Skip to main content
Glama

sync_system_memory

Saves auto-generated system documentation to persistent memory, enabling AI to recall project structure and business rules in future conversations. Run after code changes to update memory.

Instructions

Create or update the .agents/memory/ folder with auto-generated system documentation. This folder serves as AI's 'long-term memory' — it persists between conversations. After calling this, AI in any future conversation can read these files to understand the full system flow without re-analyzing. Call this after completing any code changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoProject name or path
businessRuleNoOptional: A new business rule to add to the memory (e.g. 'VIP users get free shipping')
changeDescriptionNoOptional: Description of what was just changed (for the changelog)
Behavior3/5

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

Without annotations, the description carries full burden. It explains persistence and future readability but does not disclose overwrite behavior, specific files generated, or any side effects. Adequate but not comprehensive.

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?

Two concise sentences front-load the main action and purpose, with no extraneous information. Every sentence adds value.

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?

Given 3 optional parameters, no output schema, and no annotations, the description covers the tool's purpose, usage timing, and persistence benefit. Could mention generated file types but overall 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 baseline is 3. Description adds no additional parameter meaning beyond the schema's descriptions (project, businessRule, changeDescription).

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?

Clearly states 'Create or update the .agents/memory/ folder with auto-generated system documentation,' specifying verb, resource, and purpose. Distinct from sibling tools like generate_feature_flow_diagram, which focus on diagrams rather than persistent memory.

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?

Explicitly advises 'Call this after completing any code changes' and explains the folder serves as AI long-term memory persisting between conversations. Lacks explicit when-not-to-use or alternatives, but context is clear.

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/giauphan/codeatlas-mcp'

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