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KovaMind

Kova Mind MCP Server

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by KovaMind

memory_recall

Retrieve relevant memory patterns from past interactions to understand user context. Use natural language queries to recall previous knowledge about a user.

Instructions

Retrieve relevant memory patterns for a given context. Use this to recall what you know about a user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYesNatural language context or query to search memories for
user_idNoUser ID (defaults to KOVAMIND_USER_ID env var)
max_patternsNoMaximum number of patterns to return (1-100)
min_confidenceNoMinimum confidence threshold (0.0-1.0)
Behavior1/5

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

With no annotations, the description carries full burden for behavioral disclosure. It does not mention whether the operation is read-only, any authorization needs, rate limits, or side effects. The description is purely functional.

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 with no wasted words. Front-loaded with purpose and usage context.

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?

The tool has 4 parameters, no output schema, and a short description. Missing information includes return format of memory patterns and how context influences recall. Adequate for a simple retrieval tool but not 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?

All parameters have schema descriptions, so baseline is 3. The description adds minimal context (e.g., 'natural language context') but does not explain how min_confidence affects results or how max_patterns interacts. No significant added value beyond schema.

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 verb 'retrieve' and the resource 'memory patterns', and specifies the use case of recalling what you know about a user. This distinguishes it from siblings like memory_extract or memory_reinforce.

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 a general when-to-use ('recall what you know about a user') but does not explicitly exclude alternatives like memory_extract for extracting patterns from text. No comparison with siblings is provided.

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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