list_generation_marketReturns evidence-bound agent-generation and launch platforms, machine-native distribution surfaces, supported count status, and separate verified-flow and relevance rankings.
| Name | Required | Description | Default |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that it returns 'supported count status' and 'separate verified-flow and relevance rankings,' but does not explain pagination, data freshness, or what 'supported count status' means. With annotations in place, this is adequate but not rich.
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, front-loaded with 'Returns,' but the dense jargon ('evidence-bound', 'machine-native distribution surfaces') makes it less concise than it could be. Every phrase contributes information, but clarity suffers from over-packaging.
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 must explain the return value. It lists several components (platforms, surfaces, status, rankings) but does not clarify their structure or meaning. The description is somewhat cryptic, leaving an agent without full context to use the tool effectively.
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 zero parameters, and the schema completely describes this (coverage 100%). Per the rubric, the baseline for 0 params is 4, and the description does not need to add parameter information. It neither helps nor hurts.
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 uses a specific verb ('Returns') and names a clear resource ('agent-generation and launch platforms' related to the agent-generation market), distinguishing it from siblings like gateway_status or route_valve. However, terms like 'evidence-bound' and 'machine-native distribution surfaces' are vague jargon, slightly muddying the exact purpose.
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 the tool is used to retrieve market platforms and rankings, but it gives no explicit when-to-use guidance or mentions alternatives. There is no exclusion context, so an agent must infer usage from the return-value description alone.
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.