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Tokenized Stocks & Agent Data (x402)

ai_progress_what_changed

Use this when the question is what moved in AI progress between two dates: the acceleration between two readings of 129 series (benchmarks, compute, AGI odds, model pricing, AI stocks and crypto, energy, space, robotics, research output). Per series: from/to value and date, absolute and % change, and whether a new observation landed. Dates are YYYY-MM-DD ids from the free menu at /. Costs $0.01 (USDC on Base or Solana, x402). HTTP twin: https://x402-accelerometer-feed.fly.dev/ai-progress/what-changed/{from}/{to}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYeslater reading id
fromYesearlier reading id

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the disclosure burden and does meaningful work: it reveals the $0.01 cost, the x402 payment rails, the HTTP twin, and the date-id source. It does not discuss read-only behavior or error cases, but the important operational traits are disclosed.

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?

The description is dense but every sentence serves a purpose: usage trigger, output contents, date id format, cost, and HTTP alternative. There is no filler and the main use case is front-loaded in the opening sentence.

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 simple two-parameter tool with no output schema, the description explains the return contents well enough and covers cost and id provenance. It lacks an explicit response example or error/ordering constraints, but an agent has enough context to select and invoke the tool correctly.

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 schema already describes from/to as earlier/later reading ids, and schema coverage is 100%, so the baseline is 3. The description adds the YYYY-MM-DD format and the fact that ids come from the free menu at '/', which meaningfully clarifies how to obtain valid parameter values.

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 clearly states what the tool does: it reports what moved between two dates across 129 AI-progress series, with values, changes, and new observations. It is distinguishable from sibling tools by the explicit between-two-dates framing, though it never names a sibling directly.

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 first sentence gives an explicit 'Use this when...' trigger, namely when the question is what moved in AI progress between two dates. It does not explicitly state when not to use it or name alternatives, so it stops short of a 5.

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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