xrpl_nft_offers
XRPL NFT buy/sell offers for a specific NFToken. Shows all active marketplace offers.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| nft_id | Yes | NFTokenID to check offers for |
XRPL NFT buy/sell offers for a specific NFToken. Shows all active marketplace offers.
| Name | Required | Description | Default |
|---|---|---|---|
| nft_id | Yes | NFTokenID to check offers for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It does state that it shows 'all active marketplace offers,' conveying scope and status filtering, but does not mention read-only nature, error handling, pagination, or output format. Adequate but with gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with core function, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter lookup with no output schema, the description covers what the tool does and its input. However, the absence of annotations and output schema leaves some ambiguity about return structure, though the simplicity keeps it near-complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes nft_id fully ('NFTokenID to check offers for'), and the description reinforces the purpose without adding syntax or example formats. Baseline 3 given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: showing XRPL NFT buy/sell offers for a specific NFToken. It distinguishes itself from siblings like xrpl_nft_intel by focusing on offers rather than broader NFT intelligence.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for a specific NFToken' implies the tool requires an NFT ID, but there is no explicit guidance on when to use this versus sibling tools such as xrpl_nft_intel or xrpl_dex_orderbook. Context is clear but alternatives are not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes, particularly the escrow check/monitor pair and the many RLUSD tools covering supply, integrity, holders, and compliance. Account-related tools like account_intel, gateway_balances, and token_check also share boundaries. An agent would need careful reading to choose correctly.
All tools share the xrpl_ prefix and use snake_case consistently. However, the second part mixes nouns and verbs (e.g., overview, iso20022, path_find, quantum_join), so the pattern is not strictly verb_noun. This is still readable and predictable.
31 tools is on the heavy side for an oracle server, especially with 9 RLUSD-specific tools that could be consolidated. The breadth of XRPL topics is large, but the count feels inflated beyond what an agent needs.
The tool set covers the major XRPL domains: accounts, DEX, AMM, NFTs, escrow, payments, compliance, and RLUSD. There are no glaring dead ends for an oracle use case. Some areas like transaction history are missing, but they fall outside the intelligence scope.