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concept_manage

Destructive

Prune, consolidate, or re-grade system-generated concepts. Remove outdated ones, merge similar with evidence carry-over, or adjust confidence levels to maintain accurate knowledge.

Instructions

Prune, consolidate, or re-grade concepts — the curator's eviction path.

Concepts are ALL system-generated (there is no foreground/pinned concept class the way lessons/skills have one), so unlike lesson_remove this tool needs no force escape hatch: every concept is fair game for curation. The guard is simply that the target id must exist.

action='remove' — delete one concept (the curator's PRUNE_CONCEPT). action='consolidate' — keep concept_id, fold each id in merge_ids (comma-separated) into it — their triangulation notes carry over, confidence rises to the max, last_evidence_at is bumped — then delete the merged-away rows (CONSOLIDATE_CONCEPT). action='set_confidence' — re-grade concept_id to confidence ∈ {low, medium, high} (a confidence review).

reason is recorded on the event trail for the human audit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
reasonNo
merge_idsNo
concept_idYes
confidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond annotations (destructiveHint=true), the description details what each action does, including side effects like consolidation carrying over notes and raising confidence. It also explains that no force escape hatch is needed, providing full behavioral context.

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?

The description is well-structured with paragraphs and bullet points, but it is somewhat lengthy. Each sentence earns its place, though slight tightening could improve conciseness.

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?

Given the parameters, complexity, and presence of output schema, the description covers all necessary aspects: actions, parameter details, behavioral implications, and relationship to other tools. It is fully complete for an agent to use the tool correctly.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining each parameter: action options, confidence values, merge_ids as comma-separated, and the role of reason. It adds significant meaning beyond the bare schema.

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 clearly states that the tool is for pruning, consolidating, or re-grading concepts. It lists three specific actions and distinguishes itself from sibling tools like lesson_remove by noting that concepts are all system-generated and no force flag is needed.

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 guidance on when to use each action and notes that every concept is fair game. It mentions the guard that target id must exist. However, it does not explicitly contrast with other concept-related tools like register_concept, but the context is sufficient.

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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