Get Issue Fields

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

Beyond the readOnly/idempotent annotations, the description discloses the discovery mechanism (fetching a sample issue reflecting the server's active configuration), the merge step with fields MantisBT omits when empty, and the cache TTL behavior. These are non-obvious operational traits an agent cannot infer from the schema or 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?

Three sentences, front-loaded with the return value, then mechanism, then imperative usage instruction. No filler; every sentence carries distinct information.

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?

There is no output schema, but the description states what is returned (field names) and the imperative guidance tells the agent how to use the result. For a one-parameter read-only discovery tool, nothing material is missing.

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% and the single parameter project_id is already documented there as 'scope the sample issue fetch'. The description only alludes to this indirectly ('sample issue fetch') and adds no format, default, or scoping semantics beyond the schema, so baseline 3 applies.

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 names a specific artifact ('all field names valid for the "select" parameter') and the exact sibling tools that consume it (list_issues, get_issue), so an agent can immediately tell what it returns and why it exists. This distinguishes it cleanly from get_issue/list_issues, which fetch actual issue data rather than schema metadata.

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?

It gives explicit when-to-use guidance: 'Use this tool before constructing a "select" string.' That is a clear trigger condition tied to a concrete workflow. It lacks explicit when-not / negative guidance, but the positive routing is unambiguous.

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