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Agent.ai MCP Server

by OnStartups

lead_qualifier_render_qualification_html

Convert qualification data into a professional HTML report. Ideal for generating formatted output without relying on language models.

Instructions

Renders qualification report as professional HTML. No LLM required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qualificationYes{{qualification}}
output_variable_nameYesqualification_html
Behavior3/5

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

The description adds that 'No LLM required,' which is a useful behavioral trait beyond the schema. However, with no annotations provided, the description carries the full burden of disclosing side effects or safety, and it fails to state whether the operation is read-only, modifies state, or has any constraints. It partially compensates but is incomplete.

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 two sentences, very concise and front-loaded. It efficiently conveys the core purpose and a key behavioral note. However, it could include brief parameter details without losing conciseness.

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?

For a simple render tool with no output schema and no annotations, the description lacks key details: what is the return value (HTML string?), how is output_variable_name used, and what is the expected input format for 'qualification'. The agent may be uncertain about how to invoke and use the result.

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

Parameters1/5

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

The description does not explain the two required parameters ('qualification' and 'output_variable_name') at all. Despite 0% schema description coverage, the description adds no meaning regarding parameter format (e.g., JSON string, object) or purpose (e.g., what output_variable_name does). This is a significant gap.

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 that the tool renders a qualification report as professional HTML, using a specific verb ('Renders') and resource ('qualification report'). It distinguishes itself from sibling render tools (e.g., competitive_brief_render_brief_html) by focusing on qualification reports, though it could be more explicit about the input format.

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?

No guidance is provided on when to use this tool versus alternatives like lead_qualifier_qualify_lead or other render tools. The description does not mention prerequisites, context, or exclusions, leaving the agent to 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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