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opensea_collection_chart

Retrieve an OpenSea collection's floor price or trading volume history over a requested window, with each data point in both settlement token and USD.

Instructions

Get an OpenSea collection's price or volume history. Returns a time series for a collection — either its floor price or its traded volume — over the requested window, with each sample in both the settlement token and USD. Use metric=floor_price for the floor line and metric=volume for the volume bars.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesOpenSea collection slug
metricNoSeries to return
timeframeNoWindow

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It usefully reveals that results are a time series with samples in both settlement token and USD, and clarifies the two metric modes. However, it does not mention timeframe value formats, response shape, pagination, or any API constraints.

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?

Two sentences deliver the core action, output type, currency detail, and metric options with no filler. The most important information is front-loaded in the first clause.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main purpose and output shape well, but it leaves the `timeframe` parameter underspecified and does not document the expected response structure. Since there is no output schema and no annotations, these are meaningful gaps for an agent planning to invoke the tool.

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?

Schema coverage is 100%, so the schema already documents every parameter. The description adds real value by defining what `metric=floor_price` and `metric=volume` return and clarifying that `timeframe` refers to the requested window, which goes beyond the terse schema descriptions.

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?

"Get an OpenSea collection's price or volume history" states a specific verb, resource, and result type. The phrase "returns a time series" clearly differentiates this chart endpoint from the many collection snapshots, activity, holder, and offer tools in the sibling list.

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 gives clear context for when the tool applies: when a collection's floor price or traded volume over time is needed. It also provides explicit metric-selection guidance with `metric=floor_price` and `metric=volume`, though it does not name alternative tools or state exclusions.

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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