Return the GitHub repository URL(s) for a specific data model by name. Use this when the user needs a direct link to the schema source, examples, or documentation on GitHub. Example: get_model_repo_url({"model_name": "WeatherObserved"})
| Name | Required | Description | Default |
|---|---|---|---|
| model_name | Yes | The exact data model name — e.g., 'WeatherObserved', 'OffStreetParking'. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns URL(s) but does not specify whether the output is a string, array, or object, nor what happens if the model name is not found. The phrase 'URL(s)' hints at potential plurality but lacks detail on error behavior or return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a concise two-sentence structure with the main action front-loaded, followed by a usage scenario and an illustrative example. Every sentence earns its place with no redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description adequately covers its purpose and typical use case. However, it falls short of full completeness by not describing the return format or potential error conditions, which would help an agent understand expected results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage for the model_name parameter with a clear description and examples. The tool description's example repeats this usage without adding new semantic information beyond what the schema already states. Baseline 3 applies because schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Return the GitHub repository URL(s) for a specific data model by name.' It names the resource (GitHub repo URL) and the scope (specific data model), distinguishing it from siblings like get_subject_repo_url by focusing on data model names. The example reinforces the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit context for when to use the tool: 'Use this when the user needs a direct link to the schema source, examples, or documentation on GitHub.' It does not explicitly name alternatives or state when not to use it, but the use case is clear enough to direct an agent.
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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