Universal AI Memory MCP Server

by vigneshsai890

Overview Schema Source Related Servers Discussions Admin

Python

Local

retrieve

Fetch memories from a secure local store by ID or entity, with adjustable limit and newest-first sorting.

Instructions

Retrieve memories; requires read and rejects ambiguous selectors.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "retrieve"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`entities`	No
`memory_id`	No
`newest_first`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 8, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description discloses useful behavioral traits: it requires read permission and rejects ambiguous selectors. However, it doesn't state whether it is read-only, how it handles missing memories, or anything about pagination or return format beyond the ambiguous mention of selectors.

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 is exceptionally concise at just 9 words, front-loaded with the core action, and every word adds value. It conveys purpose, a key requirement, and a behavioral constraint without waste.

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?

For a 4-parameter tool with no annotations, this description is too sparse. It omits usage guidance, parameter semantics, and behavioral details like non-mutating status or error handling. The existence of an output schema helps with return values, but there are significant gaps in context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds almost no parameter-specific meaning. It vaguely refers to 'selectors' but doesn't clarify that memory_id and entities are the selectors, and it says nothing about limit or newest_first. The description fails to compensate for the lack of schema descriptions.

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 uses a specific verb+resource ('Retrieve memories') and hints at a distinct behavior ('rejects ambiguous selectors'). However, it doesn't explicitly differentiate from the sibling 'search' tool, which also retrieves memories, so sibling differentiation is weak.

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 phrase 'rejects ambiguous selectors' implies this tool is for direct, unambiguous retrieval when you have exact memory IDs or entities, but it doesn't explicitly state when to use it over 'search' or provide alternatives. Usage context is implied rather than explicit.

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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