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Glama

Hive Zk Attestation

hive_earn_me

Look up the caller agent's registered earn profile, lifetime + pending USDC balance, last payout tx hash, and next-payout ETA. Real Base USDC, no mock data. Calls GET https://hivemorph.onrender.com/v1/earn/me?agent_did=. Returns "rails not yet live" gracefully if upstream is not yet deployed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_didYesAgent DID to look up. Required.

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries full disclosure burden. It specifies the HTTP GET endpoint, the real data nature ('Real Base USDC, no mock data'), and the graceful failure mode ('returns "rails not yet live"'). However, it doesn't explicitly state prerequisites like registration or behavior for non-registered/invalid DIDs, which would be useful.

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?

Three sentences, each earning its place: the first states purpose and returned data, the second confirms data authenticity, and the third provides endpoint and error handling. Front-loaded with the action and fully informative without waste.

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 only one parameter and no output schema, the description is complete for the tool's complexity. It names all returned fields, explains the endpoint, and covers the main failure case. No critical information is missing for an agent to correctly invoke and interpret the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only says 'Agent DID to look up. Required.' The description adds critical semantic context: the DID is the caller's own agent DID, and shows the URL template where it's inserted. This clarifies exactly what value to pass, going beyond the schema's generic description.

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 tool's purpose with a specific verb ('Look up') and resource ('caller agent's registered earn profile'), and enumerates the exact data returned. It distinguishes itself from sibling tools by emphasizing it's for the caller's own profile, not the leaderboard or registration.

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 implies when to use this tool: when you need the caller agent's own earn profile, as opposed to hive_earn_leaderboard or hive_earn_register. It doesn't explicitly state exclusions or name alternatives, but the 'caller agent's' phrasing and sibling names provide sufficient contextual 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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