Recall an agent: flags it and quarantines every transitive descendant. Reports exactly which agents were quarantined.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | No | ||
| agent_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses the destructive behavior (flags and quarantines) and the reporting of affected agents. It clearly identifies this as a mutation tool.
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?
Two sentences efficiently convey the action and output, with no extraneous information.
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?
While the description covers the basic operation and output, it omits parameter explanations. Given the output schema exists, the reporting aspect is sufficient, but the lack of parameter detail reduces completeness.
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%, and the description does not explain the purpose or usage of the 'reason' and 'agent_id' parameters, leaving agents to infer from the schema alone.
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 action ('Recall an agent') and specifies the effect: flags it and quarantines every transitive descendant. This distinguishes it from read-only sibling tools like describe_agent or lineage.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites, context, or situations where other tools would be more appropriate.
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 operation: describing, fetching, listing, registering, recalling, or verifying. No two tools overlap in purpose.
All tool names follow a verb_noun pattern in snake_case (e.g., get_artifact, register_genesis, list_records), with only 'lineage' and 'recall' being single-word verbs but still consistent in style.
11 tools cover the agent provenance domain thoroughly without redundancy. The count is well-balanced for managing genesis, artifacts, and verification.
The tool surface covers creation, retrieval, listing, verification, and recall for both agents and artifacts. No obvious gaps for an immutable provenance system.