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owp_compute_digest

Compute the canonical SHA-256 digest for OpenWorkProof payloads. Generates the hash required for Ed25519 signing and offline verification of work contracts.

Instructions

Compute the canonical SHA-256 digest of an OpenWorkProof payload.

The digest is computed over the RFC 8785 JCS canonicalisation of {"domain": "openworkproof/<object_type>/v0.1", "payload": <payload>} (with digest and signature fields stripped from the payload at the top level).

Args: object_type: One of the allowed canonical domains. payload: JSON string of the payload object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYes
object_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It excels by detailing the exact computation method: RFC 8785 JCS canonicalisation, the envelope structure, and the removal of digest and signature fields. It does not mention error handling or permission requirements, but the algorithmic behavior is clearly disclosed.

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 efficiently structured with a clear one-sentence summary, then necessary algorithmic detail, then concise parameter explanations. There is no fluff or redundant information. Each sentence earns its place.

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?

With an output schema present, describing return values is unnecessary. The description covers the algorithm, the exact input semantics, and the envelope structure. It does not mention that object_type valid values can be obtained from a sibling tool (owp_list_domains), which is a minor contextual gap, but overall it is complete for the intended cryptographic functionality.

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?

Given the input schema has 0% description coverage, the description compensates by providing an 'Args' section that gives meaning to both parameters. It states object_type must be an allowed canonical domain and payload is a JSON string of the payload object. It also explains which fields are stripped, adding semantic value. It could be improved by referencing how to get the allowed domains (e.g., owp_list_domains).

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 opens with a clear, specific verb+resource statement: 'Compute the canonical SHA-256 digest of an OpenWorkProof payload.' It further defines the exact canonicalization envelope and the stripping of digest and signature fields, which thoroughly distinguishes it from sibling tools like owp_get_schema_digest or owp_compute_key_id.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives. It implies use for computing the OWP payload digest, but gives no direct comparison or exclusion with siblings such as owp_sign_payload or owp_verify_signature. The specificity of the tool makes the usage implied rather than explicit.

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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