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structuredContent

Generate structured content validated against JSON schemas to ensure data consistency and reliability in MCP server applications.

Instructions

Returns structured content that conforms to a well-defined JSON schema. Demonstrates MCP's structured output / schema-validated tool responses.

Args: includeOptionalFields: Whether to include optional fields in the response (default: False)

Returns: A structured dict with schema-validated fields

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeOptionalFieldsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns schema-validated content and demonstrates MCP features, which is useful context. However, it lacks details on permissions, rate limits, error behavior, or whether it's read-only/mutative. The description doesn't contradict annotations (none exist), but leaves behavioral gaps for a tool with structured output.

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 efficiently structured with a clear purpose statement, demonstration context, and separate Args/Returns sections. Every sentence adds value: the first explains what it does, the second provides context, and the third/fourth document parameters and returns. No wasted words, and key information is front-loaded.

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

Completeness4/5

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

Given the tool has an output schema (so returns needn't be detailed in description) and only one parameter with good semantic coverage in the description, this is reasonably complete. The description explains the parameter's effect and the tool's role in demonstrating MCP features. However, for a tool with no annotations, it could better address behavioral aspects like idempotency or error cases.

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?

Schema description coverage is 0%, but the description compensates well by explaining the single parameter's purpose: 'Whether to include optional fields in the response (default: False)'. This adds clear semantic meaning beyond the schema's type/boolean/default. Since there's only one parameter, the description effectively covers it, earning a high score despite low schema coverage.

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 tool's purpose: 'Returns structured content that conforms to a well-defined JSON schema.' It specifies the verb ('returns') and resource ('structured content'), and distinguishes it from siblings by mentioning MCP's structured output capability. However, it doesn't explicitly differentiate from specific sibling tools like 'annotatedMessage' or 'echo' that might also return structured content.

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 alternatives. It mentions demonstrating MCP's structured output, but doesn't specify scenarios, prerequisites, or exclusions compared to sibling tools like 'annotatedMessage' or 'echo'. The agent must infer usage from the generic purpose statement 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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