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luno

Luno MCP Server

Official
by luno

get_order_book

Destructive

Retrieve real-time order book data for cryptocurrency trading pairs to analyze market depth and liquidity on the Luno exchange.

Instructions

Get order book for a trading pair

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pairYesTrading pair (e.g., XBTZAR)
Behavior3/5

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

Annotations indicate this is a destructive, non-idempotent, non-read-only operation with open-world data, but the description doesn't add any behavioral context beyond the basic purpose. It doesn't explain what 'destructive' means in this context (e.g., if it consumes resources or has side effects), nor does it mention rate limits or authentication needs. Since annotations provide some safety profile, the description adds minimal value, scoring a baseline 3.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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?

Given the complexity of a trading tool with destructive annotations and no output schema, the description is incomplete. It doesn't explain what an 'order book' entails (e.g., bid/ask levels, depth), potential side effects from the destructive hint, or return format, leaving significant gaps for the agent to operate effectively.

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 has 100% description coverage, with the 'pair' parameter clearly documented as 'Trading pair (e.g., XBTZAR)'. The description doesn't add any meaning beyond this, such as format constraints or examples not in the schema. With high schema coverage, the baseline score is 3.

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 the verb 'Get' and the resource 'order book for a trading pair', making the purpose specific and understandable. However, it doesn't distinguish this tool from potential siblings like 'get_ticker' or 'get_candles' that might provide different market data, which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives like 'get_ticker' or 'get_candles' from the sibling list. It lacks any context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.

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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