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Evolution Manage Tool

evolution_manage

Manage AI-suggested improvement proposals for agents. List, analyze, approve, apply, or reject evolution proposals to implement AI-driven optimizations.

Instructions

Manage evolution proposals (AI-suggested improvements). Actions: list (status filter), analyze (proposal_id — detailed analysis), approve (proposal_id), apply (proposal_id — execute the improvement), reject (proposal_id, reason).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform: list, analyze, approve, apply, reject
agent_idYesFilter by agent ID (required)
statusNoFilter by status: pending, approved, applied, rejected
limitNoMax results (default 10, max 50)
proposal_idYesThe evolution proposal UUID to approve
reasonNoOptional reason for rejection
Behavior2/5

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

With empty annotations, description carries full transparency burden. It hints at side effects ('execute the improvement' for apply) but fails to disclose safety profile, reversibility of rejections, whether 'apply' is destructive, or error behaviors. Action names imply behavior but explicit risk disclosure is absent.

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

Conciseness4/5

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

Single sentence with parenthetical action list. Front-loaded with resource definition. Dense but efficient—no wasted words. Action specifications are compact and consistently formatted (action_name + param clarification).

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

Completeness3/5

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

Covers primary action workflows but omits critical implementation details: doesn't mention 'agent_id' is required for all actions (per schema), omits 'limit' parameter relevance for list pagination, and provides no output guidance. With 6 parameters and mutation actions, description is minimally sufficient but has clear gaps.

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% (baseline 3). Description adds valuable semantic context for actions: '(status filter)' clarifies list behavior, 'detailed analysis' explains analyze output expectation, and 'execute the improvement' clarifies apply's function beyond the generic verb. These parenthetical notes add meaning not present in schema.

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

Purpose4/5

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

States specific resource (evolution proposals) with clarifying context '(AI-suggested improvements)'. Verb 'manage' is generic but the resource definition distinguishes it from sibling 'manage' tools like agent_manage or workflow_manage. Could explicitly differentiate from 'agent_manage' but adequately identifies the domain.

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

Usage Guidelines2/5

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

Lists available actions and their parameters but provides no selection guidance (e.g., when to 'analyze' vs 'apply', whether approval is required before applying, or prerequisites). No mention of alternatives or when NOT to use this tool.

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

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