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gsypolt

MFL MCP Server

by gsypolt

Get MFL future draft picks

mfl_get_future_draft_picks
Read-onlyIdempotent

Retrieve future rookie draft picks owned by each franchise, including year, round, and original team. Use for dynasty trade analysis.

Instructions

Get the future rookie draft picks each franchise owns (year, round, and whose original pick it is). Essential for dynasty trade analysis.

Args:

  • league, season: optional.

  • franchise (string, optional): "mine", "all" (default), a franchise ID, or a team name.

  • response_format: 'markdown' (default) or 'json'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leagueNoWhich league: a configured alias (see mfl_list_leagues), league name, or numeric MFL league ID. Omit for the default league.
seasonNoSeason year, e.g. 2025 for last season. Omit for the league's default (usually current) season.
franchiseNo"mine", "all" (default), a franchise ID, or a team name.
response_formatNo'markdown' for readable output (default) or 'json' for structured data.markdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare this a safe, idempotent, non-destructive read (readOnlyHint=true, idempotentHint=true), so the safety profile is covered. The description adds the returned data shape (year, round, original pick), which is useful, but says nothing about pagination, rate limits, or how the year/round filtering behaves.

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 opening sentence is front-loaded and earns its place. The Args block is somewhat redundant with the already-complete schema descriptions, but overall it stays short and readable with no wasted preamble.

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?

For a read-only tool with no output schema, the description compensates by naming the returned fields and the default franchise scope. It is nearly complete, missing only guidance on how it relates to the draft-results sibling.

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%, so every parameter is already documented in the schema. The description's Args block restates the same franchise and response_format options rather than adding new semantics, which lands at the baseline 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?

States a specific verb and resource ('Get the future rookie draft picks each franchise owns') and even enumerates the returned fields (year, round, original pick). It is clearly distinct from peers like mfl_get_roster or mfl_get_players, though it does not explicitly contrast itself with the closest sibling, mfl_get_draft_results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Essential for dynasty trade analysis' implies a use context, but there is no explicit when-to-use guidance, no exclusions, and no named alternative (e.g., mfl_get_draft_results for completed drafts). Usage must be inferred.

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