Crawlora MCP

usage_platform_adjacency

Read-only

Returns, per scraping platform, how many real users have used it (lifetime, successful requests only), its request volume, top entry endpoints, and which other platforms those users also use. Aggregated across all customers with internal, static, and console traffic excluded; recomputed at most once per 24 hours and served from cache. Powers the console dashboard's "Explore more" recommendation. Use usage_overview to retrieve current principal's API usage overview.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "usage_platform_adjacency"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 5, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, yet the description adds materially: aggregation across all customers with internal/static/console traffic excluded, lifetime scope limited to successful requests, and a staleness bound (recomputed at most once per 24h, served from cache). These are real behavioral traits an agent needs for correct interpretation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but efficient: the payload is front-loaded in sentence one, freshness/scope caveats follow, and the routing note is last. The first sentence is long but every clause carries distinct information, so little could be trimmed without loss.

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

Completeness5/5

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

With an output schema present, the description need not explain the return shape, and it successfully focuses on aggregation scope, freshness, and purpose. Combined with a zero-parameter schema, nothing an agent needs in order to call and interpret this tool is missing.

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?

The tool takes zero parameters, so the baseline of 4 applies. There is nothing for the description to disambiguate, and it correctly describes the response dimensions instead of inventing parameter guidance.

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?

States a precise verb+resource with the full payload: per-platform user counts, request volume, top entry endpoints, and cross-platform co-usage. It is clearly distinguishable from siblings like usage_overview, usage_endpoints, and usage_timeseries, and even names usage_overview as a distinct tool.

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?

Gives a concrete use case ('Powers the console dashboard's Explore more recommendation') and routes the agent to usage_overview for a different need. It stops short of explicit when-not-to-use exclusions among the other usage_* siblings, but the context is clear.

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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