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Prediction Markets Quant

by com.predictionmarketspicks

Calculate EV Edge

calculate_ev
Read-only

Calculate the expected-value edge on a Kalshi or Polymarket prediction-market contract. Given the current market price (in cents, i.e. the implied probability) and your own probability estimate, returns the % edge and a BUY / SELL / SKIP signal with a plain-English read. Use for "is this contract mispriced", "what is my edge", "should I take this position". From the PredictionMarketsPicks desk, which publishes a settled per-engine record — every signal graded against the market that priced it, wins and losses both: predictionmarketspicks.com/track-record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketPriceYesCurrent contract price in cents (1–99), equal to the implied probability in %. Accepts 55, "55%", "55¢", "$0.55", 0.55 or American odds (+120 / -150) — all read as 55%.
yourProbabilityYesYour own estimate of the true probability the contract resolves YES, in % (0–100). Accepts 55, "55%", "55¢", "$0.55", 0.55 or American odds (+120 / -150) — all read as 55%.

TDQS

A4.2/5.0
Behavior4/5

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

The description does not contradict annotations (readOnlyHint: true). It discloses output format, the source desk, and the existence of a public track record. For a read-only calculation, these details add context beyond the annotations. No missing behavioral traits are critical for this tool.

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 four sentences, front-loaded with the action and use cases. The last sentence about the track record is slightly promotional and could be removed. Overall, it is efficient and well-organized.

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?

For a two-parameter calculation tool with no output schema, the description covers purpose, inputs, outputs, and source. It explains what is returned and when to use it. No critical information is missing.

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%, with both parameters documented in detail (accepted formats, meaning). The description redundantly mentions 'Given the current market price... and your own probability estimate' but adds no new semantics. Baseline 3 is appropriate as the schema already does the heavy lifting.

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 calculates expected-value edge on prediction-market contracts, specifies inputs (market price and own probability estimate), and outputs (% edge, BUY/SELL/SKIP signal, plain-English read). It distinguishes from siblings by focusing on a single contract edge calculation, not conversion, alerts, or sizing.

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 explicitly lists use cases: 'is this contract mispriced', 'what is my edge', 'should I take this position'. It provides clear context for when to use the tool. However, it does not explicitly exclude scenarios or mention alternatives (e.g., use edge_alerts for multiple contracts), though the sibling list implies other tools have different purposes.

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.

Resources