check_ollama

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

With no annotations provided, the description explicitly declares 'Read-only: yes. No side effects.' and elaborates on return behavior, including error/disability states, installed models, and additional fields. This fully carries the transparency burden.

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 (purpose, usage, args, returns). It is somewhat long due to the comprehensive return dictionary, but every field is meaningful, and the structure remains clear and front-loaded with the main action.

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 no output schema, the description thoroughly enumerates all return fields, statuses, and optional entries. It also provides usage context with sibling tools and explicitly notes side-effect-free behavior, making it complete for an agent to invoke and interpret results.

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 100%, and the description's params text merely restates the schema ('project_root: Project root. Auto-detected if omitted. Used to locate the project's LLM configuration.'). No added semantic value beyond what the schema already provides, so the baseline of 3 applies.

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's purpose with a specific action ('Check whether') and resource ('LLM backend' and 'configured embedding/chat model'). This distinguishes it from siblings like configure_llm or semantic_search.

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 explicitly tells when to use the tool: 'Call before smart_search, semantic_search, or explain_symbol' and even explains a conditional (on-demand fallback vs pre-computed analysis). This is a clear usage directive with alternatives.

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