count_tokens

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

With no annotations provided, the description bears full burden for behavioral disclosure. It only states 'estimate token count' without explaining how estimation works (e.g., local tokenizer, model-dependence, accuracy) or any side effects, which is insufficient for a tool requiring trust in its output.

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: first states purpose, second gives usage advice. No redundant words, front-loads key information, and earns its brevity.

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 presence of an output schema (so return values need not be described), the description covers purpose and usage adequately but misses details about how the count is determined and potential limitations, which is a notable gap for a utility 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 coverage is 100% with well-described parameters including defaults and behavior (e.g., 'include_message_overhead' description). The description adds little beyond the schema, so 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 verb 'estimate' and resource 'token count for text or a message list', distinguishing it from sibling tools like 'compress_context' or 'prune_conversation' by positioning it as a pre-action decision tool.

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 says 'Use this to decide whether to compress, prune, or skip content', giving clear context for when to invoke this tool. However, it does not explicitly state when not to use it or mention alternatives beyond implied ones.

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