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Find cryptocurrency and blockchain project data using keywords for project names, tokens, or related terms to access brief information about projects, venture capital firms, and people in the industry.

Instructions

Search for Project/VC/People brief information according to keywords.

Args:
    query: Search keywords, which can be project/institution names, tokens, or other related terms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:51-73 (handler)
    The handler function for the 'search' MCP tool, registered via @mcp.tool(). It queries the RootData API endpoint 'ser_inv' with the provided keywords and returns formatted JSON results or appropriate error messages.
    @mcp.tool()
    async def search(query: str) -> str:
        """Search for Project/VC/People brief information according to keywords.
    
        Args:
            query: Search keywords, which can be project/institution names, tokens, or other related terms.
        """
        # Prepare request data
        data = {"query": query}
        
        # Fetch data from the API
        response = await make_request("ser_inv", data)
        
        # Check if there was an error
        if "Error" in response:
            return f"Error: {response['Error']}"
        
        # Check if data is found
        if response.get("result") != 200 or not response.get("data"):
            return "No results found for your search query."
        
        # Return the formatted results
        return json.dumps(response["data"], indent=2)
  • Supporting helper function used by the 'search' tool (and others) to perform authenticated asynchronous HTTP POST requests to the RootData API.
    async def make_request(endpoint: str, data: dict) -> dict[str, any] | None:
        """Make a request to the RootData API with proper error handling."""
        headers = {
            "Content-Type": "application/json",
            "language": "en",
        }
        
        if api_key := os.environ.get("ROOTDATA_API_KEY"):
            headers["apikey"] = api_key
        else:
            return {"Error": "ROOTDATA_API_KEY environment variable is not set"}
    
        url = f"{ROOTDATA_API_BASE}/{endpoint}"
        
        async with httpx.AsyncClient() as client:
            try:
                response = await client.post(url, headers=headers, json=data, timeout=30.0)
                response.raise_for_status()
                return response.json()
            except Exception as e:
                return {"Error": str(e)}
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 searches for 'brief information' but doesn't clarify what 'brief' entails, whether results are paginated, if there are rate limits, authentication requirements, or error handling. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded, with the core purpose stated first followed by parameter details. The two-sentence structure is efficient, though the 'Args:' section could be integrated more smoothly. Overall, it avoids unnecessary verbosity while conveying essential information.

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 moderate complexity (search function with one parameter) and the presence of an output schema, the description is minimally adequate. It covers the purpose and parameter semantics but lacks usage guidelines and behavioral details. The output schema likely handles return values, reducing the burden on the description, but more context on tool behavior would improve completeness.

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

Parameters4/5

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

The description adds meaningful context beyond the input schema. The schema has 0% description coverage, providing only a basic string parameter 'query'. The description compensates by explaining that 'query' can include 'project/institution names, tokens, or other related terms', which clarifies the expected content and scope of the search parameter.

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: 'Search for Project/VC/People brief information according to keywords.' It specifies the verb ('search') and resource types (Project/VC/People), though it doesn't explicitly differentiate from sibling tools like 'get_organization' and 'get_project'. The purpose is clear but lacks sibling differentiation.

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 like 'get_organization' or 'get_project'. It mentions what can be searched (project/institution names, tokens, etc.) but doesn't indicate scenarios where this search tool is preferred over direct lookup tools. No explicit when/when-not statements or alternatives are provided.

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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