get_context_usage

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

The description explains the basic behavior (summing inputs with tiktoken), but lacks details on return format, error handling, or side effects. Since no annotations are provided, the description should be more explicit.

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 a single, concise sentence that directly states the tool's purpose. It contains no filler or redundancy.

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?

With 8 parameters, no output schema, and no annotations, the description is too brief to provide complete context. It omits details on return values, parameter interactions, and edge cases, making it insufficient for an agent to reliably invoke the tool.

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 only 13%, yet the description only mentions three of eight parameters (used_tokens, conversation_text, tool_results_text). The roles of max_tokens, session_id, system_prompt_text, text_for_estimate, and encoding_model are not explained, leaving significant gaps.

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 estimates context usage using tiktoken by summing specific inputs (used_tokens, conversation text, tool results). It distinguishes itself from sibling tools, which are all unrelated (memory, project, etc.).

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?

No guidance on when to use this tool versus alternatives is provided. The description only explains what the tool does, not when it is appropriate or what prerequisites exist.

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