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usage_platform_adjacency

Reveal cross-user platform reach and co-usage to power 'Explore more' recommendations: see per-platform lifetime user counts, request volume, top entry endpoints, and co-used platforms.

Instructions

Get cross-user platform reach and co-usage. Returns, per 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 non-customer, static, and console traffic excluded; recomputed at most once per 24 hours and served from cache. Powers the console dashboard's "Explore more" recommendation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses aggregation scope (all customers), exclusion rules (non-customer, static, console traffic), the success-only counting semantics, the recompute cadence (at most once per 24 hours), and that results come from cache. An agent knows the data is non-real-time and pre-filtered.

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?

Three dense sentences with no filler; the returned fields are front-loaded before the aggregation and freshness caveats. Only minor improvement would be splitting the long second sentence.

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?

For a zero-parameter, no-output-schema read tool, the description is complete: it covers what is returned, the population and filtering, staleness, and the consumer context. Nothing needed to call it correctly 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 is 4; there is nothing for the description to document or compensate for beyond the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Get cross-user platform reach and co-usage') and details exactly what is returned per platform: lifetime user counts, request volume, top entry endpoints, and co-used platforms. This is clearly distinct in content from siblings like usage_timeseries or usage_recent_ips, though it never names an alternative to route against.

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?

Usage context is implied only by the closing note that it powers the console dashboard's 'Explore more' recommendation. There is no explicit when-to-use or when-not-to-use guidance relative to usage_overview, usage_timeseries, or usage_endpoints.

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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