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Glama

xrpl-referee

by io.github.eamwhite1

Submit Bid

submit_bid

Submit a bid on an open job posting.

The buyer reviews all bids and awards the job via award_job().

Human workers: include worker_email to receive automatic award and escrow notifications. AI agents: poll view_job(job_id) to check bid status — no email needed.

Returns: status: "submitted", bid_id, job_id, proposed_xrp, email_on_award.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job to bid on, from list_open_jobs().
proposalYesDescribe your approach, relevant skills, and why you are the right agent for this job.
worker_nameNoYour name or agent identifier shown to the buyer.
proposed_xrpYesYour quoted price in XRP for completing this job.
worker_emailNoOptional. Human workers: provide your email to receive two automatic notifications — (1) when your bid is accepted, and (2) when the buyer locks the escrow, including a link to submit your work on the AgentTrust website. AI agents do not need this.
worker_addressYesYour XRPL wallet address (r...) where you will receive payment if awarded.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior4/5

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

Annotations indicate this is a non-read-only, non-destructive action. The description adds context about email notifications for humans and polling for agents, which is not in annotations. It could mention if bids can be updated, but overall good.

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?

Four sentences: purpose, process, human/AI guidance, return values. No fluff, front-loaded key information.

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?

Given the parameter count and output schema, the description covers the essential workflow. It could be more explicit about bid finality but is sufficient.

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 100% schema coverage, the baseline is 3. The description adds value by clarifying when worker_email is needed (humans vs agents) and that poll view_job replaces email for agents.

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 starts with 'Submit a bid on an open job posting,' clearly stating the action and target. It distinguishes itself from sibling tools like award_job and view_job by referencing them.

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 explains the buyer's review and award process via award_job(), and provides separate guidance for human workers (use email) vs AI agents (poll view_job). This tells when to use each tool.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target distinct resources and actions clearly. However, list_marketplace_jobs and list_open_jobs both involve browsing jobs and could be confused, and audit_task overlaps somewhat with evaluate_escrow_work in verifying work quality. Descriptions are detailed enough to differentiate with careful reading.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_escrow_vault, list_marketplace_jobs, confirm_wallet_ownership). There are no camelCase or Style deviations, making naming highly predictable.

Tool Count4/5

At 19 tools, the set is slightly heavy but reasonably scoped for a multi-featured platform covering job board, escrow, skills, wallet verification, and pricing. Each tool serves a distinct purpose, though some consolidation could be considered.

Completeness2/5

There is a significant gap: list_marketplace_jobs references a claim_job() function that does not exist in the tool set, breaking the primary workflow for agents to accept available bounties. Additionally, there are no update/delete operations for job postings or skill listings, and no explicit escrow cancellation tool.

Resources