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opensea_collection_offers

Get an OpenSea collection's offer book as price levels, showing standing offers per price and total aggregate value to evaluate liquidity.

Instructions

List an OpenSea collection's offer book. Returns the collection-wide offer book as price levels: how many standing offers sit at each price per item, plus the aggregate offer count and total value across the whole collection. Offers are sorted by price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesOpenSea collection slug
limitNoPrice levels per page, 1-100
cursorNoOpaque pagination cursor from a previous next_page_cursor
sort_directionNoSort direction by offer price

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A3.9/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It does mention pagination (via cursor parameter) and price levels, but lacks details on whether the results are live or cached, what happens with zero offers, or any rate limits. As a read-only operation, no destructive behavior is implied, but the description does not explicitly state safety or latency expectations.

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 a single sentence, no filler, and front-loads the core function. Every phrase adds value: it defines what the offer book is, the output structure, the aggregate metrics, and the sort order. Highly efficient and well-structured.

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?

For a read-only listing tool with pagination, the description is comprehensive. It explains the return shape, sorting, and scope. It lacks some non-critical details like default limit or error cases, but those are not essential for correct invocation. The output schema is absent, so the description appropriately fills in expectations.

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 input schema already covers parameters (slug, limit, cursor, sort_direction) with descriptions, and coverage is 100%. The description adds meaning by explaining the result structure (price levels vs individual offers), which helps agents understand how parameters like limit and sort_direction affect output. It effectively complements the schema beyond what is visible.

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 it lists an OpenSea collection's offer book, with specific detail on the output (price levels with counts and totals) and sorting. This distinguishes it from related tools like opensea_collection_trait_offers and opensea_item_offers, which focus on traits or individual items.

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?

It clearly implies when to use it: when you need collection-wide offer book data. However, it does not explicitly mention alternatives like opensea_collection_trait_offers for trait-specific offers or opensea_item_offers for item-level offers, which would help an agent choose between them. The context is adequate but could be more explicit.

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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