token_budget_view

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

With no annotations present, the description carries the full disclosure burden. It states the tool analyzes rather than mutates and that it returns recommendations, which is meaningful behavioral context. It does not explicitly say 'read-only' or describe failure conditions, but the analytical verbs make the non-destructive nature reasonably clear.

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 entire definition is one front-loaded, information-dense sentence. Every clause earns its place: the verb, the analyzed metrics, and the actionable output are all present without filler.

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?

For a one-parameter analysis tool with no output schema, the description covers what it analyzes, what it returns, and why it matters. The only notable gap is explicit guidance on choosing between this and its siblings, but that is already accounted for in usage guidelines.

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 schema already fully documents the single 'path' parameter at 100% coverage. The description adds little beyond reinforcing that the path points to a source file, so the baseline of 3 applies; no parameter-semantic gap exists.

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 uses a specific verb, 'Analyzes,' and names the resource ('a source file') and the exact metrics (line count, estimated LLM token weight), plus the outcome ('actionable recommendations to avoid context saturation'). This clearly differentiates it from siblings like condense_source and compress_log, which imply modification rather than analysis.

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 intended use is implied by the phrase 'to avoid context saturation,' suggesting it should be run before condensing or compressing a file. However, it never names sibling tools or states when not to use it, so an agent must infer the routing decision from the purpose alone.

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