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CodeDreamer06

Unstop MCP Server

search_hackathons

Find hackathons on Unstop using filters for status, region, payment, team size, and user type, with sorting, pagination, and cache options.

Instructions

Search Unstop hackathons with filters, sorting, pagination, and optional cache usage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
oppstatusNo
regionNo
paymentNo
teamsizeNo
usertypeNo
sortNo
directionNo
searchNo
pageNo
per_pageNo
use_cacheNo

Implementation Reference

  • Registration of the 'search_hackathons' tool in the MCP server.
    @server.tool(
        name="search_hackathons",
        description="Search Unstop hackathons with filters, sorting, pagination, and optional cache usage.",
    )
    def search_hackathons(
        oppstatus: str | None = None,
        region: str | None = None,
        payment: str | None = None,
        teamsize: int | None = None,
        usertype: str | None = None,
        sort: str | None = None,
        direction: str | None = None,
        search: str | None = None,
        page: int = 1,
        per_page: int = 18,
        use_cache: bool = True,
    ) -> dict:
        try:
            query = SearchHackathonsInput(
                oppstatus=oppstatus,
                region=region,
                payment=payment,
                teamsize=teamsize,
                usertype=usertype,
                sort=sort,
                direction=direction,
                search=search,
                page=page,
                per_page=per_page,
                use_cache=use_cache,
            )
            return service.search_hackathons(query).model_dump(mode="json")
        except (UnstopValidationError, UnstopAPIError, ValueError) as exc:
            raise ValueError(str(exc)) from exc
  • Implementation of the 'search_hackathons' logic in the service class.
    def search_hackathons(self, query: SearchHackathonsInput) -> SearchResponse:
        cache_eligible = query.use_cache and query.oppstatus in (None, "open")
    
        if cache_eligible:
            self.ensure_cache()
            filtered = self.filter_cached(
                region=query.region,
                payment=query.payment,
                teamsize=query.teamsize,
                usertype=query.usertype,
                search=query.search,
            )
            sorted_data = self.sort_data(filtered, query.sort, query.direction)
            return self.build_search_response(
                sorted_data,
                page=query.page,
                per_page=query.per_page,
                applied_filters=query.model_dump(),
                is_cached_result=True,
            )
    
        params = self.build_params(**query.model_dump())
        raw = self.request(params)
        result = self.parse_response(raw)
        return self.build_search_response(
            result["data"],
            page=result["current_page"],
            per_page=result["per_page"],
            total_override=result["total"],
            applied_filters=query.model_dump(),
            is_cached_result=False,
            has_more_override=result["has_more"],
            last_page_override=result["last_page"],
        )
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions 'optional cache usage' but doesn't explain cache behavior, rate limits, authentication needs, or what happens when cache is enabled/disabled. This leaves significant gaps for a search tool with 11 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. However, given the complexity (11 parameters, no schema descriptions), it might be too brief to be fully helpful, though it avoids unnecessary verbosity.

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?

For a tool with 11 parameters, 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain return values, error conditions, or the semantics of key parameters, leaving the agent with insufficient information to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so parameters like 'oppstatus', 'region', 'payment', etc., are completely undocumented in the schema. The description only generically mentions 'filters, sorting, pagination, and optional cache usage', failing to explain what each parameter means or how they interact, providing minimal compensation for the coverage gap.

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 ('Search') and resource ('Unstop hackathons'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'search_hackathons_by_location', which could cause confusion about when to use each.

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?

No explicit guidance is provided on when to use this tool versus alternatives like 'search_hackathons_by_location'. The description mentions 'filters, sorting, pagination, and optional cache usage', but this describes capabilities rather than usage context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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