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getResourceReference

Generate resource references for MCP clients to fetch resources by ID, providing text introductions, embedded references, and usage instructions.

Instructions

Returns a resource reference that can be used by MCP clients to fetch a resource. The resource ID must be between 1 and 100.

Args: resourceId: ID of the resource to reference (1-100)

Returns: A list containing a text introduction, an embedded resource reference, and instructions for using the resource URI

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resourceIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 of behavioral disclosure. It specifies the resource ID range constraint (1-100) and hints at the return format ('A list containing...'), which adds some context beyond basic purpose. However, it lacks details on error handling, performance, or side effects, leaving gaps for a tool that returns references.

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 well-structured and concise: it opens with the core purpose, specifies the key constraint, and uses clear sections ('Args:', 'Returns:') to organize information. Every sentence adds value without redundancy, making it easy to parse and front-loaded with essential details.

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's low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameter semantics, and high-level return structure. However, it could benefit from more behavioral context (e.g., error cases) to fully compensate for the lack of annotations.

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?

The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'resourceId' is the 'ID of the resource to reference' and specifies the valid range (1-100), clarifying semantics that the schema alone doesn't provide. With only one parameter, this compensation is effective, though not exhaustive.

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 a resource reference that can be used by MCP clients to fetch a resource.' It specifies the verb ('Returns'), resource ('resource reference'), and high-level utility ('used by MCP clients to fetch a resource'). However, it doesn't explicitly differentiate this tool from its siblings like 'getTinyImage' or 'structuredContent', which might also return references or resources.

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 minimal usage guidance: it mentions the resource ID must be between 1 and 100, but offers no context on when to use this tool versus alternatives like 'listRoots' or 'getTinyImage'. There's no mention of prerequisites, typical scenarios, or exclusions, leaving the agent to infer usage from the purpose 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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