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Arize-ai

Text-to-GraphQL MCP Server

by Arize-ai

execute_graphql_query

Execute a GraphQL query against an API with optional variable support and result visualization.

Instructions

Execute a GraphQL query and optionally visualize the results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
variablesNoOptional variables for the GraphQL query
history_idNoOptional history ID to update
graphql_queryYesThe GraphQL query to execute
natural_language_queryNoThe original natural language query for context

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It states 'execute' and 'optionally visualize results', but does not disclose side effects, authentication requirements, error handling, or whether the operation is read-only or mutates state. This is insufficient for an execution tool.

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?

Single sentence with no wasted words. It efficiently communicates the primary action and optional feature. However, it lacks structure (e.g., bullet points) and could be slightly expanded for clarity.

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

Completeness3/5

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 100% schema parameter coverage, the description is adequate but not thorough. It fails to explain what 'visualize the results' entails or contextualize the tool among siblings like get_query_history. Some gap remains.

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?

Input schema covers 100% of parameters with descriptions, so baseline is 3. The tool description adds no additional parameter semantics beyond what the schema already provides, offering no extra context for parameter usage.

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 action ('Execute a GraphQL query') and resource, and distinguishes from siblings like generate_graphql_query, get_query_examples, get_query_history, and validate_graphql_query. The optional visualization feature adds specificity.

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

Usage Guidelines3/5

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

No explicit guidelines on when to use this tool vs alternatives like generate_graphql_query or validate_graphql_query. Usage is implied by the verb 'Execute' versus 'generate' or 'validate', but no direct comparisons or when-not-to-use guidance is provided.

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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