Skip to main content
Glama
hunter-io

Hunter MCP Server

Official
by hunter-io

enrich_company

Retrieve company information from a domain, including industry, description, and headquarters location.

Instructions

Return all the information associated with a domain, such as the industry, the description, or headquarters' location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes

Implementation Reference

  • main.py:32-36 (handler)
    The handler function for the 'enrich_company' MCP tool. It uses HunterAPIClient to fetch company information from the 'companies/find' endpoint given a domain and returns the response as a string.
    @mcp.tool(description="Return all the information associated with a domain, such as the industry, the description, or headquarters' location.")
    async def enrich_company(domain: str) -> str:
        async with HunterAPIClient() as client:
            response = await client.get("companies/find", {"domain": domain})
            return response
  • main.py:32-32 (registration)
    Registers the enrich_company tool with FastMCP using the @mcp.tool decorator, including the tool description.
    @mcp.tool(description="Return all the information associated with a domain, such as the industry, the description, or headquarters' location.")
  • The get method of HunterAPIClient used by the enrich_company handler to perform the API request to 'companies/find'.
    async def get(self, endpoint: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """
        Performs a GET request to the specified endpoint.
        
        Args:
            endpoint: endpoint path (without leading slash)
            params: Additional request parameters
            
        Returns:
            The JSON response from the API.
            
        Raises:
            httpx.HTTPError: In case of HTTP error
        """
        if not self._client:
            raise RuntimeError("The client must be used in an async with context")
        
        response = await self._client.get(
            f"/{endpoint.lstrip('/')}",
            params=params
        )
        response.raise_for_status()
        return response.json()
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 this is a read operation ('Return'), but doesn't mention permissions, rate limits, error conditions, or what 'all the information' entails (e.g., format, completeness). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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. It avoids unnecessary words, though it could be slightly more structured (e.g., by explicitly listing parameters). Every part of the sentence contributes to understanding the tool's function.

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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'all the information' includes (e.g., data structure, fields), potential limitations, or how results are returned. For a tool that presumably returns complex company data, this leaves too much unspecified for effective agent use.

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 the description must compensate for undocumented parameters. It mentions 'domain' implicitly but doesn't explain what constitutes a valid domain (e.g., format, examples) or add meaning beyond the schema's basic type. With 1 parameter and low coverage, the description provides minimal additional context.

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: 'Return all the information associated with a domain' with specific examples ('industry, description, headquarters' location'). It uses a specific verb ('Return') and resource ('information associated with a domain'), though it doesn't explicitly distinguish from sibling tools like 'domain_search' or 'enrich_email'.

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. It doesn't mention sibling tools like 'domain_search' or 'enrich_email', nor does it specify prerequisites or exclusions for usage. The context is implied (when you need company information from a domain) but not explicitly stated.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hunter-io/hunter-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server