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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value beyond that by disclosing the return fields (ticker, name, sector, industry, optionally market cap) and the nuance that market cap data comes from a cache, with rarely-viewed names showing '—'. This is useful behavioral context not present in 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 concise (two sentences) and front-loads the core purpose. The metaphor 'map the surface before drilling' is slightly unnecessary but does not harm. It earns a high score for efficient structure, though not perfect due to the slight verbosity.

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 that there is no output schema, the description compensates by listing the return fields. It also covers typical use cases and the special behavior of sort='market_cap'. Combined with the detailed schema, the tool is well-specified for an agent to select and invoke 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 input schema has 100% description coverage for all 4 parameters, including clear descriptions for sort, field, limit, and value. The description adds some extra context (e.g., the purpose of sort='market_cap' and example values), but this is marginal since the schema already documents the parameters effectively. Baseline 3 is appropriate.

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 uses a specific verb 'List' and identifies the resource 'securities inside a sector or industry'. It is distinct from sibling tools like search_instruments by explicitly scoping to sector/industry filtering, and provides concrete example queries. The phrase 'map the surface before drilling' adds conceptual clarity.

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 gives explicit when-to-use scenarios: "Use this when the user asks 'what's in Technology?', 'show me the Banks industry', or to build a peer set by size for a comps analysis (sort='market_cap')." This provides clear context for use, though it does not mention when not to use or name alternative tools explicitly, missing the highest bar.

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