InsiderTrack MCP

model_desk

Read-onlyIdempotent

Review the AI model's morning brief and directional calls, including bullish/bearish ticker picks with horizons, confidence, and scoring against SPY. Optionally include past calls.

Instructions

The site's own AI model's morning brief and directional calls — and how they scored.

Each morning the model reads the day's disclosures and makes 3-5 calls (ticker, bullish/bearish, 30/60/90-day horizon, confidence, reasoning). Calls are scored at their horizon against SPY exactly like members' trades. Treat it as a scorecard of the model, not a forecast.

Args: history: Also return this many past calls, newest first, 0-100 (default 0).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`history`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 20, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Beyond the readOnly and idempotent annotations, the description adds useful behavioral detail: the model reads daily disclosures, makes 3–5 calls with specific fields, and scores them against SPY like member trades. This enriches the agent's understanding of the data and its provenance.

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 efficient and well-organized: a clear opening statement, a concise explanation of the model's process, and a focused Args section. No redundant or filler content.

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?

Given the simple parameter surface (one optional arg), rich annotations, and the presence of an output schema, the description sufficiently covers the tool's purpose, behavior, and parameter semantics. Nothing critical is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has zero description coverage, but the description fully documents the history parameter: range 0–100, default 0, and ordering (newest first). This is essential for correct invocation and completely compensates for the schema gap.

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 clearly identifies the tool as returning the site's AI model's morning brief and directional calls with scoring, and explicitly differentiates it from a forecast ('scorecard of the model, not a forecast'). This distinguishes it from sibling tools like top_signals or signal_outcomes.

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 description gives interpretive guidance ('treat it as a scorecard of the model, not a forecast') but does not explicitly state when to use this tool over siblings or provide exclusions. An agent must infer its comparative utility.

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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