Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@kingsplaybook-mcp Show me tonight's NBA confirmed lineups"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The official MCP server for the KingsPlaybook Developer API. It gives an AI agent — one building a sports-betting bot, a model, or an analytics tool — direct access to KingsPlaybook's data as native tools:
Confirmed lineups — NBA, MLB, NHL
Player projections — the projection engine's raw output
Canonical game lines — moneyline / spread / total, aggregated across books
Pick-history archive — the publicly-posted record, with results + closing lines
It's a thin local (stdio) wrapper over the /v1 REST API.
Same data, second protocol.
Sign up at kingsplaybook.org/devs. The
Free tier covers lineups; paid tiers add projections, lines, and history.
Your key looks like kp_live_….
Related MCP server: AltSportsData MCP Server
No install step — run it straight from npm with npx. Add it to your MCP
client config (Claude Desktop, Cursor, etc.):
{
"mcpServers": {
"kingsplaybook": {
"command": "npx",
"args": ["-y", "kingsplaybook-mcp"],
"env": {
"KINGSPLAYBOOK_API_KEY": "kp_live_your_key_here"
}
}
}
}That's it — the agent now has the KingsPlaybook tools.
Tool | What it returns | Min. plan |
| Confirmed lineups for a league + date | Free |
| Raw player projections for a league + date | Starter |
| Canonical game lines for a league + date | Pro |
| Posted picks + results for a date | Premium |
| Per-domain data-age signal | Free |
Every tool returns JSON. Calling a tool above your plan tier returns a
clear 403 message with an upgrade link.
"What's tonight's NBA slate look like?" → kingsplaybook_get_lineups({ league: "nba", date: "2026-05-21" })
"Pull MLB projections for today" → kingsplaybook_get_projections({ league: "mlb", date: "2026-05-21" })
"How did KingsPlaybook's prop picks do yesterday?" → kingsplaybook_get_pick_history({ date: "2026-05-20", type: "prop" })
Env var | Required | Default | Purpose |
| yes | — | Your |
| no |
| Override the API base URL |
npm install
npm run build # compile TypeScript to dist/
npm start # run the built server (needs KINGSPLAYBOOK_API_KEY)
npm run dev # watch modeMIT
Per-domain data-age signal — when the projection pipeline, lineups, and lines were each last updated. Check this to decide whether the data is fresh enough before acting on it. Works on any plan.
Args: none. Returns JSON: { data: { snapshot: { latest_farmer_harvest_at, latest_crown_scored_at }, lineups: { latest_updated_at }, lines: { latest_updated_at } }, meta: { as_of } }.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by detailing the exact return structure (per-domain timestamps), clarifying a zero-argument call, and stating 'Works on any plan.' No contradictions exist, and the added context is useful beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: one clear summary sentence, one usage instruction, and a structured 'Args/Returns' section. Every sentence earns its place. The return JSON block is detailed but organized, providing necessary transparency without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description fully compensates by specifying the exact JSON shape returned, including nested fields and the meta.as_of timestamp. It covers purpose, usage, scope ('Works on any plan'), and output. No critical information is missing for an agent to invoke and interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the description explicitly confirms 'Args: none.' Since there are no parameters to document, the baseline of 4 applies, and the description provides full clarity by stating there are no arguments.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a per-domain data-age signal, listing exactly which domains are covered (projection pipeline, lineups, lines). This distinguishes it from sibling tools that return actual data, not freshness metadata. The noun-phrase style is immediately understandable in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs when to use this tool: 'Check this to decide whether the data is fresh enough before acting on it.' It also says 'Works on any plan,' adding context. However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
KingsPlaybook's canonical game-market lines — moneyline, spread, and total — aggregated across tracked sportsbooks, per game on a league + date. Requires a Pro plan or higher.
Args: league ('nba' | 'mlb' | 'nhl'), date ('YYYY-MM-DD'). Returns JSON: { data: [ { event_id, home_team, away_team, markets: [ { market_type, selections: [ { selection, line, prices: [ { book, price, decimal } ] } ] } ] } ], meta }.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Game date, ISO-8601 (YYYY-MM-DD) | |
| league | Yes | League: nba, mlb, or nhl |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safe read-only nature is known. The description adds valuable context: the Pro plan requirement, aggregation by sportsbook, and a detailed return JSON structure, which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description opens with a clear, front-loaded summary sentence, then uses structured Args and Returns sections. Every sentence serves a purpose and there is no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple 2-parameter schema, rich annotations, and an inline return structure that acts as a de facto output schema, the description is nearly complete. Minor gaps like handling of empty data or the contents of 'meta' do not significantly detract.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; both league and date are fully described in the input schema. The description's Args line merely restates the enum and date format, adding no significant new meaning. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb+resource: retrieves canonical game-market lines (moneyline, spread, total) per game by league and date. It distinguishes this from sibling tools like get_lineups and get_projections by naming the exact data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: access requires a Pro plan, and it returns lines aggregated across sportsbooks per league and date. It does not explicitly name alternative tools, but the scope strongly implies when it should be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Confirmed / projected starting lineups for every game on a given league + date, split into home and away rosters. Available on every KingsPlaybook plan, including Free.
