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honesty-mcp

honesty-mcp

by lkopietz3-byte
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Scaffold the agent-receipt-kit authorization pattern

scaffold_agent_receipts

Generate install steps and a copy-paste snippet for issuing WorkPackets before agent runs and verifying AgentClaims after, so you can trust AI agent reports; optional live example.

Instructions

agent-receipt-kit is a RUNTIME LIBRARY your own agent-orchestration code imports and calls at two specific moments (issue a WorkPacket before an agent runs, verify its AgentClaim after) -- it is not something this MCP server can 'check' on demand the way it checks a document's citations, because the packet only exists inside your application's own runtime. Call this tool to get the pattern explained, an install step, a copy-pasteable starter snippet for wiring issuePacket/verifyReceipt into your own agent loop, and (optionally) a live worked example run against the real kit -- one accepted claim, one rejected claim -- so you can see actual output before wiring it in. Use this when you're building or reviewing anything that lets an AI agent report back what it did (a coding agent, a browser-automation agent, a data-processing agent) and you don't yet trust that report by construction.

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": "scaffold_agent_receipts"
  }
}

Input Schema

NameRequiredDescriptionDefault
includeWorkedExampleNoAlso run a live accepted/rejected example against the real issuePacket/verifyReceipt, not just show a snippet.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations, so the description carries the full burden, and it does substantial work: it clarifies this is documentation/scaffold output rather than a live check, that the worked example runs against the real kit producing one accepted and one rejected claim, and that the snippet is copy-pasteable. It stops short of saying whether the worked example has side effects or resource cost, which is the one remaining behavioral gap.

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?

It is a long single paragraph, but it is front-loaded with the most important framing (runtime library, not a server-side check) before the deliverables. A few phrases restate ('so you can see actual output before wiring it in'), which keeps it just under a 5.

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?

For a zero-required-parameter scaffolding tool with no output schema, the description fully covers what the agent receives (explanation, install step, snippet, optional live example), when to invoke it, and how it differs from the verification siblings. Nothing needed to invoke it correctly is missing.

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% for the single boolean, and the schema already states it runs a live accepted/rejected example. The description's '(optionally) a live worked example ... one accepted claim, one rejected claim' largely restates that, so baseline 3 is appropriate.

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 states a specific deliverable (pattern explanation, install step, copy-pasteable starter snippet, optional worked example) and explicitly contrasts it with siblings: 'it is not something this MCP server can check on demand the way it checks a document's citations.' An agent can distinguish this scaffolding tool from check_grounding / corroborate_evidence without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use: 'Use this when you're building or reviewing anything that lets an AI agent report back what it did ... and you don't yet trust that report by construction,' with concrete example domains. It also implicitly excludes the runtime-check interpretation by explaining the packet only exists in the caller's own runtime.

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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