Words in Context

Words-in-context vocabulary practice questions with distractor explanations.

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Words-in-context vocabulary practice questions with distractor explanations.

    How to connect

    How to connect Words in Context to Claude

    The server address to paste into Claude settings:

    https://words-in-context.gumballtools.com/api/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect Words in Context to ChatGPT

    The server address to paste into ChatGPT settings:

    https://words-in-context.gumballtools.com/api/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect Words in Context to Cursor

    The server address to paste into Cursor settings:

    https://words-in-context.gumballtools.com/api/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (3)

    Raw names from tools/list. Only developers need these.

    draw_itemsDraw words-in-context vocabulary practice questions from a curated, human-written bank. Use this to quiz a learner, build a practice set, or check what a question of this type looks like. It is free, deterministic, and costs no inference. IMPORTANT: the response deliberately contains NO answer index and NO explanations. That is so you can present the questions without leaking the answers. Call `check_answer` with the item id and the chosen option to get the answer, why it fits, and why each distractor fails. Input: `count` (1-20, default 5) — an out-of-range count is REFUSED rather than clamped, so you learn the limit. `difficulty` is foundation|core|stretch. `theme` is science|humanities|social-science|literature. `seed` makes the draw reproducible: the same seed always returns the same items, so a practice session can be replayed or shared. Format note: these test inference from context, which is how the current digital SAT asks about vocabulary — not recall of definitions. Each item is a sentence with one word blanked and four options.
    check_answerCheck an answer to a practice item and get the teaching content. Returns whether the choice was correct, which option was right, why it fits the sentence specifically, why the chosen option was wrong, and the reason EVERY distractor fails. Read the distractor reasons out to the learner even when they answered correctly. Knowing why the tempting wrong answer was tempting is the part that transfers to the next question; being told "correct" teaches nothing. Input: `id` from a draw response, and `choice` as the zero-based index of the selected option.
    generate_itemsGenerate new practice items from a passage the learner supplies — their own reading, or the material they got wrong. This is the PAID tier and it costs real money per call, unlike the curated bank. Prefer `draw_items` unless the learner specifically needs questions from their own material. `model` is a priced choice: "economy" at $0.002 per item (Fast and cheap. Good enough for straightforward vocabulary in clear prose.); "standard" at $0.008 per item (Better at writing distractors that are genuinely tempting, which is the hard part of a good practice item.). Pick economy for straightforward prose and standard when the distractors need to be genuinely tempting, which is the hard part of a good question. Limits: passage 200 characters minimum, and `count` at most 5 per call. Generation is capped at 10 calls per caller per day, separately from the free quota. Generated items are NOT reviewed by a person. Every response carries a caveat saying so. Check the answer and the distractor reasons before giving them to a learner — a generated question with two defensible answers is worse than no question. Returns 503 when generation is not enabled on the deployment; fall back to `draw_items`.