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LogicMem MCP Server

LogicMem MCP Server

Official
by LogicMem
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

logicmem_memory_recall

Search persistent memory for relevant entries using a query to support AI agent recall and reasoning.

Instructions

Search memory for relevant entries.

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": "logicmem_memory_recall"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax entries
queryYesSearch query
client_idNoClient ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2.9/5.0
Behavior2/5

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

No annotations provided, so the description bears full weight. It discloses that this is a read (search) operation but nothing about the search mechanism, whether it uses semantic or keyword matching, or what constitutes 'relevant.' No exclusions or side effects are mentioned.

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?

A single sentence with no redundant words. It's appropriately concise and immediately comprehensible.

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?

There is no output schema, and the description doesn't specify the return format, pagination, or how limit/client_id influence results. The lack of context about the memory domain and the relationship to sibling tools makes it incomplete for an agent deciding to call this tool.

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 three parameters have schema descriptions (query, limit, client_id), so the baseline is 3. The tool description adds no additional parameter semantics beyond what's in the schema.

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 identifies a search operation targeting memory entries. It distinguishes from other sibling tools in that it's a retrieval action, though it doesn't explicitly name alternatives.

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 versus the other memory or a2a tools. The description gives no context about what 'relevant' means or when to prefer recall over log/reflect/session.

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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