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bcharleson

Instantly MCP Server

get_verification_stats_for_lead_list

Read-only

Analyze email verification results for a lead list to identify valid, invalid, risky, and unknown email addresses.

Instructions

Get email verification statistics for a lead list.

Returns breakdown of verification results:

  • Valid emails

  • Invalid/bounced emails

  • Risky emails

  • Unknown status

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function that executes the tool: fetches verification stats via API GET /lead-lists/{list_id}/verification-stats using the provided list_id from params, formats as JSON.
    async def get_verification_stats_for_lead_list(params: GetVerificationStatsInput) -> str:
        """
        Get email verification statistics for a lead list.
        
        Returns breakdown of verification results:
        - Valid emails
        - Invalid/bounced emails
        - Risky emails
        - Unknown status
        """
        client = get_client()
        result = await client.get(f"/lead-lists/{params.list_id}/verification-stats")
        return json.dumps(result, indent=2)
  • Pydantic input schema/model: requires list_id (str). Used for input validation in the handler.
    class GetVerificationStatsInput(BaseModel):
        """Input for getting email verification stats for a list."""
        
        model_config = ConfigDict(str_strip_whitespace=True, extra="ignore")
        
        list_id: str = Field(..., description="List UUID")
  • Registration: The tool function is included in the LEAD_TOOLS list, which is collected and registered by server.py.
    LEAD_TOOLS = [
        list_leads,
        get_lead,
        create_lead,
        update_lead,
        list_lead_lists,
        create_lead_list,
        update_lead_list,
        get_verification_stats_for_lead_list,
        add_leads_to_campaign_or_list_bulk,
        delete_lead,
        delete_lead_list,
        move_leads_to_campaign_or_list,
    ]
  • MCP annotation registration: Marks the tool as read-only in the TOOL_ANNOTATIONS dict used during tool registration.
    "get_verification_stats_for_lead_list": {"readOnlyHint": True},
Behavior3/5

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

The annotations provide readOnlyHint=true, indicating this is a safe read operation. The description adds value by detailing the return breakdown (valid, invalid, risky, unknown emails), which goes beyond the annotations. However, it doesn't disclose other behavioral traits like rate limits, authentication needs, or data freshness, leaving some gaps in transparency.

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

Conciseness5/5

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

The description is front-loaded with the core purpose in the first sentence, followed by a bulleted list of return values that adds necessary detail without redundancy. It's appropriately sized, with every sentence earning its place by clarifying output semantics, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter) and the presence of an output schema (implied by 'Has output schema: true'), the description is reasonably complete. It explains what the tool does and the breakdown of results, which complements the structured data. However, it lacks usage context and some behavioral details, slightly reducing completeness for a read operation.

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

Parameters3/5

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

The input schema has 0% description coverage, with only one parameter 'list_id' documented as 'List UUID' in the schema. The description doesn't add any parameter-specific details beyond implying it operates on a 'lead list'. Since schema coverage is low, the description compensates minimally by clarifying the tool's overall function, but doesn't explain parameter usage or constraints, meeting the baseline for moderate schema coverage.

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: 'Get email verification statistics for a lead list.' It specifies the verb ('Get'), resource ('email verification statistics'), and target ('lead list'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'get_lead' or 'list_lead_lists', which also retrieve lead-related data, so it misses full sibling distinction.

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 prerequisites (e.g., needing an existing lead list), exclusions, or compare it to siblings like 'get_lead' or 'list_leads', which might offer overlapping functionality. Without such context, users must infer usage from the name alone.

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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