Suggest Tag Improvements

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

No annotations are provided, so the description carries full burden. It details the internal logic (identify, find, compare, suggest, recommend) and output type ('prioritized improvement suggestions with explanations and example values'). No side effects or auth needs are relevant, and the process is clearly disclosed.

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 a single paragraph but is information-dense and front-loaded with the main purpose. Every sentence adds value, though it could be more scannable with bullet points or separate sections for use cases.

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 complexity (nested options, multiple tag formats, no output schema), the description covers the tool's behavior comprehensively. It explains input formats and output nature, but lacks an example output snippet, which would enhance completeness.

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 both parameters are well-documented in the input schema. The description adds no additional meaning beyond what the schema provides for 'tags' and 'options'. Thus, it meets the baseline for complete schema coverage.

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 analyzes OSM tag collections and provides intelligent suggestions for improvements. It specifies the steps: identifies feature type, finds matching preset, compares, suggests missing fields. This distinguishes it from siblings like validate_tag_collection (validation) and compare_tags (comparison).

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 explicitly lists use cases: 'enhance incomplete features, learn best practices for tagging specific feature types, or improve data quality of existing OSM data.' It does not mention when to avoid or name alternatives, but the context of siblings provides implicit differentiation.

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