nagi-ledger

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ledger_stats

Read-onlyIdempotent

Aggregate recent audit ledger actions, dispatches, and verdicts into categorized statistics (by tier, category, and outcome) for a specified number of days.

Instructions

Aggregate statistics over actions and dispatches from the last N days.

Args: days (int): How many days back to include. Defaults to 7.

Returns: dict: { "actions_by_tier": {"0": int, "1": int, "2": int}, "actions_by_category": {category: int, ...}, "dispatch_count": int, "verdicts": {"CONFIRMED": int, "REFUTED": int, "PARTIAL": int, "PENDING": int} }

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": "ledger_stats"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds behavioral context by explaining the aggregation scope (actions and dispatches over N days) and the exact return structure, which goes beyond what annotations provide.

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 concise and well-structured, with clear 'Args' and 'Returns' sections. It front-loads the primary purpose and then delivers essential parameter and return information without any filler words.

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

Completeness5/5

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

The description is complete for a read-only aggregation tool: it specifies the parameter, default value, and full return dictionary with keys and types. The presence of an output schema and clear annotations further round out the context, making the tool self-sufficient.

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

Parameters5/5

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

The schema parameter 'days' has no description in the schema, but the tool description fully compensates by stating 'How many days back to include. Defaults to 7.' This adds clear semantic meaning and default behavior beyond the bare schema definition.

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: 'Aggregate statistics over actions and dispatches from the last N days.' This is a specific verb+resource combination that clearly distinguishes it from sibling logging tools like ledger_log_action or ledger_log_dispatch, which are write operations.

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 implies the tool is for reading summary statistics rather than logging individual actions, providing clear context for when it would be used. It does not explicitly mention alternatives or exclusions, but the contrast with sibling tools is evident from the tool's aggregating 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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