate — deploy/ibm/ (three kits: local stdio toolkit, Code Engine build-from-Git, and prebuilt image).
Databricks Apps — deploy/databricks/.
Both read the same server code; app.py automatically serves HTTP when the platform injects a port.
Evaluations
Measuring how well an LLM can use your tools is the real test of server quality. This template intentionally does not ship an eval harness — see eval/README.md for how to build one with the mcp-eval skill.
License
MIT. See SECURITY.md for the vulnerability disclosure policy and hackathon security notes.
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. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
example_search_datasets
TDQS
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.
Maintenance
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