Click 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., "@Unstop MCP ServerFind online hackathons for college students with the highest prizes"
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.
Unstop MCP Server
unstop-mcp is a Python Model Context Protocol (MCP) server for discovering Unstop hackathons.
It exposes:
MCP tools for searching hackathons, fetching event details, running location-based discovery, and managing cache state
MCP resources for read-only snapshots and per-hackathon lookups
MCP prompts that help an LLM plan searches, compare hackathons, and recommend relevant events
This repository is now MCP-first. The old direct-import Python wrapper API is not the supported public interface anymore.
What It Supports
stdiotransport onlyUnstop hackathons only in v1
Automatic caching of open hackathons for fast repeated lookups
Detail enrichment for descriptions, rounds, contacts, registration counts, and views
Optional location-based search using geocoding
Deterministic unit tests with mocked upstream behavior
Optional live smoke tests kept separate from the default suite
Requirements
Python 3.12+
uvrecommended for local developmentNetwork access for real Unstop calls
Installation
Option 1: uv workflow
git clone https://github.com/your-org/unstop-mcp.git
cd unstop-mcp
uv venv
source .venv/bin/activate
uv pip install -e .Option 2: plain pip
git clone https://github.com/your-org/unstop-mcp.git
cd unstop-mcp
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .Verify the install
unstop-mcp --helpYou should see the stdio transport option.
Running The Server
Run the server locally over stdio:
unstop-mcpEquivalent module form:
python -m unstop_mcpThe server intentionally supports only:
unstop-mcp --transport stdioMCP Surface
Tools
search_hackathons
Search Unstop hackathons with filters, sorting, pagination, and optional cache usage.
Arguments:
oppstatus:open | recent | expired | closedregion:online | offlinepayment:paid | unpaidteamsize:1 | 2 | 3usertype:college_students | fresher | professionals | school_studentssort:prize | days_leftdirection:asc | descsearch: free-text keyword searchpage:>= 1per_page:1-100use_cache:true | false
Returns a structured object with:
items: normalized hackathon summariespagination: total/current page/last page/per-page/has-morecache: cache freshness metadataapplied_filters: the validated input used for the call
get_hackathon_details
Fetch full details for a single hackathon by numeric ID.
Arguments:
hackathon_id
Returns:
item: normalized detail viewcache: metadata describing current server cache state
search_hackathons_by_location
Find offline hackathons near a location using geocoding and radius filtering.
Arguments:
locationradius_kmregionpaymentteamsizeusertypesearchsort:prize | days_left | distancedirectionpageper_page
Returns the same normalized structure as search_hackathons, plus:
location.search_locationlocation.search_radius_kmlocation.search_coordinates
refresh_cache
Force a rebuild of the cached open-hackathon dataset.
Returns:
cache
get_cache_info
Inspect cache state without rebuilding the full dataset.
Returns:
cache
Resources
unstop://cache/info
Read-only JSON view of cache freshness, TTL, and item counts.
unstop://hackathons/open
Read-only JSON snapshot of all cached open hackathons.
unstop://hackathons/{hackathon_id}
Read-only JSON detail view for a specific hackathon. The server serves cached data when possible and falls back to a direct detail fetch when needed.
Prompts
find_relevant_hackathons
Guides an LLM to gather missing user preferences, call the right search tool, and summarize the best matches.
Arguments:
user_goal
compare_hackathons
Guides an LLM to fetch multiple hackathons and compare them.
Arguments:
hackathon_ids
plan_hackathon_search
Guides an LLM on which tools and resources to call, in what order, for a discovery task.
Arguments:
user_request
Output Shape
Tool and resource results are normalized into stable JSON-friendly fields rather than returning raw upstream Unstop payloads as the primary contract.
Each hackathon item includes:
idtitlestatusregionis_paidpublic_urldescriptionprize_amountprize_summaryfiltersrequired_skillsorganisationaddressregistrationroundscontactsdistance_kmwhen applicable
This keeps the MCP contract predictable even if upstream response shapes vary.
Client Setup
Generic MCP client
Point your MCP client at this command:
unstop-mcpIf your client needs an absolute command path, use the one from your environment, for example:
/absolute/path/to/.venv/bin/unstop-mcpClaude Desktop
Example claude_desktop_config.json entry:
{
"mcpServers": {
"unstop": {
"command": "/absolute/path/to/.venv/bin/unstop-mcp",
"args": []
}
}
}Codex
Configure a local MCP server entry that launches:
{
"command": "/absolute/path/to/.venv/bin/unstop-mcp",
"args": []
}If your Codex setup manages MCP servers through a separate config file or UI, use the same command and no extra arguments.
Development
Project layout
src/unstop_mcp/server.py: FastMCP server definition and MCP registrationsrc/unstop_mcp/service.py: Unstop fetching, caching, normalization, and geocoding logicsrc/unstop_mcp/schemas.py: validated inputs and normalized output modelssrc/unstop_mcp/config.py: environment-driven runtime configurationtests/: unit, MCP registration, stdio smoke, and optional live tests
Environment variables
These are optional:
UNSTOP_MCP_TIMEOUTUNSTOP_MCP_MAX_RETRIESUNSTOP_MCP_RETRY_DELAYUNSTOP_MCP_CACHE_TTL_SECONDSUNSTOP_MCP_DETAIL_WORKERSUNSTOP_MCP_DETAIL_DELAYUNSTOP_MCP_GEOCACHE_PATHUNSTOP_MCP_USER_AGENT
How caching works
Searches for open hackathons can use an in-memory snapshot instead of hitting Unstop on every call
The snapshot is rebuilt automatically when stale
refresh_cacheforces an immediate rebuildThe geocode cache is persisted to disk so repeated location lookups do not re-query the geocoder unnecessarily
Geocoding behavior
Location search depends on
geopyOffline hackathons with explicit coordinates are used directly
When coordinates are missing, the server tries multiple address variants and caches the result
Online-only hackathons are excluded from radius searches unless the query explicitly requests
region="online"
Testing
Run the default test suite:
python -m pytest -qWhat the default suite covers:
validation and parsing
normalized search responses
cache metadata and staleness behavior
location filtering
MCP tool/resource/prompt registration
basic end-to-end
stdiostartup and tool listing
Optional live smoke tests
These are excluded by default:
UNSTOP_MCP_RUN_LIVE_TESTS=1 python -m pytest -q -m liveUse live tests only when you want to verify the current Unstop integration against the real network.
Local Validation
Useful local checks:
python -m pytest -q
python -m unstop_mcp --helpIf you already use an MCP Inspector, point it at the local unstop-mcp command and stdio transport to inspect registered tools, resources, and prompts interactively.
Error Handling
The server validates inputs before making upstream calls.
Common failure cases:
invalid enum values such as unsupported
oppstatusorsortempty or unresolvable location input
transient or permanent upstream Unstop failures
missing geocoding support for location search
When possible, tool failures are surfaced as clear MCP-facing validation or request errors.
Limitations
stdioonly in v1hackathons only in v1
upstream Unstop fields and availability can change
location accuracy depends on source address quality and geocoding quality
cache refresh can take longer than simple detail fetches because it enriches open hackathons in bulk
Extending The Server
If you want to add more Unstop opportunity types later, keep this split:
add new upstream fetch/parse logic in
service.pydefine new validated contracts in
schemas.pyregister new MCP surfaces in
server.py
That keeps transport concerns separate from domain logic.
License
MIT
Resources
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