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Awareness — Memory, Knowledge & Agent Markets

awareness_apply_skill

Apply a learned skill — returns structured step-by-step execution plan.

Call this when a task matches an active skill from awareness_init's active_skills.
Returns the skill's methods, summary, and context-adapted guidance.
Automatically marks the skill as used (resets decay timer).

Args:
    skill_id: ID of the skill to apply (from active_skills).
    context: Current task context — the execution plan will be adapted to this.
    memory_id: Target memory id (auto-resolved if omitted).
    api_key: Bearer token for authentication.

Input Schema

NameRequiredDescriptionDefault
api_keyNo
contextNo
skill_idYes
memory_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose a genuinely non-obvious side effect: it automatically marks the skill as used and resets the decay timer. It also states the return payload (methods, summary, context-adapted guidance). It does not cover failure modes, permissions beyond the api_key param, or rate limits.

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?

Front-loaded with purpose and trigger in the first two lines, followed by return values, side effect, then a compact args list. The 'Args:' block is a bit catalog-style but every line carries information.

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

Completeness4/5

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

No output schema exists, so the description correctly spells out what is returned and flags the decay-timer side effect; auth is covered at the parameter level. Given the tool's moderate complexity, the only gap is error/permission behavior.

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 description coverage is 0%, so the description must compensate, and it does: skill_id is sourced from active_skills, context drives adaptation of the plan, memory_id is auto-resolved when omitted, and api_key is a Bearer token. Only the parameter types/defaults live in the schema, so this is close to fully compensating.

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?

States a specific verb+resource ('Apply a learned skill') and immediately names the output ('structured step-by-step execution plan'), so the agent knows what it gets back. It also implicitly distinguishes itself from the sibling awareness_mark_skill_used by noting it marks the skill used automatically.

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?

Explicit when-to-use condition is given ('when a task matches an active skill from awareness_init's active_skills') and it routes back to the sibling that produces skill_id. No when-not guidance or explicit alternative is offered, which keeps it short of a 5.

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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