FinanceMCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it explains the two query modes, it doesn't describe what 'non-market data' specifically means, what format the data returns, whether there are rate limits, authentication requirements, or error conditions. For a financial data tool with zero annotation coverage, this leaves significant behavioral gaps.

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 perfectly concise and front-loaded: one sentence stating the overall purpose followed by two clearly numbered query methods. Every sentence earns its place with no wasted words or 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 the tool's moderate complexity (4 parameters, two distinct query modes) and 100% schema coverage but no output schema or annotations, the description is adequate but incomplete. It explains what the tool does and the two usage patterns, but doesn't address return values, error handling, or behavioral constraints that would be important for an AI agent to use it 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 schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds value by explaining the two query modes that correspond to parameter combinations (issue type uses date parameters, info type uses ts_code), but doesn't provide additional semantic context beyond what's already in the schema descriptions.

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's purpose: '获取可转债非行情数据' (get convertible bond non-market data) with specific query methods. It distinguishes between two distinct use cases (issue data by date range and info data by code), making the purpose explicit and differentiated from sibling tools which appear to handle different financial data types.

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 provides clear context for when to use each query type: '1)使用issue类型按时间范围查询可转债发行数据；2)使用info类型按代码查询可转债详细信息' (1) use issue type to query convertible bond issuance data by time range; 2) use info type to query convertible bond detailed information by code). However, it doesn't explicitly state when NOT to use this tool or mention alternatives among sibling tools.

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