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Cachly — AI Cognitive Brain

Cachly — AI Cognitive Brain

by cachly-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

brain_import_meta

Import k-anonymous meta-lessons from the global Brain commons into your local Brain to gain collective insights without revealing individual data. Filter by category to target relevant patterns.

Instructions

Import k-anonymous meta-lessons from the global Brain commons into your local Brain. Meta-lessons are derived from ≥ k independent org signals — no individual org data is revealed. Imported lessons get state="meta" and never overwrite your own lessons. Filter by category to target relevant patterns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "instance_id": "<instance_id>"
    },
    "name": "brain_import_meta"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax meta-lessons to import (default 20, max 200)
categoryNoOptional filter, e.g. "auth" or "deploy"
instance_idYesBrain instance ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.47

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses key behaviors: imported lessons get state='meta', never overwrite local lessons, and are k-anonymous. This goes well beyond the input schema. However, it omits details like authentication requirements or what happens on failure, preventing a top score.

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

Conciseness5/5

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

Three sentences, front-loaded with the primary action, no filler. Every sentence adds essential information about purpose, data origin, behavior, and optional filtering. Efficient and clear.

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?

The description covers purpose, behavior, and filtering but lacks information about the return value (no output schema) and prerequisites (e.g., must have a local Brain instance). Given the tool's simplicity (3 params, no nesting), it is adequate but not fully complete.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents all three parameters. The description adds only a brief usage hint ('Filter by category to target relevant patterns') for the category parameter, which provides marginal value beyond the schema. Baseline 3 is appropriate.

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 'Import k-anonymous meta-lessons from the global Brain commons into your local Brain'—a specific verb+resource. It explains what meta-lessons are and distinguishes from potential siblings like brain_import by noting they are derived from k independent org signals and never overwrite local lessons.

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

Usage Guidelines3/5

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

The description implies usage through the action and filter hint ('Filter by category to target relevant patterns'), but it does not explicitly state when to use this tool over alternatives (e.g., brain_import, brain_share) or when not to use it. No exclusions or context for decision-making are provided.

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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