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Hive Zk Attestation

zk_anchor_to_base

Write an attestation commitment (32-byte hash) to Base via the Hive gateway. Anchors the attestation only; does not bridge value or move state to Aleo. Aleo snarkVM consumes the attestation independently via Leo programs (future hive-leo-circuits repo). Cost: $0.02 USDC + L1 gas. Backend RFC-stage; returns backend_pending until rails land.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_didYesDID of the attesting agent
proof_commitmentYesHex-encoded 32-byte commitment to the proof
verification_key_idNoIdentifier of the verification key referenced by the proof

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses key behavioral traits: it anchors only, does not move state, has a specific cost, and returns 'backend_pending' until rails land. This goes beyond the schema and gives the agent essential expectations about side effects and current reliability.

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 three sentences, front-loaded with the core purpose, then adds critical constraints, cost, and status. Every sentence provides value with no redundant content, making it appropriately concise for the tool's complexity.

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?

Despite having no output schema, the description covers return behavior ('backend_pending'), cost, scope limitations, and the relationship to Aleo snarkVM. This fully equips the agent to understand the tool's operational context and limitations, exceeding what is typical for a write operation with three parameters.

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%, so the baseline is 3. The description adds limited parameter meaning beyond the schema, such as reinforcing that proof_commitment is a 32-byte hash, but overall it does not significantly enhance the parameter understanding beyond what the JSON schema already provides.

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 clearly states the specific action: 'Write an attestation commitment (32-byte hash) to Base via the Hive gateway.' It also distinguishes this from related operations by explicitly noting what it does not do ('does not bridge value or move state to Aleo'), differentiating it from sibling tools like zk_verify_proof.

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 provides clear context on when to use the tool: for anchoring attestations only, with explicit exclusions (no bridging). It also adds practical usage constraints like cost and backend maturity ('RFC-stage returns backend_pending'). However, it does not explicitly name alternative tools or provide a direct 'use this when' statement, though the exclusions imply the boundary.

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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