digiforma_introspect_schema

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

With no annotations provided, the description carries the behavioral burden. Calling it a 'lightweight introspection query' communicates a read-only, low-impact operation, which is the key behavioral trait. It does not mention auth or rate limits, but those are less critical for a zero-parameter schema fetch.

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 compact sentences: the first names the action and resource, the second adds usage context. No filler or redundancy, and the core purpose is front-loaded.

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?

Despite having no parameters, no annotations, and no output schema, the description covers what is returned, the lightweight/read-only nature, and when to call it. An agent has sufficient information to decide and invoke the tool correctly.

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 tool takes no parameters, so there are no parameter semantics to clarify; the empty input schema fully covers this. The description adds the relevant output context by saying it returns GraphQL type names, meeting the 0-param baseline.

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?

States a specific verb ('Fetch') and a clear resource ('list of GraphQL type names exposed by the Digiforma API'). The parenthetical 'lightweight introspection query' and the reference to digiforma_graphql help differentiate it from data-returning siblings like digiforma_list_trainees and digiforma_get_trainee.

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?

Explicitly frames when to use the tool: 'before writing a custom digiforma_graphql query,' and names digiforma_graphql as the follow-up tool. It does not explicitly state exclusions for the trainee/session siblings, but those are clearly data retrieval rather than schema discovery.

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