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read_resource

Retrieve content and metadata from a specific resource by providing its URI. This tool reads resources from connected MCP servers for testing and validation purposes.

Instructions

Read a specific resource from the connected MCP server.

Reads a resource by URI and returns its content along with metadata.

Returns: Dictionary with resource content including: - success: True if resource was read successfully - resource: Object with uri, mimeType, and content - metadata: Content size and request timing

Raises: Returns error dict for various failure scenarios: - not_connected: No active connection - resource_not_found: Resource doesn't exist on server - execution_error: Resource read failed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYesURI of the resource to read (e.g., 'config://settings')

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 of behavioral disclosure. It effectively describes the return structure and error scenarios, adding value beyond the input schema. However, it lacks details on permissions, rate limits, or side effects, which are important for a read operation in a server context.

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?

The description is well-structured with clear sections (purpose, returns, raises) and avoids redundancy. However, the 'Returns' and 'Raises' sections could be more concise, as they detail output that might be covered by an output schema (which exists here), slightly reducing efficiency.

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 one parameter with full schema coverage and an output schema present, the description provides adequate context by explaining the tool's purpose, return values, and error handling. It covers the essentials for a read operation, though it could benefit from more usage guidance or behavioral details to be fully comprehensive.

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 input schema has 100% description coverage for its single parameter, so the baseline is 3. The description adds context by explaining that the URI is used to 'read a specific resource' and mentions example usage ('e.g., config://settings'), enhancing understanding beyond the schema's basic definition.

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 verb 'read' and resource 'specific resource from the connected MCP server', making the purpose evident. It distinguishes from siblings like 'list_resources' by focusing on individual retrieval rather than listing. However, it doesn't explicitly contrast with 'get_prompt' or other read-like tools, keeping it from a perfect score.

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 like 'list_resources' or 'get_prompt'. It mentions a 'connected MCP server' but doesn't specify prerequisites or exclusions, leaving usage context implied rather than explicit.

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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