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LogicMem MCP Server

LogicMem MCP Server

by CreedLab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

logicmem_memory_reflect

Evaluate a draft answer against retrieved memories. Identify gaps to improve response quality.

Instructions

Self-critique: evaluate a draft answer against retrieved memories.

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": {
      "draft_answer": "<draft_answer>",
      "question": "<question>"
    },
    "name": "logicmem_memory_reflect"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYes
draft_answerYes
memory_queryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions 'self-critique' but doesn't state whether the tool is read-only, whether it modifies memories, or what side effects (if any) occur. The lack of safety or mutability context is a significant gap.

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 a single, front-loaded sentence that immediately conveys the core purpose. It avoids redundancy and unnecessary detail, earning its place with clear, direct language.

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?

Given the tool's complexity (3 parameters, no output schema, no annotations), the description is too sparse. It fails to describe return values, usage context, or parameter specifics, leaving the agent with insufficient information to correctly invoke and interpret the tool. The single-sentence description covers the purpose but not the operational 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 does not compensate. It implies that 'draft_answer' is the answer to evaluate, but it fails to explain the role of 'question' and 'memory_query'—especially memory_query, which seems optional but ambiguous. No detail is provided on how parameters relate to the evaluation process.

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 tool's function: 'Self-critique: evaluate a draft answer against retrieved memories.' It uses a specific verb ('evaluate') and identifies its resource (draft answer vs. memories), distinguishing it from sibling tools like memory_recall (which retrieves) and memory_log (which writes).

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 alternatives such as logicmem_memory_recall or logicmem_memory_outcome. It doesn't specify prerequisites, typical scenarios, or exclusions, leaving the agent to infer usage solely from the purpose.

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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