evidence-ledger

get_openai_ledger

Returns the free public OpenAI security evidence ledger — 55 fixed fields, each either Documented with a vendor-published source or recorded as a Question surfaced. This is the complete Public Edition record, Dossier No. 003: 47 fields Documented and 8 Question surfaced across 10 domains, free, dated to its evidence date. Documented fields carry the vendor's own quoted wording and the source URL it was found on; Question surfaced fields carry the follow-up question a buyer can put to the vendor in writing. Question surfaced means the reviewed vendor-published sources did not establish the field for the evidence date — it does not establish absence of the control. The record is a dated evidence workproduct, checked against the reviewed source record before release. It does not score or rate the vendor. Use this tool when an agent needs the actual field-level evidence and its sources rather than a description of the framework.

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_openai_ledger"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 9, 2026

TDQS

A4.6/5.0

Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses key behavioral traits: the tool is free, public, dated to evidence date, includes quoted wording and source URLs, distinguishes Documented vs Question surfaced, clarifies that Question surfaced does not establish absence, is checked against source record, and does not score or rate the vendor. This is comprehensive disclosure of what the tool returns and its limitations.

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 a single paragraph with multiple sentences, but every sentence adds value: what it returns, the exact composition, the distinction between field types, the counter-intuitive meaning of 'Question surfaced', and the specific use case. It's slightly long but the detail is necessary given no annotations or output schema. It could be broken into two short paragraphs for readability, but it's densely informative.

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?

There is no output schema and no annotations, so the description must fully compensate. It covers the return content (fields, sources, questions), the counts, the evidence date, the non-scoring nature, and the usage context. For a zero-parameter tool, this is complete enough for an agent to select and invoke it correctly.

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 no parameters, so schema coverage is 100% by default. With 0 parameters, a baseline of 4 is given per guidelines. The description explains what the output contains (fields, sources, questions) which is more meaningful than a bare schema, though it doesn't describe parameter formats (none exist).

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 returns the free public OpenAI security evidence ledger, specifies the exact field composition (55 fixed fields, 47 Documented, 8 Question surfaced), and distinguishes it from siblings by emphasizing field-level evidence and sources rather than a description of the framework. The verb 'returns' plus resource 'OpenAI security evidence ledger' is specific and unambiguous.

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 explicitly says 'Use this tool when an agent needs the actual field-level evidence and its sources rather than a description of the framework,' which provides a clear usage context. It doesn't explicitly mention when not to use it or name alternatives, but the sibling names (get_free_dossier, get_methodology, get_register) and the contrast with 'framework description' imply alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All evidence-ledger tools Inspect

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