authenticated-multi-llm-agent

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the high-level flow: verify token then run completion. It does not mention rate limits, idempotency, error states, or what happens on auth failure. For a tool that handles authentication and LLM calls, these are critical behavioral details missing.

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 a single sentence that directly states the purpose and key steps. It is concise without redundancy. One could argue it could be split into sentences for clarity, but overall it is efficient and front-loaded with the essential action.

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 lack of output schema, annotations, and a vague parameter description, the description should provide more context about expected input structure, output format, and edge cases. It does not cover what the agent should expect to receive or return, nor does it help distinguish from very similar siblings like 'authenticated-llm-agent'. The completeness is inadequate for an informed tool selection.

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 one parameter 'input' described as 'JSON request for this capability (the same body you'd send as an A2A message).' The description adds the context of A2A messaging but does not specify the required fields within that JSON, such as the ID token or the prompt. The parameter description is vague and relies on external knowledge of the A2A format, offering only partial semantic enrichment.

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 identifies the tool as a Google-OAuth-gated LLM gateway that verifies a Google ID token and then runs a Gemini (Vertex AI) completion. This specifies both the authentication mechanism and the backend model, making the purpose specific and actionable. However, it does not explain the 'multi' in the name, and the set of siblings suggests there might be other authenticated LLM agents; lacking differentiation reduces clarity slightly.

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 implies when to use this tool: when you need an OAuth-gated gateway to run Gemini completions. However, it offers no explicit guidance on when not to use it or alternatives among the siblings. For instance, it doesn't mention that 'authenticated-llm-agent' might be for a different LLM or that 'authenticated-mcp-agent' uses a different protocol. Usage context is implied but not clearly delineated.

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