check_ollama

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

With no annotations provided, the description fully bears the burden of behavioral disclosure. It explicitly states 'Read-only: yes. No side effects.' and details the return dictionary including fields for status, errors, and debug info. This makes behavior transparent despite the absence of annotations.

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 well-structured with clear sections: main purpose, read-only note, usage suggestion, Args, Returns. Every sentence is informative and there is no redundancy. It is appropriately sized and front-loaded.

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 (one optional parameter), the description is complete. It covers purpose, usage timing, return structure, and even notes the ignored parameter. No output schema exists, but the description provides a detailed return dictionary, making it contextually complete.

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 input schema has 100% description coverage for the single parameter 'project_root'. The description adds value by repeating and clarifying that the parameter is auto-detected and ignored, and also provides a full return dictionary, adding meaning beyond the schema.

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 starts with a specific verb+resource: 'Check whether the LLM backend is running and the configured embedding/chat model is installed.' It also explicitly states it is read-only and has no side effects, and distinguishes from siblings by advising to call this before smart_search, semantic_search, or explain_symbol.

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?

Provides clear guidance on when to use the tool: 'Call before smart_search, semantic_search, or explain_symbol (when on-demand fallback is expected...).' Also clarifies it is read-only and has no side effects, giving explicit context for appropriate use.

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