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agent_report

Report your agent's identity, current task, and progress to the shared AGENTS blotter on a set schedule, so stalled jobs are visible and silent agents stand out.

Instructions

Put yourself on the bench's AGENTS blotter: say who you are, which LLM you run on, what you are doing, and how often to expect you. For a long job (an 8-hour build, an overnight proof search), call this on a schedule - every interval_s, default 900 (15 minutes) - AND on events: milestones, errors (level WARN/ERROR), and done. The blotter shows your last status and greys you when you miss your own promised cadence, so a silent agent LOOKS silent. This is the single deliberate write this otherwise read-only server performs, and it can only land on agent.* status topics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
llmYesThe model you run on (e.g. "claude-fable-5") - the blotter displays it
pctNoPercent complete, when the job has a shape
taskNoThe overall job (e.g. "8h proof search, mathlib bump")
agentYesYour name on the blotter (e.g. "proof-search", "nightly-builder")
levelNoDefault INFO; WARN/ERROR when the status IS the problem
statusYesOne line: what is happening right now
interval_sNoHow often you promise to report (default 900); the blotter greys you at 2x

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations present, the description carries the full behavioral burden and does so thoroughly: it discloses that the blotter surfaces the last status, greys out agents that miss their promised cadence, that the report can only land on agent.* status topics, and that it is a write operation on an otherwise read-only server. This gives an agent a complete picture of side effects and monitoring semantics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: purpose, cadence, event triggers, and monitoring behavior are all packed into a few sentences, with the core purpose front-loaded. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter write tool with no annotations and no output schema, the description covers purpose, usage cadence, behavioral consequences, and parameter semantics. An agent has everything needed to invoke it correctly without relying on external context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so baseline is 3, but the description adds valuable meaning beyond the schema: it explains interval_s default and the 2x grey threshold, clarifies when pct is relevant ('when the job has a shape'), and specifies level defaults and usage (INFO default, WARN/ERROR when status is the problem). This helps an agent choose parameter values correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Put yourself on the bench's AGENTS blotter') and names the resource (the blotter) and the exact data to report (identity, LLM, activity, cadence). It clearly distinguishes itself from sibling read-only tools like list_webhooks or tail_logs by emphasizing this is the server's sole deliberate write.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit scheduling guidance: call on a cadence (interval_s, default 900) and on events (milestones, errors, done). It also frames the tool as the only deliberate write on an otherwise read-only server, which implicitly signals when not to use it, though it does not name alternative tools explicitly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.