Skip to main content
Glama
liyexiaoyi

mnemosis-mcp

by liyexiaoyi

plan_support

Retrieve supporting memories for each plan step, giving the agent execution context sourced from long-term memory to enhance decision-making.

Instructions

Retrieve supporting memories for each plan step so the agent executes with context (working memory pulls from long-term memory; Baddeley & Hitch 1974).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planYes
top_kNo
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. The verb 'Retrieve' implies a read operation, but the description does not explicitly state that it is non-destructive, nor does it mention any side effects, prerequisites, or return format. This leaves significant behavioral ambiguity.

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 sentence that front-loads the core action. The parenthetical theoretical citation adds conceptual context but could be seen as slightly extraneous; overall it remains concise and well-structured.

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, no parameter descriptions, and only a minimal description. The agent does not know the return structure, how top_k affects results, or how to handle edge cases like empty plans or missing memories. This is too sparse for robust 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 input schema has zero descriptions for its parameters (plan and top_k), so the description must compensate. It clarifies that 'plan' is a set of steps and that memories are retrieved for each step, but it does not explain the structure of plan items or the meaning/effect of top_k. This is only partial compensation.

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 a specific verb ('Retrieve') and resource ('supporting memories for each plan step'), and it distinguishes itself from sibling memory tools by linking to plan steps. The purpose is unambiguous and specialized.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys a clear usage context: retrieving memories to give the agent context during plan execution. It references working memory theory but does not explicitly name alternatives or exclusions, so it stops short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/liyexiaoyi/Mnemosis'

If you have feedback or need assistance with the MCP directory API, please join our Discord server