Skip to main content
Glama

mcp_engram_set_memory_mode

Switch between lean and deep memory modes to balance recall speed and quality. Lean mode uses bounded recall on large stores; deep mode enables full BVH build for high-quality retrieval.

Instructions

Switch agent memory mode: lean (default, bounded recall on large stores) or deep (auto-spawns full BVH build for quality recall). Takes effect immediately for this process.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYeslean = fast/low RAM; deep = full GPU/BVH recall on large manifolds
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the immediate effect and the core behavior of each mode (bounded recall vs. full BVH build). However, it omits potential side effects such as performance impact, persistence across sessions, or whether mode switching is destructive to existing data.

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?

The description consists of exactly two sentences with no extraneous information. The key action and mode distinctions are front-loaded, making it immediately scannable for an AI agent.

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 the tool's simplicity—one required enum parameter, no output schema—the description is largely complete. It explains the modes and the immediate effect. Minor omissions like persistence or cross-process scope are acceptable but prevent a perfect score.

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?

The input schema already provides 100% coverage with an enum and a description for the only parameter. The description enriches this by explaining what each mode does operationally ('bounded recall' vs. 'auto-spawns full BVH build'), adding significant contextual value beyond the schema's simple labels.

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 the tool's action ('Switch agent memory mode'), identifies the resource ('agent memory mode'), and distinguishes between two specific modes ('lean' and 'deep') with concise behavioral summaries. This differentiates it from sibling tools like mcp_engram_recall or mcp_engram_rebuild_bvh.

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 by listing the two modes and their characteristics but does not explicitly state when to choose one over the other or provide guidance on alternatives. The acknowledgment that the change affects the current process helps, but lacks proactive selection criteria.

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/staticroostermedia-arch/engram'

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