llm_dashboard

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

With no annotations provided, the description carries the full burden. It discloses that a local HTTP server is started, the dashboard reads from the same SQLite DB, and it refreshes every 30 seconds. It also details the port argument. However, it does not mention lifecycle aspects like how to stop the server, which is a minor gap.

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 concise at 4 sentences, front-loaded with the core action. It uses clear structure with a main sentence, followed by bullet-like listing of dashboard features, a note on data source, and an Args/Returns section. Every sentence adds value.

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 has an output schema (though not shown), the description appropriately includes a Returns clause stating it returns URL and instructions. It covers setup, refresh interval, data source, and port parameter. No critical gaps remain for an agent to invoke the tool correctly.

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 0%, so the description must compensate. It effectively documents the single 'port' parameter with its default value (7337) in the Args section, adding meaning beyond the schema's minimal definition. This provides sufficient guidance for the agent.

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 opens the LLM Router web dashboard in the background, specifying it starts a local HTTP server showing routing stats, cost trends, model distribution, and recent decisions. This specific verb-resource combination effectively distinguishes it from sibling tools like llm_analyze or llm_route, which serve different purposes.

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 provides clear context on when to use the tool (for monitoring routing stats via a dashboard) and notes it requires no extra configuration, but it does not explicitly exclude use cases or mention alternatives. Since siblings cover many distinct functions, the implied usage is generally clear.

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