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Ahmedelmonady

Gameball MCP Server

Docs: Generate Code Example

docs-generate-code-example

Generate code examples for Gameball API endpoints in your chosen language, including JavaScript, Python, C#, Go, PHP, or Java. Provide endpoint, method, and request body to get ready-to-use code.

Instructions

Generate a code example for a Gameball API endpoint in a specific programming language. Use when the user asks for code in a specific language (not cURL, which is already included in docs-get-api-endpoint).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoOptional JSON request body to include
methodYesHTTP method: GET, POST, PUT, DELETE
endpointYesAPI path, e.g. "/api/v4.0/integrations/customers"
languageYesLanguage: javascript, python, csharp, go, php, java
Behavior3/5

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

No annotations are provided, so the description carries full burden. It clearly states the core action but does not disclose behavioral details such as the exact output format, whether it returns only a code snippet or includes explanatory text, or any limitations (e.g., unsupported languages). It adds some context by referencing docs-get-api-endpoint, but this is more usage guidance than behavioral transparency.

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 two sentences, front-loaded with the core purpose and followed by a useful usage distinction. No fluff or redundancy.

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

Completeness4/5

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

Given the low complexity (4 simple params, no output schema, no nested objects), the description is largely complete. It explains what it does, when to use it, and distinguishes it from the sibling. The only minor gap is a lack of description of the generated output's form (e.g., just a code snippet, or with explanation), but this is not critical for such a focused tool.

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 parameters are already well-documented. The description does not add additional meaning to any parameter beyond what the schema provides, which warrants the baseline score of 3.

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 verb+resource: 'Generate a code example for a Gameball API endpoint in a specific programming language.' It clearly distinguishes from sibling docs-get-api-endpoint by noting that cURL is already covered there. This makes the purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly says 'Use when the user asks for code in a specific language' and contrasts with cURL, which is 'already included in docs-get-api-endpoint.' This provides clear when-to-use guidance and names the alternative tool.

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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