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GitHub Repo Shape

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

B3.2/5.0
Behavior2/5

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

With no annotations, the description must disclose behavior, but it only says it fetches and returns group counts. It doesn't mention network access, error handling (e.g., missing robots.txt, invalid URL), any rate limits, or that other parameters are ignored. The schema descriptions hint at discarding values, but the tool description itself offers no behavioral context beyond the return type.

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?

The description is a single, tightly worded sentence with no filler. It is front-loaded with the action and resource, and the phrase 'group counts only' immediately sets expectations. Every word contributes meaning, making it highly concise and well-structured.

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?

Despite the schema covering all parameters, the description is incomplete for a tool with 9 optional parameters and no output schema. It fails to identify which parameter is the core input (url) or to explain why other parameters exist (e.g., they are used for shape checks but discarded). There's no mention of error behavior or the format of the returned group counts, leaving the agent with insufficient guidance for correct invocation.

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 description coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning; it doesn't clarify which parameter is the primary target (likely 'url') or that all others are ignored. Since the schema already documents each parameter, the description adds no extra value, keeping it at baseline.

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?

The description states a specific action (Fetch), a specific resource (public robots.txt), and the exact return (group counts only). This clearly distinguishes it from generic fetch tools like web-fetch or fetch-status, and the 'public' qualifier clarifies scope. It is not a tautology and names the output precisely.

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 given on when to use this tool versus alternatives. It doesn't mention that it's only for robots.txt, nor does it exclude cases like private robots or non-robots content. There is no reference to sibling tools like web-fetch or fetch-status, leaving the agent to infer the appropriate context.

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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