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DanielTomaro13

sportsdata-mcp

mfl_pending_trades

Read-onlyIdempotent

Retrieve pending fantasy trades offered to you or by you in a league, with trade IDs, teams, assets involved, and expiration dates.

Instructions

Trades offered to you, and trades you have offered. Needs your cookie.

Returns: {pendingTrades:{pendingTrade:[{trade_id, offeringteam, willGiveUp, willReceive, expires}]}} — trade_id is what a response quotes. Draft picks appear as DP_/FP_ tokens and blind-bid dollars as BB_.

Example: What is on the table {"year": 2026, "L": "10005"}

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
LYesLeague id.
JSONNoLeave as-is.
TYPENoLeave as-is.pendingTrades
yearYesSeason year. Required — part of the URL path.
APIKEYNoRead-only alternative to the cookie.
FRANCHISE_IDNoCommissioners only: whose pending trades to fetch.
Install Server

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering the safety profile. The description adds useful behavioral context: the need for a cookie, the behavior that trade_id is quoted in responses, and the token representation for draft picks and blind-bid dollars. It doesn't contradict annotations and provides some depth beyond them, though not extensive.

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 compact and well-organized, with clear sections for purpose, return format, example, and auth. It front-loads the primary purpose and includes only relevant details. Every sentence contributes value, making it efficient without being truncated.

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 simple read-only tool with full schema coverage, the description provides a return structure, an example, and auth notes, which is sufficient for an agent to invoke it correctly. The only gap is the lack of usage guidance, but that's already penalized in usage_guidelines. It doesn't need an output schema since the return structure is spelled out.

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 all parameters are already documented. The description adds a concrete example with year and L, but does not provide additional semantic detail beyond what the schema gives. It mentions trade_id in the output, which is not parameter-related. Baseline of 3 is appropriate.

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 tool returns trades offered to you and trades you have offered, which unambiguously identifies the resource and action (retrieval). It differentiates from sibling MFL tools by focusing on pending trades, though it doesn't explicitly use a verb like 'list'.

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?

There is no guidance on when to use this tool versus alternatives, no mention of prerequisites beyond cookie/auth, and no exclusion criteria. The example shows a call but does not explain when an agent should choose this tool over other MFL tools like mfl_rosters or mfl_transactions.

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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