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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_eval_recall

Read-onlyIdempotent

Evaluate memory retrieval accuracy by running a generic recall benchmark on a dataset path or a built-in fixture.

Instructions

v11.0 Phase 8: generic recall benchmark on a dataset path or a small built-in fixture. Same payload shape as memory_eval_locomo.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "memory_eval_recall"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNofast
limitNo
top_kNo
dataset_pathNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

The description adds little beyond the annotations. It mentions accepting a dataset path or built-in fixture but does not disclose expected side effects, return behavior, or how the benchmark is executed.

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 brief and to the point, with no redundant information. It efficiently conveys the core purpose in two sentences.

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?

The description is incomplete for a benchmark tool. It does not explain what the benchmark measures, what output to expect, how to interpret results, or any additional context needed to use the tool effectively.

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?

Only dataset_path is implicitly referenced via 'dataset path or built-in fixture'. The parameters mode, limit, and top_k are not explained, and the schema provides no descriptions, leaving most parameters ambiguous.

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 indicates a generic recall benchmark and distinguishes it from memory_eval_locomo by referencing the same payload shape. It names the resource (recall) and implies evaluation, though it could be more explicit about the exact action.

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 is provided on when to use this tool over alternatives. The reference to memory_eval_locomo is helpful but does not explain selection criteria or use cases.

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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