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crm_lead_qualification

Destructive

Qualify leads against your ICP and assign A-D grades using firmographic data, intent signals, and prior interactions. Receive a fit score, reasoning, and next-best-action.

Instructions

Qualify a lead against your ICP and grade it (A/B/C/D). Pulls firmographic data, intent signals, and prior interactions; returns a fit score, the reasoning, and a suggested next-best-action. Args: message: Free-text objective for the action. lead_id: CRM lead/contact ID. lead_email: Lead email (alternative to lead_id). company_domain: Company domain to enrich from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lead_idNo
messageNo
lead_emailNo
company_domainNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already disclose readOnlyHint=false and destructiveHint=true, so the agent knows this is not a pure read. The description adds useful behavioral context about what data it pulls (firmographic, intent signals, prior interactions) and what it returns, without contradicting the annotations.

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 compact and front-loaded: the first sentence states the action and outcome, the second summarizes data sources and outputs, and the Args block is minimal and structured. Every sentence earns its place.

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?

The presence of an output schema covers return-value expectations, and annotations cover the safety profile. However, with zero required parameters, the description does not clarify whether lead_id, lead_email, company_domain, or some combination must be supplied, which leaves a meaningful invocation ambiguity.

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?

With 0% schema description coverage, the Args section provides necessary semantics: lead_email is labeled as an alternative to lead_id, and company_domain is described as the enrichment source. It still leaves the required combination of identifiers ambiguous, so it does not reach a 5.

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 names a specific verb ('Qualify'), a specific resource ('lead'), and the evaluation framework ('against your ICP and grade it A/B/C/D'), plus concrete return values. This clearly differentiates it from sibling tools like crm_verify_lead or crm_enrich_lead.

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?

The description implies when to use it—when a lead needs ICP-based qualification and grading—but it never explicitly states when not to use it or names alternatives. An agent must infer boundaries against siblings such as crm_verify_lead, crm_strategize_lead, and crm_icp_intelligence.

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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