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x402 growth, decomposed (free)

get_network_decomposition

Breaks down x402 payment volume on Base into wash, self-dealing, clone-fleet, bundler, and organic activity, returning a locally verified SHA-256-sealed dataset before trusting headline figures.

Instructions

FREE, read-only. Integrity-sealed decomposition of x402 payment growth on Base from SmartFlow Observatory's independent ledger of USDC micro-transfers: how much of the headline volume is wash / self-dealing / clone-fleet activity vs bundler plumbing vs the organic clean core. Returns the full dataset plus seal_verified (this client recomputes the SHA-256 seal locally, recipe included in the response) and data_age_hours. Use this BEFORE trusting any headline x402 volume figure. Caveats: data refreshes roughly daily (check data_age_hours); figures reflect the observatory's ingest time, so the most recent ~1h of activity may be under-represented.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations supplied, the description carries the full burden and does so: it declares read-only and free access, explains the integrity mechanism (SHA-256 seal recomputed locally with the recipe in the response), and discloses two real limitations — roughly daily refresh and ~1h ingest lag. This is exactly the behavioral context an agent needs before quoting numbers.

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?

Front-loaded with access mode and subject, then the decomposition, then the return fields, then caveats — a logical order with no filler sentences. Some phrasing ("Integrity-sealed," "independent ledger") leans promotional, but each clause still conveys usable information.

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 tool with no output schema and no annotations, the description covers access mode, data provenance, what is returned, how to verify integrity, and the freshness caveats. Nothing an agent needs in order to call it or to interpret the result 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 there is no parameter meaning to add and the baseline applies. The description instead spends its space on return-value semantics (seal_verified, data_age_hours), which is a sensible use of the budget for a no-arg tool.

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 names a specific resource and a specific analytical output: a decomposition of x402 payment growth on Base split into wash/self-dealing/clone-fleet vs bundler plumbing vs organic core. That is far more precise than the title alone, and it is clearly distinguishable from the sibling audit/order-status tools.

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?

It gives an explicit trigger: "Use this BEFORE trusting any headline x402 volume figure," which tells the agent exactly when to reach for it. It stops short of naming alternatives or stating when NOT to use it, but the caveats section supplies the boundary conditions (stale data, recent-hour under-representation).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.