llm_fs_analyze_context

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

Given no annotations, the description fully discloses the tool's behavior: it scans specific files, writes a summary to ~/.llm-router/context_summary.json, and that summary is used in later routing decisions. It also specifies the caching and automatic use, which gives a clear picture of side effects and lifecycle.

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 highly concise and well-structured: it starts with the core purpose, then details what it does, how it's used, when to call it, and ends with argument descriptions. Every sentence adds value without redundancy.

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 tool with moderate complexity, two parameters, and an output schema, the description covers all essential aspects: inputs, action, output location, and usage pattern. It explains the integration with other tools and the automatic subsequent use, making it fully self-contained.

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 parameter schema has 0% description coverage, but the tool's description compensates fully by explaining both parameters: 'path' as workspace root (default .) and 'max_files' as max files to read (default 20). This adds crucial meaning beyond the schema's type and default.

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: analyzing workspace files to build a routing context summary. It specifies the files (package.json, etc.), the output location, and how the summary is used by sibling tools (llm_route, llm_auto), effectively differentiating from other tools like llm_analyze.

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 explicit guidance: 'Call this once at the start of a project session or after major refactors.' It also explains that the summary is automatically used, so no further action is needed. While it does not explicitly list alternatives or when not to use, the context makes it clear this is a setup tool.

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