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Unitrader

What Unitrader's AI did today

ai_activity_today
Read-onlyIdempotent

How many market checks Unitrader's AI made across all accounts in the last 24 hours, how often it chose to wait, how many trade ideas its risk rules held back, and how many trades it placed. Platform-wide counts only: no markets, accounts or people.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral facts: a fixed 24-hour rolling window, aggregation across all accounts, and explicitly that no dimension breakdown exists — the main way an agent could misuse this tool. It stops short of describing the shape of the returned counts.

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?

A single dense sentence that front-loads the four metrics and ends with the scope restriction, which is the most important caveat. Every clause maps to a distinct returned value; there is no filler or restatement of the title.

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?

With no parameters and rich annotations, the description carries almost everything an agent needs. The remaining gap is the return shape: there is no output schema, and the description does not say whether the result is four named counter fields or something else, so an agent cannot predict how to read the response.

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. The description correctly implies there is nothing to configure by stressing that the window and scope are fixed, though it does not explicitly say 'takes no arguments'.

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 the exact resource (Unitrader's AI activity over the last 24 hours) and enumerates the four counts returned: market checks, wait decisions, risk-blocked ideas, and placed trades. That is far more specific than the title, and it is clearly distinct from the informational siblings (about_unitrader, get_started, trading_lesson).

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?

The clause 'Platform-wide counts only: no markets, accounts or people' implicitly tells the agent this tool cannot be used for per-account or per-market questions, which is useful exclusion guidance. However, it never names an alternative tool or states a positive use case ('use when the user asks how active the AI has been'), so the when-to-use routing is left to inference.

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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