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

With no annotations, the description carries the disclosure burden and does well: it promises 'Instant (no AI)', states the kind of information returned, and reveals a critical caveat about stale or building indexes. It doesn't detail output structure or failure behavior, but for a zero-parameter info tool this is a solid disclosure.

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?

Two sentences with no filler: the first defines the resource and content, the second gives timing, usage, and a conditional instruction. Information is front-loaded and every clause earns its place.

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 no-parameter, no-output-schema introspective tool, the description covers what the server answers for, which repositories back it, freshness, latency, and when to use it. No important calling context is missing.

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 tool has no parameters, so there is nothing to document; the description correctly avoids inventing parameter details. This matches the baseline for a zero-parameter tool.

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 names a specific action (describe this MCP server) and a concrete resource scope (product, repositories, index freshness), so an agent can tell what it does. It implicitly contrasts with deep-answer tools ('instead of trusting deep answers') but does not explicitly name sibling alternatives, so it stops just short of full differentiation.

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?

It gives explicit guidance to call this tool once at the start of a session to confirm the correct product, and explains what to do if the index is stale or building. It lacks a named alternative like ask_product_expert for deeper questions, but the context signals make that inferable.

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