fetch_dashboard_framework_controls

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

As no annotations exist, the description bears full responsibility. It discloses the API call, returns pagination info, and mentions an optional error field, but lacks detail on rate limits or authentication.

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 fairly long and contains some redundant phrasing (e.g., repeating the purpose). It is well-structured with sections, but could be more concise.

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 no annotations, the description covers input parameters, return fields, and pagination details sufficiently. It is complete for a fetch tool, though lacks some behavioral nuance.

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?

With 0% schema description coverage, the description adds value by explaining the purpose and format of both parameters. However, there is a naming mismatch (review_period vs. period) that could confuse the agent.

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 it retrieves detailed control-level data for a specific CCF and review period, distinguishing it from aggregated overview tools like fetch_dashboard_framework_summary.

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 implicitly tells when to use this tool (for detailed item-level data) vs. aggregated overview, but does not explicitly name alternatives or state when not to use it.

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