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MegaBrain

MegaBrain

by dougrn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

brain_list_rules

List all active rules for a given project and global rules to ensure AI coding assistants apply mandatory specs before generating code.

Instructions

List all active rules for the given project including global rules.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "brain_list_rules"
  }
}

Input Schema

NameRequiredDescriptionDefault
projectNoglobal

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose two useful traits — only *active* rules are returned and global rules are always merged into the result — but it does not state the read-only nature explicitly, nor anything about ordering, volume, or pagination.

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?

A single well-formed sentence with the scope front-loaded and zero filler. Nothing in it is redundant.

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?

An output schema exists, so return-value detail is not needed, and the scope sentence covers the main ambiguity about global rules. The remaining gap is the absence of any usage/routing guidance for a tool sitting among six siblings.

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?

There is one parameter with 0% schema description coverage, so the schema explains nothing. The description adds partial meaning by framing the parameter as the project scope and clarifying that global rules are included regardless, but it does not explain the 'global' default or what happens when the argument is omitted.

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?

The description gives a specific verb (List) and resource (rules) plus the scope (active rules for the given project, including global rules). It is clearly distinguishable from the write-side siblings like brain_record_rule, though it never names an alternative tool explicitly.

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?

There is no guidance on when to call this versus siblings such as brain_consult or brain_stats, and no stated prerequisites or exclusions. Usage is only implied by the verb 'List'.

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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