Skip to main content
Glama

rules

Manage behavioral rules to customize AI assistant preferences. Add, update, remove, or list rules that persist across sessions and guide how the assistant should work with your code.

Instructions

Manage behavioral rules (add, update, remove, list). Check active rules at the start of each session to load the user's behavioral preferences. Rules persist across sessions and guide how you should work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
contentNoRule content (required for add/update)
labelNoRule label (max 15 chars, format: word-word-word, e.g., "prefer-uv", "use-pytest"). Required for add/update/remove.
scopeNoRule scope (default: global)
projectIdNoProject ID for project-scoped rules
titleNoRule title (max 50 chars)
tagsNoTags for categorization (max 5 tags, max 20 chars each)
priorityNoRule priority (higher = more important)
limitNoMax rules to return for list (default: 50)
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context: rules persist across sessions, guide agent behavior, and should be checked at session start. However, it doesn't cover important behavioral aspects like authentication requirements, error handling, rate limits, or what happens during rule conflicts. The description provides some value but leaves significant gaps for a mutation tool.

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 appropriately sized (two sentences) and front-loaded with the core purpose. The first sentence clearly states what the tool does, and the second provides important context. There's minimal waste, though the second sentence could be slightly more concise.

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?

For a 9-parameter mutation tool with no annotations and no output schema, the description provides adequate but incomplete context. It explains the purpose and persistence behavior but doesn't cover return values, error cases, or detailed behavioral expectations. Given the complexity and lack of structured data, the description should do more to be 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 description coverage is 100%, so the schema already documents all 9 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'behavioral rules' which gives context for the parameters, but no specific syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.

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 clearly states the tool's purpose: 'Manage behavioral rules (add, update, remove, list).' It specifies the resource (behavioral rules) and the four main actions. However, it doesn't explicitly distinguish this tool from its siblings (grep, list, retrieve, search, store), which appear to be more general-purpose tools.

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 provides some usage context: 'Check active rules at the start of each session to load the user's behavioral preferences. Rules persist across sessions and guide how you should work.' This implies when to use the tool (session initialization, preference management) but doesn't explicitly state when to use this vs. alternatives or provide exclusions. The guidance is helpful but not comprehensive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ChrisGVE/workspace-qdrant-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server