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get_feedback_stats

Read public aggregate product-feedback review throughput: counts by status, distinct authors and rolling-day submission/review counts. Aggregates only; never titles, bodies or rationale text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the important trait of being aggregate-only and explicitly excludes titles, bodies, and rationale text, which is genuinely useful privacy/scope context. However, it does not mention whether this is a read-only operation (implied by 'Read'), nor any caching, freshness, or aggregation-window details beyond 'rolling-day'.

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?

Two tight sentences with no filler. The core purpose and the aggregate-only constraint are both front-loaded, and every clause earns its place (status counts, author counts, rolling-day counts, and the exclusion list).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter aggregate read tool with no output schema, the description covers what is returned at a conceptual level and what is deliberately excluded. It stops short of specifying the exact response shape or the aggregation window semantics, but no output schema exists to fill that gap, so a small completeness gap remains.

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

Parameters4/5

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

There are zero parameters, so per the scoring rules the baseline is 4. Schema coverage is 100% and no additional parameter semantics are needed; the description correctly says nothing about inputs because there are none.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (read) and resource (public aggregate product-feedback review throughput), and enumerates exactly what is returned: counts by status, distinct authors, rolling-day submission/review counts. This distinguishes it clearly from siblings like list_feedback and get_feedback, which presumably return individual records rather than aggregates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by declaring it returns aggregates only and never titles/bodies/rationale, which tells the agent when this tool is appropriate (summary-level queries) versus record-level siblings. However, it never explicitly names an alternative tool or states when-not-to-use-it conditions.

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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