Skip to main content
Glama

Bot Appétit

List the checks

list_checks

List every check Bot Appétit runs on a page, grouped by category, with the id of each. Use the id with explain_check for what a check means and what good looks like. Every check asks one question: can a machine consume this page?

Input Schema

NameRequiredDescriptionDefault
categoryNoOnly checks in this category id, such as "aeo". Leave out for all of them.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the return structure (checks grouped by category, each carrying an id) and the conceptual scope of a check, which is the main thing an agent needs since there is no output schema. It doesn't address ordering, stability of ids, or permissions, but as a read-only listing the risk surface is small.

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?

Three short sentences, front-loaded with what is returned and followed by the actionable id handoff. The closing line ("can a machine consume this page?") is framing rather than invocation guidance, so it is slightly expendable, but it is cheap and clarifying.

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?

With one optional parameter, no output schema, and no annotations, the description covers what an agent needs: what is listed, that ids are included, how categories shape the result, and where to go next. Only minor gaps remain (result ordering, whether the check set is static).

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

Parameters3/5

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

Schema coverage is 100%, so the single `category` parameter is already fully documented with an example ("aeo") and the omit-for-all behavior. The description's "grouped by category" is consistent but adds no syntax or semantics beyond the schema; baseline 3 applies.

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 and resource ("List every check Bot Appétit runs on a page") plus the return shape (grouped by category, with ids). This clearly separates it from explain_check (which explains a single check) and from get_report (which presumably returns results).

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

Usage Guidelines4/5

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

Explicitly routes the agent: take the id from here and pass it to explain_check to learn what a check means and what good looks like. There is no explicit "when not to use" or note about when to prefer get_report, but the handoff to the sibling is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources