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Pacific/Apia clock

inspect-robots

Fetch a public robots.txt and return group counts only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref name; discarded after the shape check
urlNoHTTPS URL to normalize or cite
cityNoCity name for a public weather hint; discarded after the call
feedNoPublic RSS or Atom URL; titles discarded
hostNoPublic hostname
jsonNoJSON text to validate; discarded after the check
pathNoFile path to check; no disk access
zoneNoIANA timezone name
queryNoSearch text; discarded after the length check

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations, the description carries full behavioral burden. It claims a network fetch and return of group counts but does not disclose side effects, failure behavior, or what 'group counts' means. The parameter descriptions in the schema repeatedly state 'discarded after the check', implying the tool is actually a shape checker, which contradicts the stated robots.txt fetching purpose. This inconsistency is significant.

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?

The description is a single sentence, extremely concise and front-loaded. It does not waste words. However, the brevity contributes to under-specification, so it is not a full 5, but it earns points for being short and direct.

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

Completeness2/5

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

Given the tool has 9 parameters, no output schema, and no annotations, the description is grossly inadequate. It does not explain the relationship between the parameters and the operation, the format of the output, error conditions, or the meaning of 'group counts'. The mismatch with schema parameter descriptions makes the tool nearly impossible to use correctly.

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

Parameters2/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds no value for parameter understanding. More critically, the schema parameter descriptions (e.g., 'discarded after the shape check') suggest a different tool behavior than the description's robots.txt fetch, actively confusing the agent rather than clarifying parameter roles. The description fails to compensate for the mismatch.

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

Purpose3/5

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

The description states a specific action: fetch a public robots.txt and return group counts. However, the 9 input parameters are entirely generic and do not clearly map to this purpose (e.g., 'city', 'json', 'zone'). The description does not explain how the parameters relate to fetching robots.txt, so an agent cannot confidently select or invoke the tool based on this alone.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus the many shape-checking siblings (e.g., domain-shape, github-repo-shape). The description does not mention any conditions, prerequisites, or alternatives, leaving the agent to guess based solely on the name.

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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