Args: league ('nba' | 'mlb' | 'nhl'), date ('YYYY-MM-DD'). Returns JSON: { data: [ { event_id, league, game_date, home_team, away_team, lineups: { home: [players], away: [players] }, updated_at } ], meta: { count, league, date, as_of } }.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Game date, ISO-8601 (YYYY-MM-DD) | |
| league | Yes | League: nba, mlb, or nhl |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation safe (readOnly, non-destructive, idempotent). The description adds useful behavioral context: lineups are 'confirmed / projected' (potentially not final), it returns data for 'every game,' and includes a detailed return JSON structure. This goes beyond the structured annotations, although it doesn't discuss rate limits or empty-game scenarios.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: it leads with the core purpose, then the plan availability, then args and return format. Every sentence serves a purpose, with no fluff. The return JSON is compact and illustrative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given only two simple parameters and no output schema, the description compensates by specifying the return shape in detail, including nested lineups and meta fields. It covers the necessary input and output. Minor gaps: no stated behavior for dates with no games, but this is not critical for such a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with descriptions for both date and league. The description's 'Args' line effectively restates the schema (league enum and date format) without adding new meaning. It does tie the parameters to the tool's purpose ('for every game on a given league + date'), but this is minor. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Confirmed / projected starting lineups for every game on a given league + date, split into home and away rosters.' It uses a specific verb (get) and resource (lineups), and the scope (league + date, home/away) distinguishes it from siblings like projections or lines.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for starting lineups on a specific league and date. It doesn't explicitly mention alternatives or when-not-to-use, but the scope is unambiguous and the sibling tool names further clarify distinctions. The plan availability note also adds context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
KingsPlaybook's publicly-posted picks for a date — game-market and player-prop — each with its result and closing-line context. Use this to backtest against the actual posted record. Requires a Premium plan.
Args: date ('YYYY-MM-DD'); optional type ('game' | 'prop', omit for both); optional sport (e.g. 'nba'). Returns JSON: { data: { game_picks: [...], prop_picks: [...] }, meta: { count, game_pick_count, prop_pick_count, date, ... } }.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Game date, ISO-8601 (YYYY-MM-DD) | |
| type | No | Filter to game-market or player-prop picks; omit for both | |
| sport | No | Optional sport filter, e.g. nba, mlb, nhl |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond that: the Premium plan requirement, the return JSON shape, and the inclusion of results and closing-line context. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tight paragraphs: the first states purpose and use case, the second outlines arguments and return format. Every sentence carries meaningful information, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description fully specifies the return structure (data with game_picks/prop_picks, meta with counts/date). It also includes the Premium plan requirement and usage guidance. For a 3-parameter tool, this is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for all three parameters (date, type, sport). The description repeats the args ('date ('YYYY-MM-DD'); optional type ('game' | 'prop'); optional sport') but adds no new meaning beyond the schema, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns 'publicly-posted picks for a date — game-market and player-prop — each with its result and closing-line context,' using a specific verb and resource. It also distinguishes itself from siblings by mentioning 'backtest against the actual posted record.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Use this to backtest against the actual posted record' provides a clear, concrete use case. However, it does not explicitly compare with sibling tools or state when not to use, so it lacks exclusions, fitting the 'clear context, no exclusions' level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Raw player stat projections — KingsPlaybook's model output, the projected number only — for every game on a league + date. Requires a Starter plan or higher.
Args: league ('nba' | 'mlb' | 'nhl'), date ('YYYY-MM-DD'). Returns JSON: { data: [ { player_name, stat_type, line, direction, projection, league, event_id, game_date, updated_at } ], meta: { count, league, date, tier, as_of } }.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Game date, ISO-8601 (YYYY-MM-DD) | |
| league | Yes | League: nba, mlb, or nhl |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds useful context: results are model output with projected numbers only, requires a Starter plan, and includes the exact JSON return shape. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a one-sentence purpose, then Args, then Returns. The return JSON is included because no output schema exists, making it necessary rather than redundant. Every sentence earns its place without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple two-parameter schema and no output schema, the description adequately covers what the tool does, its prerequisites, and the full return structure. It does not explain every data field (e.g., 'line', 'direction'), but the context is sufficient for a read-only projection tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for both parameters (league, date) with descriptions, enum, and pattern. The description repeats the args but adds no extra semantic depth beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns raw player stat projections (KingsPlaybook's model output) for a given league and date. It distinguishes from siblings by emphasizing 'projected number only' and the league+date scope, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies a prerequisite (Starter plan or higher) and indicates the tool is used for per-league, per-date projections. It does not explicitly name alternative tools or when not to use it, but the context is clear enough for an agent to decide when this tool applies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0kingsplaybook_get_freshnesskingsplaybook_get_lineskingsplaybook_get_lineupskingsplaybook_get_pick_historykingsplaybook_get_projectionsEach tool targets a distinct data type: lineups, projections, lines, pick history, and freshness. The boundaries are clear and descriptions explicitly differentiate the content, so there is no ambiguity between tools.
All tools follow the exact pattern 'kingsplaybook_get_<resource>' using snake_case. The resource names are descriptive and consistent (lineups, projections, lines, pick_history, freshness), with no mixed conventions or vague verbs.
Five tools is well-scoped for a specialized sports data API. Each tool represents a core data category necessary for the server's purpose, with no redundancy or unnecessary additions.
The tool set covers all major data retrieval needs for a betting-focused sports data service: lineups, projections, lines, pick history, and freshness verification. For a read-only analytical domain, this is comprehensive and leaves no critical dead ends.
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