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Glama

ParlayAPI

by io.github.JacobiusMakes

parlayapi_signup

Create a free-tier ParlayAPI account for the given email.

Returns the API key, a magic-login URL the user can click to access
their dashboard, and a Stripe upgrade URL. Idempotent: if the email
already has an account, returns exists=true with the login URL only
(the existing API key is intentionally NOT exposed for security).

Use this when a user wants to start building with ParlayAPI and
does not have a key yet. After signup, ask the user to add the
returned api_key to their MCP server config under PARLAYAPI_KEY,
then restart the MCP client to pick it up.

Args:
    email: User's email address.
    intended_use: Optional one-line description of what they're
        building. Used for analytics.
    agent_id: Identifier for the agent making the call. Defaults
        to "mcp-client".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYes
agent_idNomcp-client
intended_useNo

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

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden, and it handles this well. It discloses idempotency, the security decision not to expose existing API keys, the exact return payload, and the post-signup configuration step the agent should perform.

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?

The description is appropriately detailed without being bloated. It front-loads the core purpose and outputs, then covers edge-case behavior, usage guidance, and parameter details; every sentence adds practical value.

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?

The description is complete for an agent to call the tool correctly: it explains when to call it, what it returns, how idempotency behaves, and what to do with the result. Since an output schema exists and the description also summarizes the return values, nothing critical is missing.

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?

Schema coverage is 0%, but the description fully compensates by explaining all three parameters: email is the user's email, intended_use is an optional analytics line, and agent_id identifies the calling agent with a default. This adds real semantic value beyond the bare schema.

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 states a specific action ('Create a free-tier ParlayAPI account'), names the resource, and describes the outputs (API key, magic-login URL, Stripe upgrade URL). It also distinguishes itself from account lookup/retrieval tools by focusing on new account creation.

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 says to use it 'when a user wants to start building with ParlayAPI and does not have a key yet,' which is a clear selection criterion. It also covers the idempotent existing-account case, but it does not explicitly compare against sibling tools like parlayapi_magic_link or parlayapi_account_info, leaving some alternative-routing to inference.

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

A3.7/5.0
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