Agentic Finance Graph MCP Server

Machine money: headline figures

market_state

Read-onlyIdempotent

Query headline, definition-backed metrics on AI-agent money across Base and other chains, covering payments, spending averages, income, x402 inflow, registrations, detections, and address-poisoning watch.

Instructions

The ledger's headline figures for AI-agent money, each with its value, unit, measurement time and definition id. On Base: money agents paid to others after registration (counted), daily spending by the day it was paid (last day, 7-day and 30-day averages), the part that went into positions they still hold, what left their control, the routing hops and pre-registration history excluded, how many agents are ranked (L7+ paid someone, L8 funded, L9 repeat counterparties), agent income, x402 seller inflow, treasuries, concentration, detections and the address-poisoning watch. On other chains: ERC-8004 registrations on seven chains, what registration owners paid on BNB Smart Chain, Ethereum, Arbitrum, OP Mainnet and Polygon, and Solana agent registrations and payments.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`compact`	No	Leave out the reading notes and the previous-sample block (figures, data quality and licence stay)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	Yes
`chain`	No
`since`	No
`source`	No
`figures`	Yes
`reading`	No
`data_quality`	No
`days_still_scanning`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 16, 2026

TDQS

B3.4/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered. The description adds genuine behavioral context beyond that: base-metric exclusions ('routing hops and pre-registration history excluded', the 'counted' qualifier), the L7+/L8/L9 ranking criteria, and the split between Base and other-chain coverage. It does not mention auth or rate limits, but for a read-only snapshot the added measurement semantics are substantial.

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

Conciseness3/5

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

The first sentence front-loads the purpose and the per-figure shape well, but the remainder is a long enumeration of metric names that reads like raw inventory rather than decision-relevant information, and it spills into a second paragraph on chain coverage with no structural separation.

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?

An output schema exists, so return values need not be explained, and annotations cover the safety profile. The description is complete on scope and content; the main gap is guidance on when to reach for this versus the time-series or delta siblings.

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?

The single `compact` parameter is fully documented in the schema (100% coverage) and the description says nothing about it, so no value is added on this axis. Per the high-coverage rule, baseline 3 is appropriate.

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?

The description states a clear resource (the ledger's headline figures for AI-agent money) and enumerates the exact content, including the per-figure shape (value, unit, measurement time, definition id). It is specific but never contrasts itself with adjacent tools such as metric_history or since_last, so sibling differentiation is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not guidance. With many siblings covering overlapping metric territory (metric_history, since_last, list_metrics, ladder_dynamics), the agent must guess that this is the whole-board snapshot rather than the historical or delta view; nothing in the text routes it.

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