Skip to main content
Glama
KovaMind

Kova Mind MCP Server

Official
by KovaMind

memory_extract

Parse conversation messages to extract and store user memory patterns, enabling personalized insights and persistent learning.

Instructions

Extract memory patterns from a conversation. Parses messages and stores learned patterns about the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversationYesArray of conversation messages with role and content
user_idNoUser ID (defaults to KOVAMIND_USER_ID env var)
session_idNoOptional session ID for grouping extractions
Behavior2/5

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

No annotations exist, so description must carry full burden. It mentions storage of patterns but does not disclose side effects (e.g., overwriting existing patterns), permissions needed, or any destructive potential.

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 sentences, no redundant words, front-loaded with the main action. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should explain return values or effects. It does not. Also lacks error conditions, authentication needs, or data retention details.

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 description adds no extra meaning beyond what the parameter descriptions already provide. Baseline 3 is appropriate.

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 verb 'extract' and resource 'memory patterns from a conversation', and mentions storing patterns. It is distinct from sibling tools like memory_recall (retrieve) and memory_reinforce (strengthen), but does not explicitly differentiate.

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?

No guidance on when to use this tool vs. siblings. No exclusions or prerequisites are provided, leaving the agent to infer usage from the name alone.

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/KovaMind/mcp-server'

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