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

With no annotations, the description must carry the full behavioral burden. It discloses the aggregate nature and dimensions of the returned data, which is genuinely useful, but says nothing about authentication requirements, rate limits, freshness/lag of the statistics, or whether the result is a fixed snapshot.

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?

Two sentences, front-loaded with the resource and the dimensions covered, followed by usage examples. Every clause earns its place, though the example-question list is slightly padded.

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?

There is no output schema, and the description compensates reasonably by enumerating the four result categories. Combined with a fully documented two-parameter schema, an agent has enough to call it correctly, though it lacks any note on result scope or limitations.

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 (month, province) are already fully documented in the schema, including the YYYY-MM format and the 'default current month' behavior. The description only echoes these at a high level ('本月/指定月份'), so baseline 3 applies.

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 names a specific verb+resource (bidding market statistics) and enumerates the exact aggregates returned: project counts for a month, industry-category distribution across engineering/goods/services, top provinces, and agency rankings. This clearly separates it from search_bidding_projects (per-project results) and ask_minger_bidding (Q&A), though it never names those siblings explicitly.

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?

It provides concrete when-to-use context via two example queries ('品类分布' and '表现好的代理机构') and frames the tool for market-insight scenarios. There are no explicit exclusions or named alternatives telling the agent when NOT to pick this tool.

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