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PerkOS-xyz

Nayori Agent MCP

Official
by PerkOS-xyz

nayori_provider_submit_committed

Submit provider work for an evaluable Nayori job with evidence and acceptance criteria, producing an on-chain commitment and returning a txid after verifying the criteria hash.

Instructions

WRITE (mainnet). Act as a PROVIDER: submit work for an evaluable job with an evidence commitment (escrow submit-work). Reads the job fresh, splits its on-chain description into plain text + criteria hash, recomputes the commitments with @perkos/agent-sdk from your acceptanceCriteria and evidence, and REFUSES unless the criteria hash equals the one committed on-chain, the signer is the assigned provider and status is 1 (funded). The deliverable is 36 bytes: 'ny1:' + the 32 raw bytes of the evidence hash. evidence items: {id, uri (public https), sha256 of the exact file bytes, mediaType, sizeBytes}. Keep the exact inputs for nayori_evaluation_request. Use this instead of nayori_submit_work when the job description ends with 'nayori-criteria-v1:'. Returns a txid; broadcast is not confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetYes
jobIdYes
evidenceYesEvidence files: public https URI, SHA-256 of the exact bytes, media type and size.
acceptanceCriteriaYesThe job's acceptance criteria. They are hashed into the on-chain commitment, so keep the exact text.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (write, non-read-only), the description discloses important behaviors: it reads the job fresh, refuses unless on-chain conditions hold, computes commitments via @perkos/agent-sdk, emits a specific 36-byte deliverable, and warns that 'broadcast is not confirmation.' This is rich behavioral context that the annotations alone do not convey.

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?

The description is dense but every sentence earns its place: protocol/network, role, purpose, behavioral checks, output format, parameter semantics, comparison to a sibling, and a caveat. It is front-loaded with 'WRITE (mainnet)' and the core action, and contains no filler.

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 write tool with four required parameters and no output schema, the description is complete: it explains refusal conditions, output shape, return value (txid), and the broadcast caveat. It also references the related evaluation tool, giving the agent enough context to 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?

With schema description coverage at 50%, the description compensates by explaining the evidence items format, the hashing role of acceptanceCriteria, and the instruction to keep exact inputs for nayori_evaluation_request. It adds semantic meaning beyond the schema, though asset and jobId still rely mostly on the schema definition.

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 action and resource: 'submit work for an evaluable job with an evidence commitment (escrow submit-work).' It also distinguishes itself from the sibling nayori_submit_work by naming the exact condition for choosing this tool, so an agent can identify its purpose unambiguously.

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?

It gives an explicit routing rule: 'Use this instead of nayori_submit_work when the job description ends with nayori-criteria-v1:.' It also states the prerequisites for a successful call (criteria hash match, assigned provider, status 1), which tells the agent when this tool is appropriate and when it will refuse.

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