Compute a lead’s total score server-side from caller-supplied component judgments, ensuring the deterministic model calculates the result and does not accept client-provided totals.
Apply the deterministic scoring model. The eight component judgements are supplied by the caller; the total is computed server-side and never accepted as an input.
| Name | Required | Description | Default |
|---|---|---|---|
| scores | Yes | ||
| confirm | No | ||
| dry_run | No | ||
| lead_id | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It usefully discloses that the total is computed server-side and not accepted as input, but it does not state whether scoring mutates the lead, what confirm or dry_run do, or what permissions or side effects are involved.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two front-loaded sentences with no wasted wording. The key constraint—server-computed total—is stated directly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no annotations, no output schema, 0% schema description coverage, and a nested scores object. The description adds one important behavioral constraint, but it is not complete enough for an agent to confidently invoke the tool, especially regarding mutation, confirmation, dry-run behavior, and return value.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for all four parameters. It clarifies that scores contains eight component judgements and that total is server-computed, but it does not explain lead_id, confirm, dry_run, or the structure and valid values of the scores object.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action: applying a deterministic scoring model, with caller-supplied component judgements and server-side total computation. The lead context comes from the name and lead_id rather than the prose, and it does not explicitly differentiate itself from the sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains how the score is calculated but gives no guidance on when to call this tool versus get_lead, update_stage, or other siblings. It also does not explain when to use confirm or dry_run.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.