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Submit the signed credit offer (step 2 of 2)

submit_credit_offer

Complete the offer: pass workId, the BODY_JSON from prepare_credit_offer UNCHANGED, and the signature your wallet produced over MESSAGE_TO_SIGN. Relays to the same public door any HTTP client uses (POST /labor/work/:workId/back/credit); this server holds no keys, no funds and no credits. Free with a valid signature. Returns the recorded offer, including the regen1 account the board derived for you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workIdYesThe packet id.
bodyJsonYesThe exact BODY_JSON string from prepare_credit_offer, unchanged.
signatureYesThe 0x… personal_sign output over MESSAGE_TO_SIGN.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
toolNo
fundedNo
payUsdNo
workIdNo
claimIdNo
claimableNo
next_callNo
proofRuleNo
fulfillableByNo
definitionOfDoneNo
needs_from_callerNo
notClaimableReasonNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

The description discloses meaningful behavior beyond annotations: it relays the request to a public HTTP endpoint, states the server holds no keys/funds/credits, notes it is free with a valid signature, and says it returns the recorded offer with the derived regen1 account. No contradiction with the annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise but includes somewhat vague filler such as 'Relays to the same public door any HTTP client uses' and repeated assurances about holding no keys/funds/credits. These do not strongly help an agent decide to call the tool and could be simplified.

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?

Given the tool's complexity and the existence of an output schema, the description provides enough context for invocation: required inputs, their sources, the endpoint, cost, and return information. It could have explicitly stated 'call prepare_credit_offer first,' but the step-2 framing makes that clear.

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?

Schema coverage is 100%, and the descriptions add important constraints: bodyJson must be the exact unchanged string from prepare_credit_offer, and signature must be the 0x personal_sign output over MESSAGE_TO_SIGN. workId is less detailed ('packet id') but sufficient in context.

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 title and description clearly state the action: submitting the signed credit offer, explicitly labeled as step 2 of 2. It identifies the resource (credit offer) and the verb (submit/complete), and is distinct from sibling tools like prepare_credit_offer and submit_credit_settlement.

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 clearly ties usage to prepare_credit_offer by requiring its BODY_JSON and MESSAGE_TO_SIGN, and the title marks it as step 2. It does not explicitly list when not to use it, but the sequencing and required inputs provide strong practical guidance.

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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