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agy_computer_use.mcp_server

agy_computer_use.mcp_server

by orakulzero
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

cu_merkle_audit

Retrieves RFC 6962 SHA-256 Merkle audit roots, levels, and verified receipts to verify audit trail integrity.

Instructions

Retrieve RFC 6962 SHA-256 Merkle audit root, levels, and verified receipts.

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": "cu_merkle_audit"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

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 implies a read operation through the verb 'Retrieve' but says nothing about read-only safety, authentication, rate limits, or whether the operation contacts a remote service.

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?

A single front-loaded sentence with no filler. Every term names a specific returned artifact: audit root, levels, and verified receipts.

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?

The description lists the returned components (root, levels, receipts), which is useful since no output schema exists. However, it gives no detail about the shape or format of those values, and lacks behavioral context, leaving some gaps for an agent invoking it the first time.

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?

The tool has zero parameters, so the baseline is 4. The schema is empty and there is no parameter-level ambiguity to resolve.

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 gives a specific verb (Retrieve) and a precise resource (RFC 6962 SHA-256 Merkle audit root, levels, and verified receipts). It is unmistakably different from every sibling, which are all computer-use automation tools.

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?

There is no explicit guidance on when to call this tool, what preconditions apply, or what alternatives exist. The auditing use case is only implied by the resource name, leaving an agent to infer context.

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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