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list_models

Discover available data models in a Gadget app using GraphQL introspection to understand the app's structure and available data types.

Instructions

List all models (types) available in this Gadget app via GraphQL introspection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler implementation for the list_models tool, which performs a GraphQL introspection query to fetch available models.
    case "list_models": {
      const data = await gql(`
        query {
          __schema {
            queryType {
              fields {
                name
                description
                type { name kind }
              }
            }
          }
        }
      `);
      const fields: any[] = data.__schema.queryType.fields;
      const models = fields
        .filter((f) => f.name && !f.name.startsWith("__") && f.type?.kind === "OBJECT")
        .map((f) => ({ name: f.name, description: f.description ?? "" }));
      return { content: [{ type: "text", text: JSON.stringify(models, null, 2) }] };
    }
  • src/tools.ts:180-183 (registration)
    The tool registration and schema definition for list_models.
      name: "list_models",
      description: "List all models (types) available in this Gadget app via GraphQL introspection.",
      inputSchema: { type: "object", properties: {} },
    },
Behavior3/5

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

No annotations are provided, so the description carries the full disclosure burden. It reveals the implementation mechanism (GraphQL introspection) but omits safety properties (read-only status), side effects, or return value structure. 'List' implies read-only behavior, but explicit confirmation would be expected given zero annotation coverage.

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?

Single sentence with no waste. Front-loaded with the action verb, immediately followed by the object and implementation detail. Every word earns its place.

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?

With no output schema and no annotations, the description should ideally disclose return value structure or safety characteristics. While the tool has low complexity (zero parameters), the absence of any behavioral guarantees or response format details leaves gaps in the agent's understanding of the tool's contract.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has zero parameters, which per evaluation rules sets a baseline of 4. The description does not need to compensate for parameter documentation, and the single sentence structure appropriately reflects the lack of configuration needed.

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?

Description provides specific verb 'List' and clear resource 'all models (types) available in this Gadget app'. The addition of 'via GraphQL introspection' distinguishes it from sibling run_graphql (arbitrary queries) and implicitly contrasts with introspect_model (likely single-model focus).

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 versus siblings like introspect_model or run_graphql. While the scope 'all models' implies a broad discovery use case, the description does not state prerequisites or selection criteria for choosing this over alternatives.

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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