check_output

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

No annotations are provided, so the description carries the full burden. It discloses what the tool returns (alert level, token count, etc.) but lacks information about side effects, such as whether the analysis is purely read-only or if it logs/store results. The description implies a safe query operation but could be more explicit about non-destructiveness.

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 two sentences, front-loaded with purpose and return values, followed by usage guidance. Every sentence adds value without redundancy. It is concise and well-structured.

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 the tool's simplicity (analyze a single text), the presence of an output schema (not shown but indicated), and full parameter coverage, the description sufficiently covers the tool's function. It tells the agent what it does, when to use it, and what it returns.

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%, with both parameters (text and type) described in the schema. The description does not add additional semantic detail beyond the schema; it mainly explains the output. For a tool with full schema coverage, a baseline score of 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 states the tool's purpose: analyzing text output from Claude API responses. It specifies the return fields (alert level, token count, suppression flag, waste patterns) and uses a specific verb ('analyze'). The tool is distinct from siblings like analyze_history, which deals with history rather than a single output.

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 advises using this tool 'after every API response' to catch issues early. It implies when to use it, but does not explicitly mention when not to use it or provide alternative tools. However, given the sibling list, the usage context is clear.

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