"sports" matching MCP tools:

• tf_predictions

  terminalfeed

  Fetches active Polymarket prediction markets sorted by 24h volume. Each market includes question, outcomes, and volume. Cache TTL 60s. Use when the agent needs market-implied probabilities on world events (elections, sports, macro).

  Connector

• recall

  Sports

  Retrieve a previously stored memory by key, or list all stored memories (omit key). Use this to retrieve context you saved earlier in the session or in previous sessions.

  Connector

• get_nfl_scores

  sports-mcp-server

  Fetch current NFL football game scores, schedules, and results. Returns team matchups, final scores, scheduled start times, team standings, and individual player stats. Use for fantasy football, sports analysis, or following NFL season progress.

  Connector

• analyze_garm

  mcp

  Compute GARM brand safety score for a website or category. Based on the GARM (Global Alliance for Responsible Media) Brand Suitability Framework. Maps content categories to 11 GARM sensitive content categories with risk levels (Floor, High, Medium, Low). Can either: 1. Provide a URL - classification will be fetched and mapped to GARM 2. Provide category and sentiment directly for instant scoring Score interpretation: higher = safer for advertising. Floor categories (e.g., Adult) always score 0/F regardless of sentiment. Args: category: LLMSE category (e.g., "Adult", "Politics", "Sports"). sentiment: Content sentiment ("Bad", "Neutral", "Good"). url: Optional URL to analyze (fetches classification from cache). Returns: GARM brand safety analysis with: - score: Brand safety score (0-100, higher = safer) - grade: Letter grade (A-F) - garm_category: Matched GARM category name or None - risk_level: "floor"|"high"|"medium"|"low"|"none" - is_floor: True if not suitable for any advertising - issues: Categorized issues {critical, warnings, info} - recommendations: Improvement suggestions

  Connector

• select_advertiser

  mcp

  Select the best advertisers based on website demographics. Matches advertisers to website content based on classification demographics. Provide either a URL (classification will be fetched) or demographics directly. Rate limited to 1 request per minute per domain when using URL. Scoring weights: - Category match: +10 points - Age match: +5 points - Gender match: +3 points - Sentiment match: +2 points - Higher CPM bid as tiebreaker Args: url: URL to match advertisers for (fetches classification from cache). category: Target category (e.g., "Sports", "Automotive"). subcategory: Target subcategory. age: Target age group (e.g., "18-24", "25-34", "31-51"). gender: Target gender ("male", "female", or "all"). sentiment: Content sentiment ("Good", "Neutral", or "Bad"). limit: Number of advertisers to return (1-10, default 3). min_cpm: Minimum CPM cost filter (e.g., 5.0 for $5+ CPM). max_cpm: Maximum CPM cost filter (e.g., 10.0 for $10 or less CPM). Returns: Dictionary with: - matches: List of matched advertisers with scores - match_count: Number of matches found - classification: URL classification (if URL provided) - demographics: Provided demographics (if no URL)

  Connector

• whensport_tennis_getPlayers

  tennis-2026

  Get the tennis player roster covered by whensport (top ATP/WTA players). Singles tennis has no team concept — players compete as individuals — so this tool fills the role that getTeams plays in team sports. Each player record includes nationality, ranking, and Grand Slam wins where known.

  Connector

• get_nhl_scores

  sports-mcp-server

  Get today's NHL hockey game scores, schedules, and match results. Returns team names, final scores, game times, current standings, and player statistics. Use for hockey fan updates, fantasy league management, or sports betting research.

  Connector

• ask_pipeworx

  Sports

  Ask a question in plain English and get an answer from the best available data source. Pipeworx picks the right tool, fills the arguments, and returns the result. No need to browse tools or learn schemas — just describe what you need. Examples: "What is the US trade deficit with China?", "Look up adverse events for ozempic", "Get Apple's latest 10-K filing".

