Skip to main content
Glama
DanielTomaro13

sportsdata-mcp

mlb_draft_prospects

Fetch MLB draft prospects for a given year, returning rank, player, school, and position. Optionally filter results by round or limit count.

Instructions

Draft prospects for a year (the pre-draft prospect board).

Returns: {prospects:[{id, rank, person, school, position}]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
limitNo
roundNo
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral transparency. It only mentions the return format but discloses no behavioral traits such as data freshness, rate limits, or any side effects. The agent gains no insight into the tool's behavior beyond its basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise at two sentences, with no redundant information. It front-loads the purpose and then provides return structure. However, it omits necessary parameter details, so while concise, it sacrifices some completeness.

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 lack of output schema and annotations, the description is incomplete. It only partially describes the return structure and provides no context on parameter behavior or edge cases. For a tool with three parameters, the description is insufficient for an agent to fully understand its usage.

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

Parameters2/5

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

Schema description coverage is 0%, yet the description does not explain the 'limit' or 'round' parameters. It only implicitly ties 'year' to the draft year. Without parameter descriptions in the schema, the description fails to compensate by adding meaning to the parameters.

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 states 'Draft prospects for a year (the pre-draft prospect board)', specifying exactly what the tool does and the scope. It also distinguishes itself from the sibling tool 'mlb_draft' by indicating it returns the prospect board rather than draft results. The return structure is provided, further clarifying the purpose.

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 given on when to use this tool vs. alternatives like 'mlb_draft'. There is no mention of context, prerequisites, or exclusions. The description simply states what it does without helping an agent decide between related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/DanielTomaro13/sportsdata-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server