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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, establishing it as a safe, read-only tool. The description adds valuable behavioral context: parallel fan-out to multiple sources, fallback logic (GDELT preferred, GNews on rate limit/5xx), soft-failure for USPTO, and the return format (structured changes grouped by source, total_changes count, pipeworx:// citation URIs). 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 moderately long but well-structured. It front-loads with example queries and core purpose, then systematically covers sources, parameter details, return format, and alternative tools. Each sentence adds value without redundancy. A slight reduction could make it even tighter, but it remains efficient.

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 the tool's complexity (multiple data sources, fallback logic, special date formats) and the absence of an output schema, the description is remarkably complete. It explains what sources are consulted, how fallback works, the `since` parameter formats, the output structure, and directs to the sibling tool for static profiles. The annotations cover safety and idempotency. An AI agent has sufficient information to decide when and how to invoke this tool.

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 baseline is 3. The description goes beyond by explaining the 'since' parameter accepts both ISO dates and relative shorthand (with examples '7d', '30d', '1y'), specifying for 'value' it can be a ticker or zero-padded CIK, and clarifying 'type' is currently limited to 'company'. This adds practical usage guidance beyond the schema definitions.

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 starts with example queries ('What's new with X', 'latest on Y') and clearly states the tool provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call'. It identifies specific data sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly distinguishes itself from the sibling tool 'entity_profile', which serves the complementary use case of static profiles.

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 explicitly tells when to use this tool vs alternatives ('Use entity_profile instead when you want the static profile...'), and provides example natural language queries. It implies usage for recent changes but does not explicitly state when not to use it (e.g., for historical data beyond a few years).

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