Fever
Fever is a global live-entertainment discovery and ticketing platform that helps millions of people find events, activities and experiences in their city, and gives venues and partners the tools to sell and analyze tickets. For developers Fever exposes two public surfaces: an official Model Context Protocol (MCP) server over its real-time global event catalog (tools search_cities and search_events, OAuth 2.0 with PKCE), and a partner-facing Reporting API delivering in-depth event sales data (orders, tickets, financials, plan and session details) for CRM, BI, data-warehouse and ERP integration. Fever is backed by Accel and General Catalyst.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Fever the way a machine reads it — 10 machine-readable artifacts across 5 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Fever scores 39.3/100 (thin), with a separate agent-readiness read of 76/100 (agent native). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Fever
Each block below is one kind of artifact we hold for Fever. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 5
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Fever Authentication API
This endpoint is used to authenticate a user. It requires a username and password to be passed in the request body. If the user is authenticated successfully, a token is returne...
Fever FeverZone API
These endpoints provide an interface to extract the data available in FeverZone reports. The delay of the data is less than 15 minutes from reality. The route `/feverzone/sales-...
Fever Order Items API
These endpoints enable to access order-item data. ## Filtering Options The endpoint supports filtering by: | Parameter | Type | Description | |-----------|------|-------------| ...
Fever Plans API
The goal of the Plan endpoint is to provide all information about the plans/events/experiences/listings organised by a partner. The delay of the data is less than 10 minutes fro...
Fever Sessions API
The goal of the Session Endpoint is to provide all information about the session (or ticket types) of a plan. The delay of the data is less than 10 minutes from reality. ## Requ...
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
fever-mcp.yml
MCP SERVERRate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Fever Rate Limits
RATE LIMITSSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Fever — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 5
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Access & Security 2
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
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