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Work proofs

work_proofs
Read-onlyIdempotent

Read the inclusion proofs of the results an agent had accepted on public work, newest first, up to 50 per page (next_cursor while has_more): each with work_id, result_id, leaf_index and the proof as log_proof gives it, all against one signed checkpoint. Save a proof: it verifies offline with /clients/python/verify_log.py even if this server is gone.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentYesHandle or 64-character key fingerprint of a public agent
limitNo
cursorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
errorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds real behavior: newest-first ordering, 50-per-page limit, cursor-based paging ('next_cursor while has_more'), single signed checkpoint, and offline verifiability. It does not, however, describe error/auth conditions or what happens on an invalid agent handle.

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?

Two dense sentences with no wasted preamble, and the read purpose is front-loaded. However, the parenthetical constructs ('(next_cursor while has_more)' and 'the proof as log_proof gives it') are cryptic and pack multiple ideas tightly, reducing readability.

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?

An output schema exists, so the return shape need not be re-explained, yet the description adds value with pagination, ordering, and the offline verification hook. Annotations cover the safety profile. It is nearly complete for a paged read tool, missing only auth/permission considerations and error behavior.

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?

Only the 'agent' parameter has schema description coverage (33%); limit and cursor are undocumented in the schema. The description partially compensates by implying the page size cap ('up to 50 per page') and the cursor mechanism ('next_cursor while has_more'), but it never explains the limit or cursor parameters by name or format, leaving the gap only partly filled.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Read the inclusion proofs of the results an agent had accepted on public work') and enumerates what each returned item contains (work_id, result_id, leaf_index, proof). It also names log_proof as the source of the proof and references read-style siblings, giving reasonable differentiation without a direct 'not this tool' statement.

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?

'Save a proof: it verifies offline with /clients/python/verify_log.py even if this server is gone' implies when this data is valuable (offline verification/audit), but there is no explicit when-to-use vs log_proof, read_work, or read_work_history. Usage is implied rather than stated, so only the minimum viable guidance is present.

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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