milton_analyze

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

With no annotations, the description carries the full burden. It thoroughly explains what the tool returns: the full Milton-Model lens, output contract, detected patterns, presuppositions, gaps, vagueness, counter-questions, and covers all sub-types and edge cases. It does not mention runtime behavior or dependencies but comprehensively describes the output behavior.

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 dense yet efficient, front-loading with 'DEFENSE (recognition)' and the core purpose. Every sentence adds specific detail about capabilities and constraints, though the length may be slightly verbose for quick scanning. Still, it earns its length with valuable 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?

Despite no output schema, the description fully specifies the return value structure, including the output contract, pattern details, presuppositions, gaps, counter-questions, and edge cases (NO_PATTERNS_DETECTED, INSUFFICIENT_INPUT). It also mentions coverage of all ambiguity sub-types and text-channel markers, making the tool's behavior completely clear.

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 coverage is 100%, so the schema itself already documents both parameters. The description adds value by specifying that the text is analyzed for 'Milton-Model patterns' and that the response language can be 'pl', 'en', or 'both'. This enriches the parameter meaning beyond the schema definitions.

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 'DEFENSE (recognition)' and clearly states the tool takes a block of text and returns the canonical Milton-Model lens with patterns, explicitly differentiating from the sibling tool 'milton_generate' by referencing its key set. The verb 'analyze' and specific resource 'Milton-Model patterns' make the purpose unambiguous.

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 indicates this is for 'defense' (recognition), implying it is used to detect patterns rather than generate them. It specifies return statuses for edge cases (no patterns, empty input). However, it does not explicitly state when not to use this tool or provide direct comparisons with siblings like 'milton_generate', leaving some implicit guidance.

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