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k-edge

mcp-graphql-bridge

by k-edge

query__language

Fetch language data from a GraphQL API using its ID. Provide the ID and, optionally, a selection set to control which fields are returned.

Instructions

[QUERY] language

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes(ID!)
__fieldsNoGraphQL selection set for the return type, e.g. '{ id name description }'. If omitted, only scalar/id fields are returned.
bearer_tokenNoBearer token to authenticate this request (overrides GRAPHQL_TOKEN)
custom_headersNoAdditional request headers as key-value pairs, e.g. {"X-Tenant-ID": "abc"}
Behavior1/5

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

There are no annotations, so the description carries full responsibility for behavioral disclosure. It discloses nothing about authentication requirements, error behavior, rate limits, mutation safety, or what the query returns. The bare query prefix gives the agent no meaningful behavioral understanding.

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?

"[QUERY] language" is extremely short, but this is under-specification rather than effective conciseness. It contains no useful content and earns no structural credit for front-loading meaningful information.

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?

This tool has four parameters, no annotations, no output schema, and a GraphQL-like interface with sibling operations. The description is completely inadequate for an agent to know how to call it correctly, what the response looks like, or how it relates to query__languages and execute_graphql.

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 the schema already documents all four parameters, including the required code and the optional __fields, bearer_token, and custom_headers. The description adds no extra parameter meaning, but the baseline of 3 applies because the structured schema carries the parameter documentation burden adequately.

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

Purpose1/5

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

The description "[QUERY] language" merely restates the tool name and operation prefix without stating what the tool actually does, what a language is in this context, or how it differs from siblings like query__languages. It provides no specific verb and resource description beyond the name itself, making it essentially a tautology.

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?

No usage guidance is provided. The description does not indicate when to use this tool versus query__languages, query__countries, or get_type_details, nor does it mention any exclusions or alternatives. An agent is left to infer usage from the name alone.

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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