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LogicMem MCP Server

LogicMem MCP Server

by CreedLab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

logicmem_memory_outcome

Record the usefulness of stored memories to feed the DPO training pipeline and refine AI memory retrieval.

Instructions

Record whether a stored memory was useful. Feeds the DPO training pipeline.

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": {
      "memory_ids": "<memory_ids>",
      "success": "<success>"
    },
    "name": "logicmem_memory_outcome"
  }
}

Input Schema

NameRequiredDescriptionDefault
successYes
magnitudeNo
memory_idsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It states the intent to record memory usefulness and feed a training pipeline, but it does not mention whether the operation appends, overwrites, requires existing memory entries, or what happens with the success/magnitude values. The observable effects beyond 'record' are unclear.

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 two short sentences with no redundant phrasing. It is front-loaded with the core action and purpose, and every word 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?

For a tool with three parameters, no annotations, and no output schema, the description is under-specified. It lacks details on magnitude semantics, when to call the tool, return behavior, and any side effects, making it insufficient for reliable invocation.

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?

The schema has zero description coverage, so the description must compensate. It partially maps memory_ids to stored memories and success to usefulness, but magnitude is entirely unexplained, and no value ranges or conventions are provided. This leaves a key parameter ambiguous.

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 identifies the action ('Record'), the target ('whether a stored memory was useful'), and the downstream purpose ('Feeds the DPO training pipeline'). This distinguishes it from sibling tools like logicmem_memory_log (logging) and logicmem_memory_recall (retrieval).

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 implies a usage context by mentioning the DPO training pipeline, but it does not explicitly state when to use this tool versus alternatives, nor does it provide conditions or exclusions. It lacks direct guidance on when this feedback should be recorded.

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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