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GrowthPilot

GrowthPilot

by P-Suraj
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

gp_qualify_lead

Calculates a sales qualification fit score for a company against a campaign using AI reasoning.

Instructions

Calculates the sales qualification fit score for a discovered company using Gemini API reasoning.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "company": "<company>",
      "research": "<research>",
      "campaign": "<campaign>"
    },
    "name": "gp_qualify_lead"
  }
}

Input Schema

NameRequiredDescriptionDefault
companyYesThe target Company details
campaignYesThe Campaign parameters
researchYesThe Company web research profile

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description must bear the full burden. It mentions 'using Gemini API reasoning' but does not disclose side effects (is it read-only?), cost implications, rate limits, or whether state is modified. For a tool that likely triggers an external API call, this is a significant gap.

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 a single sentence that conveys the core function without any extraneous words. It efficiently captures what the tool does, which is appropriate for a focused scoring tool.

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?

Despite high schema coverage and no output schema, the description is too minimal. It does not explain what the fit score represents, how it should be interpreted, or what the tool returns. Given the tool computes a qualification score and has no output schema, more contextual details are needed for proper use.

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?

Schema coverage is 100% with basic descriptions for each object (e.g., 'The target Company details'). The description adds the context that these parameters are used to compute a fit score, but does not explain individual fields beyond the schema. This is adequate but adds little extra meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates a sales qualification fit score for a discovered company, using Gemini API reasoning. The verb 'calculates' and resource 'sales qualification fit score' are specific. In context of siblings like gp_discover_companies and gp_research_company, this tool's purpose as the qualification step is distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied from the naming and sibling tools (e.g., gp_research_company before, gp_generate_email_draft after), but no explicit when-to-use or when-not-to-use guidance is provided. The description does not state prerequisites or alternatives, relying on the pipeline ordering.

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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