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Universal AI Memory MCP Server

Universal AI Memory MCP Server

by vigneshsai890
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search

Find memories by natural language query, with optional entity filters and result limits. Retrieve relevant information from the secure local memory store.

Instructions

Search memories; requires the read capability.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes
entitiesNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait by requiring the read capability, implying a read-only operation. However, it does not elaborate on search semantics (e.g., fuzzy vs. exact) or potential side effects, leaving gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that front-loads the core action and includes a useful prerequisite. It wastes no words, though its briefness borders on under-specification.

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?

The description is incomplete for the tool's complexity. It doesn't explain when to use search vs. retrieve, how the query/limit/entities parameters affect results, or any behavioral nuances. The presence of an output schema mitigates the need for return-value explanation, but other context is lacking.

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

Parameters1/5

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

Schema description coverage is 0%, and the description makes no mention of the parameters (query, limit, entities). It provides zero added meaning beyond the schema's structural definitions, failing to compensate for the lack of parameter documentation.

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 states the tool's action ('Search') and resource ('memories'), making its purpose clear. It does not explicitly differentiate from the sibling tool 'retrieve', but the verb 'search' implies query-based lookup, which subtly distinguishes it.

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?

The description provides no guidance on when to use this tool versus the sibling tools (store, retrieve, forget). It only mentions a prerequisite ('requires the read capability'), which is not usage direction.

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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