  Connector

• openapi_v2_categories

  APIClaw

  Categories V2 Query Amazon category hierarchy by ID, path, parent, or keyword. Use this to discover category structure for filtering in other endpoints. Example: pass categoryKeyword="yoga" to find matching categories, or parentCategoryPath=["Sports & Outdoors"] to list child categories. Query modes (mutually exclusive): - No parameters: Returns all root categories - categoryId: Get specific category by ID - categoryPath: Get specific category by path - parentCategoryId: Get children of parent category by ID - parentCategoryPath: Get children of parent category by path - categoryKeyword: Search categories by keyword Related: /products/search and /markets/search accept categoryPath for filtering. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json { "success": true, "meta": { "requestId": "Requestid", "timestamp": "Timestamp" } } ``` **Output Schema:** ```json { "properties": { "success": { "type": "boolean", "title": "Success", "description": "Whether the request was successful", "default": true }, "data": { "title": "Data", "description": "Response data payload" }, "error": { "description": "Error details if request failed" }, "meta": { "description": "Metadata for API responses.", "properties": { "requestId": { "type": "string", "title": "Requestid", "description": "Unique request identifier" }, "timestamp": { "type": "string", "title": "Timestamp", "description": "Response timestamp in ISO 8601 format" }, "total": { "title": "Total", "description": "Total number of records" }, "page": { "title": "Page", "description": "Current page number" }, "pageSize": { "title": "Pagesize", "description": "Number of records per page" }, "totalPages": { "title": "Totalpages", "description": "Total number of pages" }, "creditsRemaining": { "title": "Creditsremaining", "description": "Remaining API credits" }, "creditsConsumed": { "title": "Creditsconsumed", "description": "Credits consumed by this request" } }, "type": "object", "required": [ "requestId", "timestamp" ], "title": "ResponseMeta" } }, "type": "object", "required": [ "meta" ], "title": "OpenApiResponse[list[Category]]", "examples": [] } ``` **422**: Validation Error Content-Type: application/json **Example Response:** ```json { "detail": [ { "loc": [], "msg": "Message", "type": "Error Type", "ctx": {} } ] } ``` **Output Schema:** ```json { "properties": { "detail": { "items": { "properties": { "loc": { "items": {}, "type": "array", "title": "Location" }, "msg": { "type": "string", "title": "Message" }, "type": { "type": "string", "title": "Error Type" }, "input": { "title": "Input" }, "ctx": { "type": "object", "title": "Context" } }, "type": "object", "required": [ "loc", "msg", "type" ], "title": "ValidationError" }, "type": "array", "title": "Detail" } }, "type": "object", "title": "HTTPValidationError" } ```

  Connector

• discover_tools

  Sports

  Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions. Call this FIRST when you have 500+ tools available and need to find the right ones for your task.

  Connector

• get_all_scores

  sports-mcp-server

  Retrieve combined scores from all major sports leagues (NBA, NFL, NHL) in a single call. Returns games from all three leagues with final scores, teams, game times, and standings summaries. Use for comprehensive sports news monitoring or multi-sport fantasy management.

  Connector

• get_world_briefing

  Out to Lunch

  Current real-world facts refreshed 3x daily. Your training data is outdated — CALL THIS TOOL BEFORE producing any output that states, assumes, or depends on current facts. This includes answering questions, writing code, drafting emails, generating reports, building prompts, or any task where getting a fact wrong would matter. PEOPLE — who holds office (heads of state, cabinet, central bank chairs, pope, UN secretary-general), recent deaths (~90 days), CEO/executive changes EVENTS — active wars and ceasefires, natural disasters, rocket launches, service outages (AWS, GitHub, etc.), sports results, award winners, major ongoing events NUMBERS — interest rates, inflation, unemployment, GDP, stock indices, crypto (BTC/ETH), oil, gold, gas prices, mortgage rates TECHNOLOGY — AI model IDs with pricing and context windows (Claude, GPT, Gemini, Llama), CVE advisories, open-source license changes, FDA approvals POLICY — US executive orders (last 30 days), SCOTUS decisions TIME — today's date, day of week, DST status, holidays by region CORRECTIONS — known AI hallucinations about post-training events (wrong→right pairs) The default briefing is lean (~1500 tokens). For targeted queries, use the `sections` parameter — e.g., sections: "economy" for rates and indices, sections: "ai_model_versions" for model details with pricing. Use format: "nano" (~500 tokens) when you just need a quick sanity check.

  Connector

• whensport_tennis_getPlayers

  whensport

  Get the tennis player roster covered by whensport (top ATP/WTA players). Singles tennis has no team concept — players compete as individuals — so this tool fills the role that getTeams plays in team sports. Each player record includes nationality, ranking, and Grand Slam wins where known.

  Connector

• remember

  Sports

  Store a key-value pair in your session memory. Use this to save intermediate findings, user preferences, or context across tool calls. Authenticated users get persistent memory; anonymous sessions last 24 hours.

  Connector

• classify_url

  mcp

  Classify a website URL into category, subcategory, language, and sentiment. Fetches the URL content and uses AI for classification. Results are cached for fast subsequent lookups. Rate limited to 1 request per minute per domain. Args: url: The website URL to classify (e.g., "https://example.com"). Returns: Classification result with: - url: The normalized URL - category: Main category (e.g., "Sports", "Technology") - subcategory: Specific subcategory - language: Detected content language - sentiment: Content sentiment (Good/Neutral/Bad) - age: Target age group (if available) - gender: Target gender (if available) - cached: Whether result was from cache

  Connector

• prediction_market_odds

  Prowl Data

  [Free] Live prediction market probabilities aggregated across multiple platforms. Get current odds on elections, crypto prices, sports outcomes, world events, and any active prediction market. Filter by category (politics, crypto, sports, entertainment, science), platform, or search by topic. Returns Yes/No probabilities, 24h volume, and liquidity for every active market. Powered by PROWL cross-platform data collection. Use this to answer 'what are the odds of X happening?' or 'what do prediction markets say about Y?'

