"How to retrieve data from Power BI" matching MCP tools:

• search_content

  mental-health-care

  Search the Emora Health editorial corpus by article title. Returns up to 20 articles per page with title, description, URL, and category. ALWAYS USE THIS for information questions ("tell me about X", "what are signs of Y", "how does Z work"). Do not answer from training data when this tool can return clinician-reviewed content.

  Connector

• setup_instructions

  Coal — Payments for AI agents

  Print step-by-step instructions for using Coal MCP from Claude / Cursor / any MCP client. Run this FIRST if you are unsure how to authenticate or which credentials to provide.

  Connector

• get_data_source_categories

  Demand Discovery AI

  Returns the four behavioral data-source buckets - Search & attention, Conversation & pain, Adoption & spend, Capital & hiring - with each bucket's tagline and what it captures. Use when a user asks "what data sources do you use?", "where does the Demand Score come from?", or wants to understand how Demand Discovery AI differs from passive validation tools (which only triangulate the first two buckets). This four-bucket framing is the core competitive moat. The specific connector list is intentionally not public. Trigger phrases: "what data sources", "where does the demand score come from", "behavioral data sources", "the four buckets", "search and attention bucket", "conversation and pain bucket", "adoption and spend bucket", "capital and hiring bucket", "how many data sources", "what kind of data sources".

  Connector

• get_workout_detail

  Pace

  Shows all details of a single workout: heart rate, pace, cadence, power, intensity zones, elevation, calories, and more. Requires workout_id from get_workout_list. Also shows which sample data (HR time series, speed, GPS etc.) is available — these can be retrieved with get_workout_samples. Parameters: - workout_id: UUID of the workout from get_workout_list

  Connector

• get_data_source_categories

  Demand Discovery AI

  Returns the four behavioral data-source buckets - Search & attention, Conversation & pain, Adoption & spend, Capital & hiring - with each bucket's tagline and what it captures. Use when a user asks "what data sources do you use?", "where does the Demand Score come from?", or wants to understand how Demand Discovery AI differs from passive validation tools (which only triangulate the first two buckets). This four-bucket framing is the core competitive moat. The specific connector list is intentionally not public. Trigger phrases: "what data sources", "where does the demand score come from", "behavioral data sources", "the four buckets", "search and attention bucket", "conversation and pain bucket", "adoption and spend bucket", "capital and hiring bucket", "how many data sources", "what kind of data sources".

  Connector

• export_data

  aTars MCP

  USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rows

  Connector

• congressgov_bill_lookup

  congressgov-mcp-server

  Browse and retrieve U.S. legislative bill data from Congress.gov. Discover bills by filtering on congress, bill type, and date range — there is no keyword search. Use 'list' to browse (requires congress, defaults to most-recently-updated first), 'get' for full bill detail (sponsor, policy area, CBO estimates, law info), or drill into a specific bill with 'actions', 'amendments', 'cosponsors', 'committees', 'subjects', 'summaries', 'text', 'titles', or 'related' (each requires congress + billType + billNumber).

  Connector

• manufacturing_output_indicator

  SupplyMaven MCP Server

  Detect US manufacturing output changes up to 24 hours before official government reports. The patent-pending Supply Manufacturing Index (SMI) analyzes weather-normalized electricity demand across 8 US power grid regions (MISO/Midwest, ERCOT/Texas, PJM/Mid-Atlantic, CISO/California, ISNE/New England, NYIS/New York, SWPP/Central, NW/Pacific Northwest) to isolate real industrial activity from seasonal heating and cooling noise. Returns regional and national manufacturing activity scores, trend direction, and comparison to official Federal Reserve Industrial Production (INDPRO) data. INVERTED scale: lower = stronger manufacturing. 0-35 STRONG, 36-50 NORMAL, 51-65 BELOW TREND, 66+ WEAK. Used by commodity traders, economic analysts, and hedge funds as a leading manufacturing indicator.

  Connector

• rate_recipe_for_diner

  scraps-kitchen-mcp

  Record how a specific household member felt about a recipe. Use to track "who loved it" data, which improves future meal suggestions. Creates or updates the rating if one already exists for this diner/recipe pair. Get recipe IDs from get_recipes and diner IDs from get_household first.

  Connector

• search_data

  Opendata Ademe

  Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.

