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hjarni

by com.hjarni
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instructions-get

Read-onlyIdempotent

Get LLM instructions at the specified level. Call with level 'brain' early in conversations to learn user preferences. Optional: level ('brain'|'personal_root'|'container'|'team'), defaults to 'brain' if omitted or blank; the response echoes resolved_level and defaulted_level (true when the level was defaulted). Optional: id (integer, required for 'container' and 'team' levels). 'container' level takes a personal (or shared) container id and returns the full inheritance chain, outermost first; each entry carries a level field ('brain'|'personal_root'|'team'|'container'). Team container ids are not addressable here — read a team note's chain from notes-get, or the team's own instructions with level 'team'. For an agent token, level 'brain' also returns agent_instructions, written by its person for that agent alone; brain_instructions_off: true means its person kept their brain instructions from it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoContainer ID or Team ID (required for 'container' and 'team' levels)
levelNoInstruction level: 'brain' (global), 'personal_root', 'container', or 'team'. Defaults to 'brain' if omitted or blank.brain

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / level / default
      Added value: +"brain"
    • changedInput schema / properties / level / description
      Previous value: -"Instruction level: 'brain' (global), 'personal_root', 'container', or 'team' (required)"New value: +"Instruction level: 'brain' (global), 'personal_root', 'container', or 'team'. Defaults to 'brain' if omitted or blank."
    • changedInput schema / required
      Previous value: -[
-  "level"
-]New value: +[]
  2. Changed1 schema field changed
    • changedInput schema / properties / level / description
      Previous value: -"Instruction level: 'brain' (global), 'personal_root', 'container', or 'team'"New value: +"Instruction level: 'brain' (global), 'personal_root', 'container', or 'team' (required)"
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover read-only, idempotent, non-destructive, but the description adds behavior the structured fields cannot: the response echoes resolved_level and defaulted_level (with semantics of when it is true), container returns the full inheritance chain outermost-first with per-entry level fields, and agent tokens additionally receive agent_instructions with a brain_instructions_off flag. That is substantive disclosure of return semantics and edge cases.

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 the core action and the primary recommended invocation, and every clause carries information (defaults, requirements, inheritance ordering, agent-specific fields). It is dense and passes over semicolon-joined clauses that could be split, but there is no filler.

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?

No output schema exists, and the description nonetheless describes the response shape (resolved_level, defaulted_level, inheritance chain, agent_instructions). All parameters are covered in schema and description, no required params, and the level-specific routing gaps are closed.

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%, so baseline would be 3, but the description goes beyond the schema by tying id to specific levels (required for 'container' and 'team'), clarifying that 'container' accepts a personal or shared container id, and flagging that team container ids are not addressable here. That adds real selection meaning.

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 ('Get LLM instructions') and immediately scopes it by level, which is the tool's distinguishing axis versus instructions-update. An agent can identify the resource, the enumeration of levels, and the default without opening the schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use guidance ('Call with level "brain" early in conversations to learn user preferences') and names alternatives with the condition that selects them: team container ids route to notes-get or level 'team'. Both the trigger and the exclusions are stated.

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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