get_agent_registry

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

The description adds behavioral context beyond the readOnlyHint annotation by explaining that the data reflects real calls from a sliding window and lists recognized clients. It does not contradict annotations and provides useful insight into the dynamic nature of the data.

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 extremely concise: two sentences covering purpose, output, examples, and use case, plus a 'Try:' suggestion. Every sentence is meaningful with no wasted words.

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?

Despite lacking an output schema, the description thoroughly explains what the tool returns (caller info, counts, breakdown, tier) and provides examples of recognized agents. This is sufficient for an agent to understand the tool's functionality.

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?

With no parameters and 100% schema coverage, the description does not need to explain parameters. It adds value by describing the output fields (citation counts, breakdown, authentication tier), which compensates for the lack of an output schema.

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 returns a 'live roster' of AI platforms and agent frameworks that have called DC Hub, with specific data points like citation counts, tool-usage breakdown, and authentication tier. This distinguishes it from sibling tools that retrieve different types of data.

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 indicates it is 'useful for benchmarking which agents discover and integrate the platform,' providing clear usage context. However, it does not explicitly state when not to use it or compare it to similar tools among siblings, though the unique output makes the use case clear.

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