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Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

opensea_collection_social_proof

Fetch an OpenSea collection's social following to see follower and watcher counts plus highlighted notable collectors, adding demand signals to trading stats.

Instructions

Get an OpenSea collection's social following. Returns how many accounts follow or watch a collection, plus the notable collectors OpenSea highlights for it. Useful as a demand signal alongside the trading stats.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "opensea_collection_social_proof"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesOpenSea collection slug

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses what data is returned (follower counts, notable collectors) and frames it as a demand signal, which is helpful. However, it doesn't mention whether the data is live or cached, whether the slug must be URL-encoded, or what happens for invalid/unknown slugs. It's a read-only lookup by nature, but that's inferred rather than stated.

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: what it does, what it returns, and when to use it. Front-loaded with the action and resource, no fluff.

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 single-parameter read-only lookup with no output schema, the description is nearly complete. It explains the return content and use case. The only minor gap is not specifying the exact shape of the response (e.g., field names), but with no output schema and a simple resource, this is acceptable.

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

Parameters3/5

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

Schema description coverage is 100%: the only parameter 'slug' is described as 'OpenSea collection slug'. The description adds the context that the slug identifies the collection for social data, but doesn't add format details (e.g., slug format, how to find it). Baseline 3 is appropriate since the schema already covers the parameter.

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 states a specific verb ('Get'), a clear resource ('an OpenSea collection's social following'), and specifies the exact data returned (follower/watch counts and notable collectors). It also distinguishes itself from the broader opensea_collection tool by focusing on social proof, and the sibling list shows many opensea_* tools, so this one is clearly scoped.

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 a clear use case: 'Useful as a demand signal alongside the trading stats.' This implies when to use it (when assessing demand/social interest) and implicitly contrasts with trading-stat tools. It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.

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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