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AI Process Manager

AI Process Manager

by aipm-engine
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JavaScript
Local

get_economy_stats

Read-only

Retrieves local economy metrics: token/time savings vs screenshots, queries served, success rates, top apps, trends, distinguishing measured vs agent-reported savings.

Instructions

Local economy metrics: tokens and time saved by structured state vs screenshots, queries served, task/action success rates, top apps, 24h/7d trends. Two independent views of savings — economy_measured_by_api (measured here) and economy_reported_by_agents (self-reported): never add them together. The store block states which local database the totals came from.

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": "get_economy_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already mark this as read-only and non-destructive, so the description only needs to add extra context. It does so by disclosing the two independent savings views and explicitly warning against summing them, which is a crucial semantic behavior. It also notes the store block identifies the source database.

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?

Three sentences deliver a dense but well-organized summary: what metrics, what critical caveat, and what the store block means. No filler or redundant wording.

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

Completeness4/5

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

For a read-only, parameterless tool, the description covers the main output categories, the data-source distinction, and a warning. Without an output schema, it could be slightly more explicit about return format, but the stated content is sufficient for an agent to understand what this tool provides.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, the schema provides no parameter details, but the description compensates by enumerating the dataset contents (metrics, trends, views). This exceeds the baseline for parameter-less tools by giving useful semantic structure.

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 provides local economy metrics including tokens/time saved, queries served, success rates, top apps, and trends. This specific resource and scope distinctly separate it from sibling tools like get_system_status or get_recent_events.

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 gives clear context for when this tool is relevant (querying economy statistics) but does not explicitly contrast it with alternatives or state when not to use it. The warning about never adding the two savings views implies careful usage but is not a usage guideline.

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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