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Player Draft Outlook

player_outlook
Read-only

A single player's 2026 fantasy outlook (standard, half-PPR or full-PPR): projected points and per-game, floor/ceiling range, boom/bust odds, consensus ADP and draft round, our positional rank, and whether the model tags him a SLEEPER or a BUST vs the market, with a one-line thesis. Free, no key. Use for "is a sleeper", " fantasy outlook 2026", "should I draft ", " projection".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playerYesPlayer full name (e.g. "Bijan Robinson", "Jaxon Smith-Njigba").
scoringNoScoring format: standard, half_ppr (default), or ppr (full PPR). Overrides a platform preset. Works for Yahoo/ESPN/Sleeper defaults.
platformNoLeague platform — applies its default scoring, size, roster + best-ball settings (overridable by explicit scoring/teams). Underdog/DraftKings are best ball.

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, matching the description's read-only nature. The description adds value by explaining the output scope (single player, 2026 season) and that it's free. However, it doesn't mention rate limits, data freshness, or whether missing players return errors or empty results. With good annotations, a 3 is appropriate for minor additional context.

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 a single, dense paragraph that front-loads the core purpose and output, then provides use-case examples. It's well-structured but slightly verbose in listing all output items; could be tighter with bullet points or fewer examples. Still, every sentence earns its place.

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?

Given 3 parameters (all documented in schema), no output schema, and moderate complexity (enums for scoring/platform), the description covers the key behavioral aspects (what's returned, free access, query types). It doesn't describe pagination or error handling, but for a simple single-player lookup, this is adequate.

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 the schema already documents all 3 parameters (player, scoring, platform). The description adds value by mentioning the scoring options (standard, half-PPR, full-PPR) and noting that platform applies default settings overridable by explicit scoring. This enriches the schema defaults but doesn't add entirely new meaning, so baseline 3 fits.

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 starts with a specific verb ('A single player's 2026 fantasy outlook') and lists exactly what the output contains (projected points, floor/ceiling, ADP, rank, tags). It clearly distinguishes from siblings by focusing on a single player's outlook, unlike tools like compare_players or sleepers_and_busts which handle multiple players or broader analysis.

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

Usage Guidelines5/5

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

Includes explicit query examples like 'is <player> a sleeper', '<player> fantasy outlook 2026', 'should I draft <player>', and '<player> projection', making it obvious when to use. Also states 'Free, no key' to set expectations about no authentication. The sibling context (e.g., sleepers_and_busts, compare_players) implies alternatives, though not explicitly named, but the examples are sufficient.

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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TDQS

A3.9/5.0
Disambiguation3/5

Many tools have clearly distinct domains (fantasy vs NFL vs commodities vs general mispricings), but the 'edge' family is crowded: calculate_ev, scan_mispricings, edge_alerts, find_arbitrage, commodity_edge, nfl_edge, and nfl_prop_edge all surface pricing edges in overlapping ways. Fantasy tools like best_available and who_do_i_draft also have very similar mid-draft recommendation purposes, though their inputs differ.

Naming Consistency4/5

All tool names use lowercase snake_case and are readable, but they mix verb_noun patterns (calculate_ev, compare_players, scan_mispricings) with noun-phrase names (adp_market_gaps, edge_alerts, kelly_size, market_pulse). The style is consistent enough that an agent can predict the convention, with only minor deviations from a strict verb-first pattern.

Tool Count3/5

23 tools is on the heavy side for a single MCP server, though the scope is genuinely broad: prediction-market edge detection, position sizing, probability math, and fantasy football draft tools. It is not bloated enough to feel chaotic, but several tools could be consolidated or are tier-gated variants of the same underlying data.

Completeness4/5

The fantasy football surface covers the draft lifecycle well: rankings, player outlooks, comparisons, ADP gaps, and in-draft recommendations. The prediction-market side covers edge detection, EV, Kelly sizing, base-rate comparison, and arbitrage discovery, though it lacks direct market-price fetching or portfolio tracking—minor gaps that users can work around by supplying prices themselves.