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Text-to-GraphQL MCP Server

Text-to-GraphQL MCP Server

by Arize-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

generate_graphql_query

Converts natural language descriptions into valid GraphQL queries. Provide a query in plain English to get a ready-to-use GraphQL query.

Instructions

Generate a GraphQL query from natural language description

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "generate_graphql_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesNatural language description of the desired GraphQL query
history_idNoOptional history ID to associate with this query

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.1/5.0
Behavior2/5

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

No annotations exist, so description should disclose behavioral traits like dependencies (e.g., schema) or side effects. It does not mention any constraints, rate limits, or AI usage. Minimal transparency.

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 sentence with no unnecessary words. It is appropriately concise, though could include more context without bloating.

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

Completeness2/5

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

Given the existence of an output schema and sibling tools, the description lacks details on expected query type (query/mutation) or integration with other tools. Incomplete for practical agent use.

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 describes both parameters with full coverage (100%). The description adds no extra context beyond what the schema provides. Baseline 3 applies.

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 clearly states the tool's purpose: generating a GraphQL query from natural language. This is distinct from sibling tools which execute, validate, or retrieve examples/history.

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 explicit guidance on when to use this tool vs alternatives. No when-not or alternative recommendations. The description only implies usage without context.

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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