llmkit_local_session

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

Annotations already indicate readOnly=true, idempotent=true. The description adds the behavioral detail that no API key is required, which is useful. However, it does not disclose what happens when no tools are detected or any other execution traits beyond what annotations cover.

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, efficiently conveying purpose and a key usage detail. Every word serves a purpose, no fluff, and it is front-loaded with primary information.

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 (zero params, output schema present, clear annotations), the description fully covers what an AI agent needs: what it returns (session cost across tools) and an important precondition (no API key). No gaps remain.

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 zero parameters and 100% schema coverage, the description needs to add no extra meaning. It correctly states the absence of required inputs ('No API key needed'), meeting the baseline expectation for a parameterless tool.

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 returns the current session cost across detected AI coding tools, specifically naming Claude Code and Cline. This verb+resource+scope effectively distinguishes it from siblings like budget_status or cost_query.

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?

It mentions 'No API key needed' implying ease of use, but lacks explicit guidance on when to use this tool vs alternatives like llmkit_cost_query or llmkit_session_summary. Context is implied but not directly compared.

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