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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

C2.8/5.0
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 states it parses owner and repo, but does not mention that many input parameters (city, feed, host, json, path, zone, query) are discarded or irrelevant. There is no mention of side effects, return format, or limitations. This is a significant gap for a tool with a large schema.

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 fluff. It is appropriately sized and front-loaded with the core purpose. It could arguably be expanded to cover more behavior, but as a concise statement it is efficient.

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 9 parameters, no output schema, and no annotations, the description is grossly incomplete. It only explains a fraction of the tool's behavior (parsing owner and repo from a URL) while the schema suggests it handles a variety of inputs (weather hints, RSS feeds, JSON validation, timezones, etc.). An agent cannot understand the full scope, what parameters to pass, or what to expect as output. This is a critical gap for a tool with such a complex schema.

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 all 9 parameters are described in the schema. The description adds no parameter-level meaning beyond mentioning 'owner and repo' which are not explicit parameters but extraction targets. Since the schema covers the parameters, the baseline of 3 applies, but the description does not clarify which parameters are actually relevant or how they interact with the parsing behavior.

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

Purpose4/5

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

The description clearly states a specific verb and resource: 'Parse owner and repo from a GitHub URL.' This distinguishes it from sibling shape tools like domain-shape or figma-url-shape by the explicit GitHub context. However, it does not mention any alternative tools or scope limitations, so it lacks sibling differentiation beyond the GitHub URL focus.

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 other shape-checking siblings. No conditions, exclusions, or alternatives are mentioned. The description only states the action without any contextual routing, leaving the agent to infer usage.

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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