Search symbols

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

No annotations are provided, so the description must disclose behavioral traits. It does: it limits results to 'up to 20 matches', specifies the return structure ('compact JSON with symbol, name, exchange, and assetClass'), and notes the fixed-universe limitation. It does not mention pagination or error handling, but for a simple lookup tool this is a solid disclosure.

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 dense but well-structured: it opens with the core search capability, follows with when-to-use and an illustrative example, then parameter constraints, return format, and a key caveat. Every sentence earns its place; no redundant phrasing or filler.

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 tool with a single parameter, no output schema, and limited complexity, the description is complete. It covers the input, output format, result limit, and a critical limitation (fixed universe) with a pointer to the authoritative list. The agent can invoke this tool confidently with the expected result described well enough.

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 description coverage is 100%, so the baseline is 3. The description adds value by providing concrete examples ('resolving Apple to AAPL', 'checking whether Pyon covers a given asset') and restating the parameter range ('1-100 characters'). This helps the agent understand the intended use beyond the schema's literal definition.

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 function: 'Search Pyon's market database for tradable instruments by ticker or company name fragment.' It uses a specific verb (search), names the resource (market database), and indicates the query type (ticker or name fragment). It also differentiates from sibling tools by positioning this as the first step for resolving symbols, for example 'resolving Apple to AAPL'.

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 gives explicit when-to-use guidance: 'Use this first whenever you need an exact symbol to mention in a research prompt or strategy description.' It also provides a concrete alternative: 'call get_capabilities(section='tickers') for the definitive tradable list,' which clarifies when not to rely solely on this tool (fixed universe). This exceeds baseline with clear context and exclusions.

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