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Cabrini Market Data

get_brief

Full research brief: price, fundamentals, insiders, splits for one ticker. $0.25 USDC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoAs-of date YYYY-MM-DD (default: today)
tickerYesStock ticker (e.g. NVDA)
lookback_daysNoDays of price history, 1-365 (default 30)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / lookback_days / description
      Previous value: -"Days of price history (default 30)"New value: +"Days of price history, 1-365 (default 30)"
    • addedInput schema / properties / lookback_days / maximum
      Added value: +365
    • addedInput schema / properties / lookback_days / minimum
      Added value: +1
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

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

The description discloses the $0.25 USDC cost, which is a meaningful behavioral trait, but with no annotations it carries the full burden of behavioral disclosure. It does not explain the output structure, whether the price data is a time series or snapshot, or how the four components are combined. Significant behavioral details remain hidden.

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 two short sentences, with the core content ('Full research brief') and key qualifiers (components and cost) stated up front. There is no fluff or repetition.

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?

As a paid composite tool with no output schema, the description should cover the response format and usage trade-offs compared to individual data tools. It only lists high-level components and cost, leaving the agent to guess what 'brief' contains structurally and when it is advantageous to use.

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 input schema provides 100% coverage of all three parameters (ticker, date, lookback_days) with descriptive text, so the baseline is 3. The description adds no extra meaning beyond naming the data components; it does not map those components to specific parameters or explain interactions.

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 that it returns a 'Full research brief' composed of price, fundamentals, insiders, and splits for one ticker. This distinguishes it from sibling tools like get_pricing or get_insiders, which each cover only one aspect. The missing explicit verb is compensated by the tool name 'get_brief'.

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?

No guidance is provided on when to use this composite brief versus calling individual sibling tools (e.g., get_fundamentals, get_insiders). The description does not mention prerequisites, alternatives, or exclusions. The only additional context is the cost and the single-ticker restriction.

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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