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agentmem

agentmem

by Thezenmonster
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

recall_memory

Retrieve relevant memories for a topic within token limits to provide context at task start.

Instructions

Get the most relevant memories for a topic, fitted to a token budget. Use at the start of a task to load context.

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": "recall_memory"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
max_tokensNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions token budgeting and context loading, but lacks details on permissions, rate limits, error handling, or what 'most relevant' means algorithmically. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 concise sentences that are front-loaded with the core purpose. Every word earns its place, with no redundancy or fluff, making it highly efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers purpose and usage but lacks details on behavior, parameter semantics, and output, leaving gaps that could hinder effective use by an AI agent.

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?

The input schema has 2 parameters with 0% description coverage, so the schema provides no semantic information. The description adds some meaning by implying 'query' is for a topic and 'max_tokens' controls output size, but it doesn't explain parameter formats, constraints, or interactions. This partial compensation justifies a baseline 3.

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 states the tool's purpose: 'Get the most relevant memories for a topic, fitted to a token budget.' It specifies the verb ('Get') and resource ('memories'), with additional context about token budgeting. However, it doesn't explicitly differentiate from siblings like 'search_memory' or 'list_memories', which limits the score to 4.

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 provides clear usage context: 'Use at the start of a task to load context.' This gives a specific when-to-use guideline, but it doesn't mention when not to use it or name alternatives among the many sibling tools (e.g., 'search_memory'), so it falls short of a perfect 5.

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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