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JSON Lines row count, body discarded

github-repo-shape

Parse owner and repo from a GitHub URL.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only mentions parsing, but does not disclose what happens on invalid URLs, whether the tool performs network calls, what it returns, or that many inputs are discarded. The schema hints at discarding behavior (e.g., 'discarded after the shape check') but the description adds no such transparency.

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, concise sentence with no wasted words. It is appropriately front-loaded with the core action. However, it is so minimal that it sacrifices necessary detail, which slightly reduces the score from 5.

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

Completeness1/5

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

Given the complexity of 9 parameters, no annotations, and no output schema, the description is woefully inadequate. An agent cannot determine which parameters are relevant, how to construct a valid call, what the output format is, or what edge cases exist. The description provides almost no contextual information needed for correct invocation.

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?

The schema coverage is 100%, so baseline is 3, but the description adds zero parameter meaning. Worse, the schema descriptions are inconsistent with the tool purpose: 'url' is described as 'HTTPS URL to normalize or cite' instead of a GitHub URL, and other parameters (city, feed, etc.) have no relation to the stated function. The description does not clarify which parameter is the primary GitHub URL or how others are used.

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 ('parse owner and repo') on a specific resource ('GitHub URL'), which is clear in isolation. However, the input schema lists nine unrelated parameters (city, feed, host, json, path, zone, query, etc.) with no required fields, implying the tool does far more than parse GitHub URLs. This disconnect makes the stated purpose incomplete and potentially misleading about the tool's actual scope.

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?

There is no guidance on when to use this tool versus siblings like 'figma-url-shape' or 'normalize-url'. No preconditions, alternatives, or exclusions are mentioned. The description simply states what it does without contextual cues.

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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