EpicMe MCP

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

No annotations are provided, so the description carries the full burden. It mentions 'sampling' but does not disclose behavioral traits such as whether this is a read-only or mutative operation, potential rate limits, authentication needs, or what the output format looks like. The description is minimal and lacks critical 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It is appropriately sized for the tool's complexity, though it could be more front-loaded with key details to improve clarity.

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

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

Given the lack of annotations and output schema, the description is incomplete. It does not explain what 'sampling' returns, how results are formatted, or any error conditions, leaving significant gaps for an LLM interaction tool with two parameters.

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

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

Schema description coverage is 100%, so the schema fully documents the two parameters ('prompt' and 'maxTokens'). The description adds no additional meaning beyond what the schema provides, such as examples or constraints, meeting the baseline for high schema coverage.

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

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

The description states the action ('Samples from an LLM') but is vague about the specific mechanism ('using MCP's sampling feature') without explaining what sampling entails. It distinguishes from siblings like 'echo' or 'printEnv' by involving LLM interaction, but lacks specificity about the resource or output type.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, context, or exclusions, leaving the agent to infer usage based on the tool name alone among siblings like 'annotatedMessage' or 'structuredContent'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.