quality_checkRun/store a simple machine-readable quality check such as JSON required-fields or non-empty validation.
| Name | Required | Description | Default |
|---|---|---|---|
| result | No | ||
| schema | No | ||
| target_id | Yes | ||
| check_type | No | ||
| target_type | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false and destructiveHint=false. The description adds that the tool can both run and store checks and operates on machine-readable inputs, which is useful context, though it does not disclose persistence details, permissions, or side effects of storing.
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 front-loaded sentence with no filler, and every phrase adds value: the action, the nature of the output, and concrete validation examples. It is appropriately sized for the tool's complexity.
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?
With five parameters, nested objects, no output schema, and no parameter documentation, the description is too thin to fully support correct invocation. An agent would lack clarity on what each parameter represents, how schema and result interact, and what the tool returns or persists.
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 description coverage is 0%, so the description must compensate for undocumented parameters, but it only gives check-type examples and does not explain target_type, target_id, result, schema, or check_type. The examples hint at what check_type might do, but not enough for an agent to confidently supply all five parameters.
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 tool runs/stores a simple machine-readable quality check and gives concrete examples like JSON required-fields or non-empty validation. The title 'Verify an agent outcome' adds context, but the description does not explicitly distinguish this from sibling tools such as project_run_checks.
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 phrase 'simple machine-readable quality check such as...' implies the tool is for lightweight validation tasks, which provides some usage context. However, it does not state when to prefer this tool over alternatives or provide any when-not-to-use guidance.
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.
Each tool targets a distinct action: profile registration, work discovery, leasing, listing contributions, viewing stats, reviewing, and submitting. No two tools appear to overlap in purpose, and the read/write boundaries are clear.
Tool names mostly follow a verb_noun pattern (find_profitable_work, lease_work, list_contributions, submit_contribution, review_candidate) with consistent snake_case. Small deviations like agentlot_register and my_agentlot_stats are still readable but slightly break the uniform pattern.
Seven tools is well-scoped for an agent marketplace workflow. Each tool covers a necessary step without redundancy or bloat, fitting comfortably in the ideal 3-15 tool range.
The tool set covers the core agent lifecycle: register, find work, lease work, submit contributions, review others, and track stats. Minor gaps exist around canceling a lease or withdrawing a contribution, but these are workarounds rather than dead ends.