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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, and the description meaningfully augments them by stating that the dataset is tiny, many lookups are misses, and the agent must not fabricate facts. This is exactly the behavioral context needed for an open-world lookup.

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?

Three short sentences, front-loaded with the tool purpose, then constraints and an anti-hallucination warning. Every sentence earns its place and there is 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 one-parameter, read-only tool with no output schema, the description sufficiently covers what the result contains, how often it misses, and what the agent must not do. Nothing critical is missing for correct invocation and interpretation.

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 coverage is 100%: the login parameter already explains case-insensitivity and unknown-login behavior. The description adds no extra parameter-level detail but reinforses the small known-set context, so baseline 3 is appropriate.

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 identifies the resource (GitHub login) and the operation (retrieve public-record facts from research findings). It implicitly distinguishes itself from sibling tools by its GitHub-specific contributor profile focus, though it does not explicitly name alternatives.

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 gives clear context: the known set is small, most logins return found=false, and the agent must not invent employment or affiliation. This tells the agent when to trust the result, but it does not explicitly state when to prefer sibling tools.

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