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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds substantial behavioral detail: null pct indicates data-quality inconsistency (not a true zero), ambiguous symbols return JSON candidates instead of data, and the instrument suffix is honored over the default. These are non-obvious behaviors that materially affect how the agent interprets results and interacts with the user. No contradiction with 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 dense but every sentence earns its place. The core purpose is front-loaded, followed by the instrument nuance, return format, pagination, range, null handling, and ambiguity behavior. It is about 130 words, which is efficient for the level of operational detail it conveys. No filler or redundant phrasing.

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 five parameters and no output schema, the description covers everything an agent needs to call it correctly: the return format (CSV or JSON candidates), the meaning of null values, the pagination semantics, the date-range behavior, the instrument suffix override, and the ambiguity resolution workflow. It also accounts for the data-quality caveat. No critical behavior is left unexplained.

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 the baseline is 3. However, the description adds significant meaning beyond the schema: it explains the semantic of a null pct value, clarifies that the symbol is typo-tolerant and that an explicit suffix overrides the default, and describes how the ambiguity fallback works. It also contextualizes limit/offset as pagination for 'most recent days'. This goes well beyond the schema's bare parameter definitions, fully compensating and then some.

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 opens with a specific verb+resource: 'Get the daily foreign-shareholding percentage series for a CSE symbol'. It clearly defines the resource scope (CSE symbol) and the data type (foreign-shareholding percentage). It also distinguishes itself from generic sibling tools by highlighting the instrument-specific nuance and the ambiguity fallback, making the tool's unique role evident even without naming siblings.

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 rich usage context: it explains the instrument suffix behavior, pagination via offset/limit, date-range scoping, and the ambiguity fallback that instructs the agent to ask the user for an exact symbol. It does not explicitly contrast with alternative tools or state when not to use it, but the context is clear enough for an agent to decide when this tool fits. The lack of explicit exclusions keeps it from a 5.

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