tokscale_market_share

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

Annotations already declare readOnlyHint=true and openWorldHint=false. Description adds context: 'All data is read locally from tokscale's scan of your session logs — no network calls, no PII.' This reinforces safety and discloses the data source beyond 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?

Description is concise (~80 words) and front-loaded with purpose. Every sentence adds value: purpose, aggregation method, output format, data privacy, usage guidance, and negative guidance. No redundant 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?

Given 0 parameters, rich annotations (readOnlyHint, openWorldHint), and presence of output schema, the description fully covers what the tool does, how it works, what it returns, and when to use it. No gaps remain.

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?

No parameters; schema coverage is 100% with empty schema. Description is not required to add parameter details, and baseline for 0 params is 4. The description confirms lack of parameters by stating 'No parameters' in 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?

Description clearly states 'Complete AI tool market share analysis', specifying aggregation by client and computation of shares for tokens, cost, messages. It distinguishes from sibling tool 'tokscale_developer_profile' by directing users elsewhere for per-model detail.

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?

Explicit usage guidance: 'Use this to see which AI coding tools dominate your workflow' and 'Do NOT use this for per-model detail — use tokscale_developer_profile for that.' Also notes data is local with no network calls or PII.

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