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BACH-AI-Tools

Judge0 CE MCP Server

get_a_language

Retrieve programming language configuration details from the Judge0 CE API to identify supported languages and their execution parameters for code submissions.

Instructions

Get a language.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesExample value: 52
Behavior1/5

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 but fails completely. It doesn't indicate whether this is a read-only operation, whether it requires authentication, what happens on failure, or what the return format might be. The description provides zero behavioral context beyond the basic action implied by 'get'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with just three words, this is an example of harmful under-specification rather than effective brevity. The description is so minimal that it fails to convey necessary information. Every sentence should earn its place, but this single sentence doesn't earn its place by providing meaningful guidance to an AI agent.

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 lack of annotations, no output schema, and a description that provides almost no meaningful information, this description is completely inadequate. For a tool that presumably retrieves specific language data based on an ID, the description should explain what constitutes a 'language' in this system, what information is returned, and how this differs from the sibling 'get_languages' tool. The current description leaves critical gaps.

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?

The input schema has 100% description coverage with the 'id' parameter clearly documented as requiring a string value with an example. The description adds no additional parameter information beyond what the schema already provides. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description.

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

Purpose2/5

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

The description 'Get a language' is a tautology that essentially restates the tool name without adding meaningful specificity. It doesn't clarify what 'language' refers to in this context (programming language, natural language, configuration language) or what 'get' entails (retrieve details, fetch metadata, download resources). While it uses a verb+resource structure, it lacks the differentiation needed to understand what this tool actually does compared to its sibling 'get_languages'.

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

Usage Guidelines1/5

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

The description provides absolutely no guidance about when to use this tool versus alternatives. It doesn't mention the sibling 'get_languages' tool, which appears to retrieve multiple languages, nor does it explain when to use this single-language retrieval versus batch operations like 'get_a_batched_submission'. There's no context about prerequisites, appropriate scenarios, or exclusions.

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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