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DanielTomaro13

sportsdata-mcp

mfl_transactions

Read-onlyIdempotent

Retrieve completed MFL league transactions (adds, drops, trades, waivers) and filter by team, week, days, or type to isolate specific moves.

Instructions

Completed league transactions — adds, drops, trades, waivers. Filter it: unfiltered is very large.

Returns: {transactions:{transaction:[{type, timestamp, franchise, transaction}]}} — the transaction field is a packed, comma/pipe-delimited string whose meaning depends on type.

Example: This franchise's last week of moves {"year": 2026, "L": "10005", "FRANCHISE": "0001", "DAYS": 7}

Auth: works without a key; MFL_COOKIE unlocks more if set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
LYesLeague id.
WNoOnly this week.
DAYSNoOnly the last N days.
JSONNoLeave as-is.
TYPENoLeave as-is.transactions
yearYesSeason year. Required — part of the URL path.
COUNTNoCap the number returned.
APIKEYNoRead-only alternative to the cookie.
FRANCHISENoJust this franchise, e.g. '0001'.
TRANS_TYPENoOne of WAIVER, BBID_WAIVER, FREE_AGENT, TRADE, IR, TAXI, AUCTION_INIT, AUCTION_BID, AUCTION_WON, SURVIVOR_PICK, POOL_PICK. Omit for all.
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description adds the precise return structure, explains the tricky packed `transaction` field, covers auth requirements (works without a key, cookie unlocks more), and provides a concrete example. No contradiction with annotations.

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 front-loaded with purpose and the filter warning, then gives return format and example. It's slightly dense with the packed-field explanation but each sentence adds necessary context without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description provides the return structure and an example. It covers filtering, auth, and the meaning of the tricky field. For a 10-param tool, this is impressively complete.

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

Parameters5/5

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

Schema already covers all 10 params at 100%, but the description adds extra value: explains that JSON and TYPE should be left as-is, demonstrates real usage with an example, and clarifies the packed transaction field's dependency on type. This goes well beyond the schema.

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 the tool retrieves completed league transactions (adds, drops, trades, waivers), which is a specific verb+resource. It distinguishes itself from other MFL tools like mfl_rosters or mfl_free_agents by naming the transaction types it covers.

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 warns that unfiltered results are very large, implying the need for filters, and gives an example with FRANCHISE and DAYS. It doesn't explicitly name alternative tools, but the context makes it clear this is for transactions, not rosters or other MFL data.

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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