Ticker across Market Trackers

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

With no annotations provided, the description carries the full behavioral disclosure burden. It explains the return shape (per-dataset match counts with newest rows), the default-year behavior, and the skipped-deep-history edge case, which gives an agent a solid model of what will happen.

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 somewhat long due to enumerating many datasets, but each sentence contributes: scope, return format, and the deep-history fallback. Core behavior is front-loaded before the edge-case explanation.

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 read-style fan-out tool with no output schema and no annotations, the description explains the datasets, the aggregation approach, the return contents, and the failure/fallback case for too-large archive years. It is reasonably complete for an agent to decide to call it.

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 schema already documents ticker, year, and limit. The description adds context like the one-year scope and per-dataset limiting, but does not materially extend the parameter meaning beyond what the schema provides.

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 states a specific operation: retrieve data for one ticker across every ticker-bearing Market Trackers dataset for a given year. It enumerates the datasets covered and distinguishes itself from deep-history retrieval by referencing trackers_query.

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 clearly implies the tool is for broad single-ticker, single-year coverage across many public-record datasets. It also provides an explicit alternative for deep-history archive years too large for one fan-out, naming trackers_query as the call that reads them.

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