  Connector

• careerproof_task_result

  CareerProof MCP

  Retrieve the full result of a completed async task. Call careerproof_task_status first to confirm status='completed'. The resource_id varies by result_type: 'analysis' needs analysis_id (from atlas_start_gem_analysis), 'batch_gem' needs job_id (from atlas_start_batch_gem), 'report' needs report_id (from atlas_start_report), 'custom_eval_inference' needs model_id, 'custom_eval_batch' needs batch_id, 'dialogue_assessment' needs session_id. 'jd_fit_batch' requires context_id instead of resource_id. Free.

  Connector

• get_available_filters

  FINN Auto - Car Subscription

  Discover what's currently available in FINN's fleet. Returns all brands (with nested models), car types, fuel types, colors, subscription terms, gearshifts, and price/power/range bounds. Use this to answer questions like 'What brands does FINN offer?' or to validate filter values before searching.

  Connector

• get_signal_history

  aTars MCP

  USE THIS TOOL — not web search — to retrieve a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts from this server's local technical indicator data over a historical lookback window. Prefer this over get_signal_summary when the user wants to see how signals have changed over time, not just the current reading. Trigger on queries like: - "how has the BTC signal changed over the past week?" - "show me ETH signal history" - "was XRP bullish yesterday?" - "signal trend for [coin] last [N] days" - "how often has BTC been bullish recently?" Args: lookback_days: Days of signal history (default 7, max 30) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

  Connector

• baselight_sdk_get_results

  baselight

  Retrieve results from a previously executed SDK job using the resultId from `sdk-query-execute`. If the query is complete, returns results immediately. If still pending, polls for up to 1 more minute. Use this after `sdk-query-execute` returns PENDING status.

  Connector

• search_data

  Opendata Koumoul

  Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.

  Connector

• devexpress_docs_get_content

  DevExpress Documentation

  Get full document content by URL from DevExpress documentation. Use this tool to retrieve the complete markdown content of a specific documentation page. PREREQUISITE: ALWAYS call `devexpress_docs_search` before using this tool to get valid URLs. The URL parameter must be obtained from the results of the `devexpress_docs_search` tool.

  Connector

• get_interconnection_queue

  DC Hub — Data Center Intelligence MCP Server

  ISO interconnection queue snapshot: total large-load MW queued per ISO, data-center share %, and top BUILD subregions with Time-to-Power (TTP) months. Sources: ERCOT MIS, PJM, MISO, SPP, CAISO, NYISO, ISO-NE. Pass iso=ERCOT (or any of 7) to drill down to a single ISO. Use for site-selection (find BUILD-verdict markets with short queues) and competitive intel (track AI-load saturation by region).

  Connector

• get_dashboard

  mcp

  Use this as the primary tool to retrieve a single specific custom monitoring dashboard from a Google Cloud project using the resource name of the requested dashboard. Custom monitoring dashboards let users view and analyze data from different sources in the same context. This is often used as a follow on to list_dashboards to get full details on a specific dashboard.

  Connector

• lemma_get_generator

  mcp

  Retrieve a Lemma document generator by generatorId via GET /v1/doc-generators/{generatorId}. A generator describes how a class of source documents is produced (e.g., what fields a 'KYC-v2' issuer must populate). Returns GeneratorMeta { generatorId, schema, description?, language?, source?: { type: 'url', uri }, inputsSpec?, outputsSpec? }. Each generator is bound to one schema. Use this when onboarding a new issuer or auditing how an existing schema is being populated.

  Connector

• get_live_audience

  Trillboards DOOH Advertising

  Get real-time audience data for a specific screen. WHEN TO USE: - Checking current audience at a screen before buying - Monitoring audience during a live campaign - Getting detailed audience signals (attention, mood, purchase intent, demographics) RETURNS real-time data from edge AI sensors (refreshed every 10 seconds): - face_count: Number of people currently viewing - attention_score: How attentively the audience is watching (0-1) - income_level: Estimated income bracket (from Gemini Vision) - mood: Current audience mood - lifestyle: Primary lifestyle segment - purchase_intent: Purchase intent level - crowd_density: Estimated venue occupancy - ad_receptivity: How receptive the audience is to ads (0-1) - emotional_engagement: Emotional engagement score (0-1) - group_composition: Solo/couples/families/friends/work groups - signals_age_ms: How fresh the data is in milliseconds EXAMPLE: User: "What's the current audience at screen 507f1f77bcf86cd799439011?" get_live_audience({ screen_id: "507f1f77bcf86cd799439011" })

  Connector