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ledger

ledger

by Arcaeon-io
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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ledger_append

Append action records to a tamper-evident hash-chained log, returning the chain hash. Use it to log consequential actions, making history verifiable and provably unaltered.

Instructions

Append one action record to a tamper-evident, hash-chained log. Returns the record's chain hash. Use this to log every consequential action (tool calls, payments, decisions) so the history can later be proven unaltered.

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": {
      "record": "<record>"
    },
    "name": "ledger_append"
  }
}

Input Schema

NameRequiredDescriptionDefault
recordYesAny JSON object describing the action (tool, args, result, actor, etc.). A `ts` timestamp is added if absent.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It reveals that the log is tamper-evident and hash-chained, that appending is the operation, and that the tool returns the record's chain hash. It could mention permanence or permission requirements more explicitly, but the core write behavior is transparent.

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?

Two sentences, with the primary action and return value front-loaded, followed by a concise usage directive. Every sentence contributes information and there is no redundant or filler text.

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 single-parameter tool with full schema coverage, the description adequately covers what the tool does, when to use it, and what it returns. It does not explicitly mention the auto-added ts field, but that is already in the schema. Minor gaps such as error behavior or permissions do not make it insufficient.

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?

Schema description coverage is 100% and the schema already documents that 'record' can be any JSON object describing the action, with a ts timestamp added if absent. The description adds no parameter-specific detail beyond calling it an 'action record,' so the schema carries the semantic weight.

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 opens with 'Append one action record to a tamper-evident, hash-chained log,' which states a specific verb, resource, and effect. It also names the return value (chain hash) and clearly separates this append-side tool from verification siblings like ledger_verify and prove_my_conduct.

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 explicit usage context: 'Use this to log every consequential action (tool calls, payments, decisions) so the history can later be proven unaltered.' It does not explicitly call out when not to use it or contrast it with alternatives, but the intended use case is clear.

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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