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lead_scorer

Score and prioritize sales leads based on company data, industry, size, budget, and engagement metrics to identify high-potential prospects.

Instructions

Scores and prioritizes leads. Requires payment (0.001 SOL).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNameYes
industryYes
companySizeYes
budgetNo
engagementNo
notesNo
signatureNoSolana transaction signature
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions payment requirement (0.001 SOL) which is useful context about cost, but doesn't describe what the scoring output looks like, how prioritization works, whether this is a read-only analysis or creates records, or any rate limits/authentication needs. For a 7-parameter tool with complex nested objects, 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.

Conciseness5/5

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

The description is extremely concise at just two short sentences with zero wasted words. It's front-loaded with the core purpose followed by a critical prerequisite. Every sentence earns its place by providing essential information about what the tool does and its cost requirement.

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 complexity (7 parameters with nested objects), no annotations, no output schema, and low schema coverage (14%), the description is incomplete. It doesn't explain what the scoring output looks like, how to interpret results, what the prioritization criteria are, or provide sufficient parameter guidance. The payment requirement is helpful but doesn't compensate for other major gaps.

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 only 14% (only the 'signature' parameter has a description). The description doesn't add any parameter meaning beyond what's in the schema - it doesn't explain what 'companySize' values are expected, what 'budget' format to use, how 'engagement' metrics affect scoring, or what 'notes' should contain. For 7 parameters with low schema coverage, the description fails to compensate.

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: 'Scores and prioritizes leads' - a specific verb+resource combination. It distinguishes from most sibling tools (e.g., create_invoice, pitch_deck) by focusing on lead evaluation rather than document generation or contract checking. However, it doesn't explicitly differentiate from cv_screener which also involves scoring/evaluation.

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 minimal guidance: 'Requires payment (0.001 SOL)' indicates a prerequisite but doesn't explain when to use this tool versus alternatives. No context about when this scoring is appropriate versus other lead management approaches, and no mention of sibling tools like cv_screener that might handle different scoring scenarios.

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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