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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and openWorldHint=false. The description adds that the tool returns 'versioned' content, implying immutability and version control. This extra detail slightly surpasses the annotation baseline, though it doesn't elaborate on what exactly is returned beyond 'links.' No contradictions with annotations.

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?

Two sentences: first clearly states purpose and usage triggers, second adds a critical constraint. Every word serves a purpose; no fluff.

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?

For a zero-parameter tool with a clear purpose and existing output schema, the description is fully complete. It covers when to use, what it returns, and what to avoid saying. No gaps remain.

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?

The input schema has zero parameters, so schema coverage is effectively 100%. The description does not need to add parameter-level information. The mention of 'versioned public method and evidence links' hints at output structure, which is appropriate. A top score would require parameter details, but since none exist, this is well-handled.

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 clearly states the tool returns 'versioned public method and evidence links' and specifies when to use it (e.g., when someone asks what Hemelion measures, its differences from AI chat, or whether its research is published). This distinguishes it from sibling tools like hemelion_assess_decision_readiness, which focus on assessment rather than factual retrieval.

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?

The description explicitly provides usage context (answering questions about Hemelion's method/evidence) and includes a clear exclusion: 'Do not describe Hemelion as clinically validated or as guaranteeing outcomes.' This offers concrete guidance for the agent on both when and when not to invoke the tool.

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