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processed_cn_stock_finance

Finance Signal Bundle - get live computed signal: Processed analytics layer over financial markets: 30-period percentile rank, 4-period momentum, and a plain-language trend verdict. Turns ra Price 0.01 via x402 (USDC on Base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
walletNooptional 0x wallet for X-Wallet free tier (free credits every month: 100 anonymous or 5000 with a bound wallet)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

C2.6/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It does disclose the main computed outputs and appears to mention a 0.01 x402/USDC cost point, which is behaviorally relevant. However, the cost sentence is garbled and unreliable, and the description does not cover auth expectations, rate limits, or side effects, leaving transparency partial.

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

Conciseness2/5

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

The description is front-loaded with a clear label and a concise list of signal components, which is good. But the final sentence, 'Turns ra Price 0.01 via x402 (USDC on Base),' is unclear and reads like a corrupted or misplaced fragment, undermining the overall structure. It is not concise in a useful way because part of the content fails to communicate.

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

Completeness2/5

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

With no output schema, the description must adequately define what the agent will receive; it names three signal metrics but does not specify the underlying data universe, period, or interpretation. The optional wallet behavior is covered by the schema, but whether this endpoint is paid, requires setup, or returns a specific shape is left ambiguous. An agent cannot confidently select and interpret this tool without additional context.

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 only parameter, 'wallet', is already fully described in the input schema, including the free-tier distinction between 100 anonymous credits and 5000 with a bound wallet. With 100% schema coverage, the description is not required to add parameter detail. It adds nothing, so the baseline 3 is appropriate.

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

Purpose3/5

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

The description names a concrete deliverable—a live computed signal bundle over an analytics layer—and lists specific signal components (30-period percentile rank, 4-period momentum, plain-language trend verdict). However, it never explicitly connects the tool to the 'cn_stock_finance' scope implied by the name, and the trailing fragment 'Turns ra Price 0.01 via x402 (USDC on Base)' is incoherent. It is understandable as a generic finance-signal tool but not crisply differentiated from many processed_* siblings.

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?

The description gives no explicit guidance on when to use this tool instead of siblings like processed_stock_quote, processed_a_share_quote, or signal_stock_report. It only implies that the tool is useful when you want computed analytics signals, with no exclusions, prerequisites, or alternative routing. Given the large sibling set, this is a substantial gap.

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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