retention_planReturns receipt-bound D1 and D7 definitions for one valve and explicitly withholds retention claims when a genuine verified activation cohort does not exist.
| Name | Required | Description | Default |
|---|---|---|---|
| valve | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond these: the result is 'receipt-bound' and retention claims are explicitly withheld when a genuine verified activation cohort does not exist, which is a meaningful edge-case behavior not captured by annotations.
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 and front-loads the core purpose, making it concise and structurally sound. However, terms like 'receipt-bound' and 'genuine verified activation cohort' are jargon-heavy and could be clearer, slightly reducing readability.
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?
The tool has no output schema, so the description must convey what the agent can expect. It explains the high-level return concept and a critical caveat, but it does not specify what the returned 'definitions' contain or how the withholding of claims is represented in the response. This leaves some ambiguity but is adequate for a relatively simple read-only tool.
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 schema provides a single 'valve' parameter with an enum, but the description offers no additional meaning for the enum values (colloquium, open_standing, agentic_substrate_prelaunch). Since schema description coverage is 0%, the description should compensate but only repeats that the operation applies to 'one valve' without clarifying the semantic distinctions.
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 action ('Returns') and the specific resource ('receipt-bound D1 and D7 definitions') for one valve. It also highlights a unique withholding behavior that distinguishes it from sibling tools like route_valve and gateway_status.
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 intended use is implied rather than explicit: an agent would use this when needing D1/D7 retention definitions for a valve. However, there is no direct statement of when to use it vs alternatives, nor any exclusions or conditions for choosing another tool.
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.