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fanduel_research_sports

Lists FanDuel Research sports and topics with slugs and team counts, and retrieves team IDs for a specified sport to filter research routes.

Instructions

FanDuel Research sports and topics. Lists the 23 sports and topics of the FanDuel Research site (NFL, NBA, MLB, NHL, college football and basketball, golf, tennis, soccer, MMA, horse racing, casino, FanDuel Predicts and more) with the slug the other research routes take, the position codes, the team count and the player-news types each has. Pass sport to also get that sport's teams with the number_fire_id values the team filters take.

Input Schema

NameRequiredDescriptionDefault
sportNoSport or topic slug; adds the sport's teams

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.16

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it states that the tool lists sports/topics with specific metadata, and that passing `sport` adds that sport's teams along with `number_fire_id` values. It does not mention read-only status, rate limits, or error behavior, but as a listing tool the disclosed behavior is substantial.

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?

Two dense but well-structured sentences with zero wasted text. The primary behavior is front-loaded, and the optional parameter's effect follows immediately.

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 compensates fully by enumerating the returned fields (slug, position codes, team count, player-news types) and explaining how the optional `sport` argument augments the response. Nothing substantive is missing for correct invocation.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining that `sport` is a 'sport or topic slug' and that passing it 'also' returns the sport's teams with the `number_fire_id` values used by team filters.

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 uses a specific verb ('Lists') and precisely identifies the resource: the 23 sports and topics of FanDuel Research. It also distinguishes this index tool from the downstream research routes by noting that it returns 'the slug the other research routes take.'

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?

It clearly implies this is the discovery tool for slugs and team IDs used by other research routes, and it explains the optional sport parameter's additive behavior. However, it does not explicitly say when to call this vs. a sibling like fanduel_research_sub_categories or when not to use it.

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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