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Artsy Analytics MCP Server

by damassi

query_analytics_custom

query_analytics_custom

Run tailored GraphQL queries on Artsy Analytics MCP Server to analyze gallery metrics, sales data, audience insights, and content performance.

Instructions

Execute a custom GraphQL query against the analytics schema

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesGraphQL query string
variablesNoVariables for the GraphQL query
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the action but doesn't describe important behavioral aspects like authentication requirements, rate limits, error handling, response format, or whether this is a read-only or mutation operation. The description is minimal and lacks operational context.

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 extremely concise - a single sentence that efficiently communicates the core functionality without any wasted words. It's appropriately sized for a tool with clear parameters documented elsewhere.

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?

For a GraphQL query execution tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the analytics schema contains, what types of queries are supported, what the response format will be, or any limitations. Given the complexity of GraphQL and the lack of structured documentation, the description should provide more context.

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 both parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions 'GraphQL query' which is already in the parameter descriptions. Baseline 3 is appropriate when schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('Execute a custom GraphQL query') and target ('against the analytics schema'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools, which are all analytics-related but appear to be predefined queries rather than custom ones.

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?

The description provides no guidance on when to use this tool versus the many sibling analytics tools. It doesn't mention alternatives, prerequisites, or specific contexts where a custom GraphQL query would be preferred over the predefined analytics endpoints.

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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