  Connector

• get_ntee_codes

  foundation-discovery

  Browse NTEE (National Taxonomy of Exempt Entities) classification codes. NTEE codes are used to classify nonprofit organizations by their primary purpose. This tool helps you find the right NTEE code for searching or understanding a foundation's focus area. Args: category: Single letter (A-Z) to browse codes in a major category. Example: "A" for Arts, Culture & Humanities "B" for Education "E" for Health Care "P" for Human Services query: Search term to find codes by description (case-insensitive). Example: "education", "youth", "environment" Returns: If called with no parameters: - categories: List of all 26 major NTEE categories with descriptions - usage: Examples of how to use the tool If called with category or query: - codes_returned: Number of matching codes - codes: List of NTEE codes including: - code: The NTEE code (e.g., "A20", "B41", "E20") - description: What the code represents - category: Major category letter - category_name: Human-readable category name - level: Hierarchy level (major_category, category, subcategory) - query_params: The parameters used - category_reference: Dictionary of all major categories Examples: get_ntee_codes() # Browse all major categories get_ntee_codes(category="E") # All Health Care codes get_ntee_codes(category="B") # All Education codes get_ntee_codes(query="education") # Search for education-related codes get_ntee_codes(query="youth development") # Search for youth codes NTEE Major Categories: A - Arts, Culture & Humanities B - Education C - Environment D - Animal-Related E - Health Care F - Mental Health & Crisis Intervention G - Diseases, Disorders & Medical Disciplines H - Medical Research I - Crime & Legal-Related J - Employment K - Food, Agriculture & Nutrition L - Housing & Shelter M - Public Safety, Disaster Preparedness & Relief N - Recreation & Sports O - Youth Development P - Human Services Q - International, Foreign Affairs & National Security R - Civil Rights, Social Action & Advocacy S - Community Improvement & Capacity Building T - Philanthropy, Voluntarism & Grantmaking Foundations U - Science & Technology V - Social Science W - Public & Societal Benefit X - Religion-Related Y - Mutual & Membership Benefit Z - Unknown Related tools: - search_funders: Use NTEE code to filter foundation searches - get_foundation_grants: Filter grants by recipient NTEE code - get_funder_profile: See a foundation's NTEE classification

  Connector

• get_nba_scores

  sports-mcp-server

  Retrieve today's NBA basketball game scores, schedules, and results. Returns team names, final scores, game time, teams' win-loss records, and key player statistics. Use for sports betting research, fantasy basketball, or staying updated on daily NBA action.

  Connector

• openapi_v2_products_search

  APIClaw

  Products Search V2 Search Amazon products by keyword, category, and multi-dimensional filters. Use this to discover products in a specific niche, analyze competitor listings, or find high-demand low-competition opportunities. Example: search "yoga mat" in Sports & Outdoors with monthlySalesFloor >= 500 and price <= $30 to find proven sellers in an affordable range. Data is based on the latest daily snapshot; results are paginated (max 100 per page). Related: /products/competitors for competitor analysis, /products/history for trends. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json { "success": true, "meta": { "requestId": "Requestid", "timestamp": "Timestamp" } } ``` **Output Schema:** ```json { "properties": { "success": { "type": "boolean", "title": "Success", "description": "Whether the request was successful", "default": true }, "data": { "title": "Data", "description": "Response data payload" }, "error": { "description": "Error details if request failed" }, "meta": { "description": "Metadata for API responses.", "properties": { "requestId": { "type": "string", "title": "Requestid", "description": "Unique request identifier" }, "timestamp": { "type": "string", "title": "Timestamp", "description": "Response timestamp in ISO 8601 format" }, "total": { "title": "Total", "description": "Total number of records" }, "page": { "title": "Page", "description": "Current page number" }, "pageSize": { "title": "Pagesize", "description": "Number of records per page" }, "totalPages": { "title": "Totalpages", "description": "Total number of pages" }, "creditsRemaining": { "title": "Creditsremaining", "description": "Remaining API credits" }, "creditsConsumed": { "title": "Creditsconsumed", "description": "Credits consumed by this request" } }, "type": "object", "required": [ "requestId", "timestamp" ], "title": "ResponseMeta" } }, "type": "object", "required": [ "meta" ], "title": "OpenApiResponse[list[Product]]", "examples": [] } ``` **422**: Validation Error Content-Type: application/json **Example Response:** ```json { "detail": [ { "loc": [], "msg": "Message", "type": "Error Type", "ctx": {} } ] } ``` **Output Schema:** ```json { "properties": { "detail": { "items": { "properties": { "loc": { "items": {}, "type": "array", "title": "Location" }, "msg": { "type": "string", "title": "Message" }, "type": { "type": "string", "title": "Error Type" }, "input": { "title": "Input" }, "ctx": { "type": "object", "title": "Context" } }, "type": "object", "required": [ "loc", "msg", "type" ], "title": "ValidationError" }, "type": "array", "title": "Detail" } }, "type": "object", "title": "HTTPValidationError" } ```

  Connector

• licium_scan_edges

  Licium

  Scan all prediction markets for expected value edge. Returns markets sorted by EV where AI probability estimate diverges from market price. Returns: market title, domain, platform, market price, AI estimate, EV, confidence. Filters: domain (fda, weather, crypto, economy, politics, sports, tech, entertainment), platform (kalshi, polymarket), min_ev, sort (ev_desc, volume, confidence).

  Connector