route_valveReturns the current machine entry points, countable outcome, next action, and claim boundary for exactly one named valve.
| Name | Required | Description | Default |
|---|---|---|---|
| valve | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide the safety profile (readOnlyHint=true, destructiveHint=false, idempotentHint=true). The description adds the specific return data categories, which is useful context. It does not disclose additional behavioral traits like error handling or response shape, but the strong annotations lower the bar.
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 well-structured sentence that front-loads the action ('Returns...') and includes only necessary details. Every word contributes to understanding the tool's function.
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?
There is no output schema, so the description should describe return values. It lists four categories of returned data but does not specify their structure or types. Annotations cover safety, and the tool's scope is simple (one parameter), so the description is adequate but not thorough.
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 must compensate. It clarifies that the valve parameter is a single named valve, but it does not explain the meaning of the enum values (colloquium, open_standing, agentic_substrate_prelaunch), leaving the agent to infer their semantics from 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?
The description clearly states it 'Returns the current machine entry points, countable outcome, next action, and claim boundary for exactly one named valve,' using a specific verb and resource. It distinguishes from siblings by focusing on a single named valve and listing multiple return elements, unlike a tool like next_action.
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 usage when a specific valve is named and its details are needed, but it does not explicitly mention when to prefer this over sibling tools like next_action or route_reputation_skill. No exclusions or alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, such as status, learning, market listing, retention, and routing. However, route_valve also returns a next action, which overlaps with the dedicated next_action tool, causing slight ambiguity.
Naming conventions are mixed: some tools start with a verb (learn_reputation, list_generation_market, route_reputation_skill, route_valve), while others are noun phrases (gateway_status, next_action, retention_plan). This lack of a consistent pattern makes it harder to predict tool names.
With 7 tools, the server is well-scoped for a reputation gateway that provides status, learning, market, routing, and retention insights. Each tool earns its place, and the count is within the ideal range.
The server covers key advisory functions like status, curriculum, market, next actions, retention, and routing, but is entirely read-only and lacks any tool to list available valves or execute actions. This leaves notable gaps for agents that need to actually interact with the reputation system.