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mambaventures

NZXplorer MCP Server

get_risk_language

Scans over 64,000 NZX announcements for 8 risk categories, delivering scores, trends, and critical flags for going concern, covenant, impairment, litigation, and more.

Instructions

Get pre-computed risk language scores for an NZX company. Scans 64,000+ announcements for 8 risk categories: going_concern, covenant, impairment, litigation, restructuring, liquidity, regulatory, force_majeure. Returns total mentions, 12-month trend, category breakdown, critical flags, first-time detections, and yearly trend. Use when asked about risk factors, going concern warnings, covenant issues, litigation exposure, or regulatory risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesNZX ticker symbol (e.g. 'FBU', 'AIR', 'MEL')
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only describes what the tool returns, not its behavior (e.g., destructive actions, authentication needs, rate limits, or side effects). It implies a read-only operation but does not explicitly state it.

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 concise, consisting of three sentences that front-load the purpose, then detail the categories and return metrics, and end with usage guidance. Every sentence adds value with no redundancy.

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?

Without an output schema, the description adequately explains what is returned (total mentions, trend, category breakdown, critical flags, etc.). However, it lacks specifics on the output format or structure, which would be helpful for an agent. Given the single-parameter simplicity, it is fairly complete.

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?

Schema description coverage is 100% for the single parameter 'ticker', with a clear description of NZX ticker examples. The description does not add any additional meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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 it gets pre-computed risk language scores for NZX companies, enumerating 8 risk categories. It is specific about the resource (NZX company) and actions (scan announcements, return metrics). However, it does not explicitly differentiate from sibling tools like get_announcement_sentiment or check_insolvency_status, though the focus on risk language categories is distinct.

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 description explicitly says 'Use when asked about risk factors, going concern warnings, covenant issues, litigation exposure, or regulatory risk,' providing clear context for when to invoke this tool. It does not mention when not to use it or suggest alternatives, which would improve the score.

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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