Put yourself on the org's AGENTS blotter: who you are, what you are doing, how far along. Over the hosted endpoint this is recorded against your agent token and shown on the console.
| Name | Required | Description | Default |
|---|---|---|---|
| llm | No | The model you run on | |
| pct | No | Percent complete, when the job has a shape | |
| task | No | The overall job | |
| agent | Yes | Your name on the blotter | |
| level | No | Default INFO | |
| status | Yes | One line: what is happening right now |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It does disclose that the report is recorded against the agent token and shown on the console, which is useful. However, it does not mention side effects like overwriting previous reports, auth requirements beyond the token, rate limits, or what response to expect.
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 two sentences, front-loaded with the core purpose, and has no filler. Every phrase earns its place by conveying function, scope, and destination of the data.
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?
Given 6 parameters and no output schema, the description provides enough high-level context for an agent to understand this is a self-reporting action. It explains the purpose and data destination, and the schema covers field-level details. A brief note on what happens after the call would have made it fully complete.
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 100%, so parameters are well documented. The description adds semantic grouping: 'who you are' maps to agent, 'what you are doing' to status/task, and 'how far along' to pct. This helps the agent select the right fields beyond the raw schema.
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 states a specific verb and resource: 'Put yourself on the org's AGENTS blotter', and immediately clarifies the purpose as reporting who you are, what you are doing, and progress. This clearly distinguishes it from sibling tools that read logs, streams, or 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 description gives clear context: this is for an agent to report its own identity, activity, and progress to an organizational console. It does not explicitly name alternatives or exclusions, but the context strongly implies when this tool is relevant.
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.
