Skip to main content
Glama

ParlayAPI

by io.github.JacobiusMakes

parlayapi_find_ev

Find positive-EV bets vs a sharp book's no-vig fair line.

Compares every soft book's price against the no-vig fair probability
derived from a sharp book (default Pinnacle); rows where a soft book
offers better odds than the sharp fair price are +EV. Three-way soccer
markets use a dedicated no-vig pass over home/draw/away, so +EV on the
draw surfaces too.

Args:
    sport_key: e.g. "baseball_mlb", "soccer_epl".
    sharp_book: Sharp baseline book. Default "pinnacle".
    min_edge: Minimum edge % to include (e.g. 3 for 3%). Default 2.
    markets: Comma-separated market_keys (optional).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketsNo
min_edgeNo
sport_keyYes
sharp_bookNopinnacle

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it usefully explains the no-vig derivation, default Pinnacle baseline, and the special three-way soccer handling. It does not address read-only status, rate limits, data freshness, or prerequisites, though these are less critical for a search-like 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 front-loaded with the core purpose and follows with a clearly formatted Args section; the extra sentence on three-way soccer earns its place. Slight redundancy exists between the opening summary and the second sentence's restatement of the comparison.

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 4-parameter lookup tool with an output schema, it provides the algorithm, defaults, accepted value examples, and a market-specific edge case. It omits only minor operational details such as where sport_key values come from and how soft books are selected, which are inferable from sibling tools.

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?

Even though schema coverage is 0%, the Args section explains all four parameters with meanings, defaults, and examples: sport_key shows concrete formats, sharp_book names the default, min_edge gives a 3% example, and markets clarifies formatting. This fully compensates for the empty schema descriptions.

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?

Opens with a specific verb and object: 'Find positive-EV bets vs a sharp book's no-vig fair line.' The second sentence elaborates the exact comparison (soft book odds vs sharp no-vig fair probability), which clearly sets it apart from siblings like find_arbitrage or find_middles.

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 makes the intended use explicit: detect +EV prices against a sharp book's no-vig line, with a soccer three-way nuance. It does not explicitly name sibling alternatives or say when not to use it, but the search/analysis context is clear enough to separate it from arbitrage and middles.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ParlayAPI toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Most tools map to distinct workflows (raw odds, best-line, EV scan, arb, middle, single-bet grade, parlay grade), but several pairs are easy to mix up: find_ev vs best_bets both surface +EV opportunities, live_sports vs list_sports differ only in 'live', and verdict vs parlay_verdict have near-identical names. The detailed descriptions resolve most ambiguity, so it is not chaotic, but the boundaries are not all crisp.

Naming Consistency3/5

All names share the parlayapi_ prefix and snake_case, but the suffix style is inconsistent: some are verb-led (get_odds, find_arbitrage, set_bettable_books) and many are bare noun phrases (consensus, verdict, source_quality, magic_link). The live_* and best_* groups are internally consistent, but pairs like list_sports/live_sports and verdict/parlay_verdict add confusion.

Tool Count3/5

22 tools is on the heavy side for an MCP server, even though the sports-betting domain is broad. Each tool has a plausible purpose, but the public demo/metadata tools (live_command_center, book_coverage, source_quality, live_sports) could probably be consolidated or separated.

Completeness4/5

The surface covers the core domain well: sport discovery, game odds, props, consensus, best-line, EV, arbitrage, middles, single-bet verdicts, parlay verdicts, and account/signup flows. Minor gaps exist (no explicit book/market metadata list, no historical odds, no betting-account history), but agents can usually work around them.

Resources