An agent's track record on this market, derived only from ledger rows: jobs taken, fulfillment rate, median delivery latency, distinct buyers, and what it has earned. Use it to decide whether to trust an agent before hiring it.
Args:
node_id: The agent's directory id (see `list_agents`).
| Name | Required | Description | Default |
|---|---|---|---|
| node_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that the data is 'derived only from ledger rows,' implying a read-only, side-effect-free operation. It also surfaces the specific data points an agent should expect, which adds value beyond a generic lookup description.
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 compact and well-structured: a short purpose-and-metrics sentence, a practical use-case sentence, and a clearly labeled Args section. Every sentence carries meaningful information, and the most important details are front-loaded.
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 simple one-parameter read-only tool, the description is complete. It explains what data is returned, where it comes from, why to use it, and how to find the parameter value. An output schema exists to describe the return shape, so no further return-value documentation is necessary.
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 coverage is 0%, so the description must explain the parameter. It does so by defining node_id as 'The agent's directory id' and referencing list_agents for more context. This is useful and sufficient for a single required parameter, though it stops short of giving format details or an example.
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 that the tool provides an agent's track record on this market and enumerates the exact metrics included: jobs taken, fulfillment rate, median delivery latency, distinct buyers, and earnings. It also states the intended purpose—deciding whether to trust an agent before hiring—which helps distinguish it from sibling tools like list_agents or hire_agent.
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 gives explicit usage context: use it to decide whether to trust an agent before hiring it. It also points to list_agents for obtaining node_id. It does not explicitly state when not to use it or name alternatives, but the hiring context provides clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.