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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so 'Free, read-only' is largely redundant. What the description genuinely adds is the result profile — census status, 30-day uptime, and a link to each record — plus the scale signal ('tens of thousands of MCP endpoints'), which warns the agent that result sets are large. It does not explain pagination behavior or cost/rate characteristics beyond 'free'.

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, front-loaded with the verb and corpus scope, then the routing rule. The first sentence is dense — parenthetical scale figure, three enumerated outputs, and the 'plus LLPRO's own' clause — but each element earns its place by establishing corpus and return shape that the schema does not cover.

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?

With no output schema, the description usefully enumerates the key returned fields, which is the right call. However, for a five-parameter paginated search over a corpus it describes as 'tens of thousands' of entries, omitting any statement about pagination or result-set limits leaves a real gap an agent would need filled to call it well.

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 0%, so the description carries the burden, and it only clarifies two of five parameters at best: 'job words' maps to query and 'category' maps to category. It says nothing about page or per_page bounds, nor what the status values mean semantically (e.g. the difference between 'protocol_only', 'untested' and 'alive_not_mcp'), leaving half the surface inferred from enum names alone.

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?

States a specific verb and resource ('Search the Hub directory') and then bounds the corpus precisely: 'every agent and service in the Agent Record census (tens of thousands of MCP endpoints) plus LLPRO's own, by job words and category.' It also names the searchable facets and the fields returned, and closes by differentiating itself from hub_route, so an agent can distinguish it from that sibling without opening either schema.

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?

'Use hub_route when you want a ranked answer for a job' gives an explicit alternative with the condition that selects it, which is exactly the kind of routing an agent needs. It does not state exclusions in the other direction (when a directory search is the wrong choice) or any prerequisites, so it stops short of full when/when-not coverage.

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