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

While annotations already indicate read-only and non-destructive behavior, the description goes far beyond by detailing the output structure (viability band with five drivers, weakest signal explanation), the data source (RightAIChoice verification engine with weekly re-probing), and handling of nulls ('null driver means "not yet measured"'). It also clarifies that bands classify risk rather than rank, preventing misinterpretation. No contradiction with 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?

The description is longer than average but every sentence serves a purpose: usage, output description, data source, null handling, band interpretation, and exclusions. It is well-organized and front-loaded with the primary use case. A slight reduction in wordiness could earn a 5, but it remains concise relative to the information density.

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?

With no output schema, the description fully specifies what is returned (band, drivers, weakest signal) and clarifies nuances (null vs bad, band not a leaderboard). It also provides context on the verification engine's scope and freshness. Combined with the clear input schema and usage boundaries, nothing important is missing for the agent to invoke and interpret results correctly.

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 only parameter 'tool' is already thoroughly described in the schema (product name or site slug with examples). Since schema coverage is 100% and the description does not add new parameter semantics beyond the schema, the baseline score of 3 is appropriate. The description's mention of 'specific AI tool' in the usage does not add extra meaning beyond the 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?

The description opens with a specific verb-resource pairing: assessing the viability of an AI tool for adoption or building. It enumerates the types of queries it answers (safe to adopt, likely to survive, maintenance, momentum, abandonment risk) and explicitly distinguishes itself from sibling tools (compare_tools, check_tool_status), making 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?

Provides explicit when-to-use criteria: user asks about safety, longevity, maintenance, momentum, or abandonment risk. Also gives explicit exclusions with alternatives: 'Not for: comparing two tools (use compare_tools), liveness checks alone (use check_tool_status), or investment decisions.' This is exemplary usage guidance.

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