Calculate min, max, and average quality scores for a filtered stock universe using sector, market cap, and score range criteria.
Return min/max/average score statistics for a filtered universe.
| Name | Required | Description | Default |
|---|---|---|---|
| sectors | No | ||
| max_score | No | ||
| min_score | No | ||
| max_market_cap_usd | No | ||
| min_market_cap_usd | No |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already discloses the safe read-only nature, so the description's main contribution is naming the exact outputs (min/max/average). It does not disclose behavior when filters are null/omitted, how sector and market-cap filters combine, or whether multiple sectors yield per-sector or pooled statistics. No contradiction with annotations.
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?
A single front-loaded sentence with zero filler: the verb, the computed statistics, and the scope all appear in the opening clause. Every word earns its place, and the length is appropriate for a one-purpose read-only aggregation tool.
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?
There is no output schema and no parameter documentation, so the agent must infer return shape (beyond the three numbers) and filter semantics from names alone. Critical ambiguities—default universe when all filters are null, multi-sector behavior, and filter interaction—are unaddressed, making the one-sentence description insufficient despite the simple surface.
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 carries the burden of explaining the five filter parameters, but it only gestures at them via 'filtered universe.' It does not clarify whether min/max bounds are inclusive, whether filters are ANDed, or how an array of sectors is handled; the parameter names themselves carry the entire semantic load.
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 verb ('Return') and resource ('min/max/average score statistics') over a 'filtered universe,' making the aggregate intent unambiguous. It is internally clear and distinct in function from siblings like scores_list and scores_show, though it never names a sibling or explicitly states what it is not.
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?
No guidance on when to prefer this tool over the five other score-related siblings (scores_list, scores_top, scores_show, scores_for_tickers, scores_market_cap). 'For a filtered universe' weakly implies a filtering use case, but there are no exclusions, prerequisites, or explicit alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.