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robcerda

MLB SportRadar MCP Server

by robcerda

get_draft_summary

Retrieve MLB draft summaries for specific years to analyze player selections and team strategies.

Instructions

Get MLB draft summary for a specific year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states a read operation ('Get') but doesn't describe response format, error conditions, rate limits, or authentication needs. For a tool with zero annotation coverage, this is a significant gap in transparency about how it behaves.

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 with zero waste—every word contributes to the purpose. It's appropriately sized for a simple tool and front-loaded with the key action and resource.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers the basic purpose but lacks details on behavior, parameters, and return values, which are critical for a tool with one required parameter and unknown output structure.

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 0%, so the description must compensate. It mentions 'for a specific year,' which aligns with the 'year' parameter in the schema, adding basic semantic context. However, it doesn't specify format (e.g., integer year like 2023), valid ranges, or examples, leaving gaps in parameter understanding.

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 resource ('MLB draft summary') with specific scope ('for a specific year'), making the purpose immediately understandable. It distinguishes from siblings like get_player_profile or get_standings by focusing on draft data, though it doesn't explicitly differentiate from non-existent draft-related siblings.

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 tool versus alternatives. The description doesn't mention prerequisites, constraints, or sibling tools that might overlap (e.g., if draft data is also available elsewhere). This leaves the agent without context for tool selection beyond the basic purpose.

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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