alpha_vantage_query

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. It states this is a query operation (implying read-only), but doesn't mention authentication requirements, rate limits, error handling, response formats, or any constraints like data freshness or availability. For a financial API tool with 6 parameters, 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 extremely concise - just two sentences that efficiently communicate the tool's purpose and scope. The first sentence states the core function, and the second enumerates the data categories without unnecessary elaboration. Every word earns its place, and the information is front-loaded appropriately.

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?

For a financial data query tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how results are structured, any limitations or constraints, or how this tool differs from similar financial data tools in the server. The description provides only basic purpose information without the contextual details needed for effective tool selection and use.

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 description coverage is 100%, so all parameters are documented in the schema. The description adds minimal value beyond the schema - it mentions the categories of data (stock, forex, crypto, economic indicators) which loosely maps to parameter usage, but doesn't provide additional semantic context about how parameters interact or when specific combinations are needed. The baseline of 3 is appropriate given the comprehensive schema documentation.

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: 'Query financial market data from Alpha Vantage' with specific examples of data types (stock time series, forex rates, crypto prices, economic indicators). It uses a specific verb ('query') and identifies the resource ('financial market data'), but doesn't distinguish this tool from sibling tools like 'finnhub_query' or 'fmp_financial' which appear to serve similar financial data domains.

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 no guidance on when to use this tool versus alternatives. While it lists data categories (stock, forex, crypto, economic indicators), it doesn't specify when to choose this over sibling tools like 'finnhub_query' or 'fmp_financial', nor does it mention any prerequisites, limitations, or typical use cases beyond the broad category of financial data querying.

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