explore_fit_distributionSee how each base resume's match scores are distributed across jobs to identify which roles fit best.
How each base resume's match scores (deterministic engine) are
distributed across jobs. Each row's display_name is the resume's own name
(null for a slug with no row); the slug is an internal identifier.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.11.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful field semantics — that display_name is the resume's own name and is null for a slug with no row, and that the slug is an internal identifier — but says nothing about the granularity of the distribution, ordering, or how scores are bucketed.
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?
Two short sentences with no filler, front-loaded on what is distributed. The parenthetical about the deterministic engine and the display_name note both earn their place, though the sentence flow is slightly choppy.
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?
With no output schema and no parameters, the description must carry the whole burden, and it only partially does: it explains two output fields but not the shape of the result (per-resume rows vs. buckets, score ranges, ordering) an agent needs to interpret a 'distribution'.
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?
The tool takes zero parameters, so the baseline of 4 applies. The description need not document any inputs, and the empty schema is consistent with that.
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?
States a specific subject — per-base-resume match-score distribution across jobs — and clarifies the deterministic engine as the source of the scores. It reads clearly as an analytics/distribution tool, though it never explicitly differentiates itself from the other explore_* siblings (top_skills, role_mix_over_time, skill_heatmap).
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?
There is no when-to-use guidance and no named alternative, despite a crowded explore_* family where an agent must choose among several distribution-style tools. Usage is only inferable from the name and the word 'distribution'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.