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135,502 tools. Last updated 2026-05-25 23:15

"How to extract and analyze data from a MongoDB database" matching MCP tools:

  • Rollback a project to a previous version. ⚠️ WARNING: This reverts schema AND code to the specified commit. Database data is NOT rolled back. Use get_version_history to find the commit SHA of the version you want to rollback to. After rollback, use get_job_status to monitor the redeployment. Rollback is useful when a schema change breaks deployment.
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  • Returns the complete surveillance intelligence record for a domain name. If the domain is in TunnelMind's tracker database (80,000+ entries), the response includes tracker category, risk score, fingerprinting data, cookie persistence, IAB TCF purposes, and the owning corporate entity. If the domain is not in the database, a live probe is automatically run: RDAP registration data, DNS records (MX, SPF, TXT verification tokens), HTTP headers, and CSP third-party actors are fetched fresh from the edge and returned. Use this tool when: - You need to know whether a specific domain tracks users, and how aggressively. - You are researching who owns a domain and what corporate entity controls it. - You want to check HTTP security headers and third-party services embedded in a site. - You are building a risk score for a domain before routing traffic through it. Do NOT use this tool when: - You want to search by keyword or category — use `search` instead. - You want all domains for an entity — use `get_entity` instead. - You want jurisdiction routing data — use `get_ghostroute_cert` instead. Inputs: - `domain` (path, required): Domain name. Strip `www.` prefix — it is removed automatically. Subdomains are resolved to the parent: `ads.doubleclick.net` → `doubleclick.net`. Examples: `doubleclick.net`, `google-analytics.com`, `intercom.io`. Returns: - Full `DomainRecord`. Free tier returns the domain, category, score, prevalence, and entity name. Pro/enterprise additionally return `tcf_vendor_id`, `tcf_purposes`, `tcf_features`, and `disconnect_cats`. - If the domain is not in the tracker database, `live_lookup: true` is set and RDAP/DNS/HTTP probe results are returned instead of tracker fields. - 404 if the domain cannot be found via live probe either (unknown TLD, unreachable). Cost: - Free tier: included in 50 req/day limit. Pro/enterprise: included in plan. Latency: - Database hit: typical <100ms, p99 <300ms. - Live probe: typical 2-5s, p99 10s (external DNS/HTTP calls).
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  • 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".
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  • 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".
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  • DESTRUCTIVE — IRREVERSIBLE. Permanently delete a file from the user's Drive. Removes the file from S3 storage and the database. Storage quota is freed immediately. ALWAYS ask for explicit user confirmation before calling this tool. # delete_file ## When to use DESTRUCTIVE — IRREVERSIBLE. Permanently delete a file from the user's Drive. Removes the file from S3 storage and the database. Storage quota is freed immediately. ALWAYS ask for explicit user confirmation before calling this tool. ## Parameters to validate before calling - file_token (string, required) — The file token (UUID) of the file to delete. Get via fetch_files. ## Notes - DESTRUCTIVE — IRREVERSIBLE. Always confirm with the user before calling. Explain what will be lost.
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  • Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文:获取数据库整体统计(供应商总数、面料总数、产业带总数、关联记录数)。动态快照,含生成时间戳。
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  • Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.

  • Access comprehensive company data including financial records, ownership structures, and contact information. Search for businesses using domains, registration numbers, or LinkedIn profiles to streamline due diligence and lead generation. Retrieve historical financial performance and complex corporate group structures to support informed business analysis.

  • 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.
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • Search for equivalent terms across multiple medical terminologies. Use this tool to: - Find the same concept in different coding systems - Compare how terminologies represent a concept - Support terminology mapping and data integration Searches across: ICD-11, SNOMED CT, LOINC, RxNorm, and MeSH. Set `target_terminologies` to limit which are searched, or set `source_terminology` to exclude one (e.g. when you already have a code from that terminology and want equivalents elsewhere). The two combine: source is subtracted from targets.
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  • Ask anything about this API: commodities covered, how on-chain provenance works, pricing tiers, x402 payment flow, MCP integration, or the Extract API. Also ask how to use this data as input for UFLPA compliance, EU Battery Regulation 2023/1542 sourcing disclosures, CBAM/CSDDD supply-chain research, or DoD/DFC domestic mineral sourcing assessments. Free to call. Returns a natural-language answer from a small LLM grounded on the API docs.
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  • 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.
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  • Extract clean readable text from any URL. No API key needed. Returns title, author, publish date, and full body text. Args: url: Full URL to scrape (must start with https://)
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  • 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" })
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  • Permanently delete a stored memory by its UUID. This is a hard delete for GDPR right-to-erasure compliance. The memory is removed from both the vector store and the database. This action cannot be undone.
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  • Use this tool whenever the user shares, uploads, or references a PDF file and wants to read, summarise, search, or analyse its contents. Extracts all plain text from the PDF (base64-encoded). Returns text, page count, word count, and character count. Call this first before attempting any analysis of PDF content — e.g. 'summarise this PDF', 'what does this contract say', 'extract the data from this report'. Free, no API key, no signup; the file is processed in memory and never stored.
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  • Sync ALL tenants: push Builder FS → GitHub, then pull GitHub → Core MongoDB. Requires master key authentication. Returns a summary table with results for each tenant/solution.
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  • Extract metadata from a PCD point cloud file. Returns point count, field definitions, data format, organization, viewpoint, feature flags (RGB, intensity, normals, curvature), bounding box, centroid, and point density estimate. Payment via x402 (USDC on Base) or card via MPP (Stripe). See format_auto for payment flow details. Privacy policy: https://caliper.fit/privacy
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  • Get the seat map for a flight from our database. Shows all seats, cabin classes, characteristics, and availability as both text and an interactive visual seatmap. The interactive app lets users click seats for details, filter by cabin, and find best seats. This returns cached data — for fresh/updated data, use search_flight with your API key.
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  • Extract structured information from web pages using LLM capabilities. Supports both cloud AI and self-hosted LLM extraction. **Best for:** Extracting specific structured data like prices, names, details from web pages. **Not recommended for:** When you need the full content of a page (use scrape); when you're not looking for specific structured data. **Arguments:** - urls: Array of URLs to extract information from - prompt: Custom prompt for the LLM extraction - schema: JSON schema for structured data extraction - allowExternalLinks: Allow extraction from external links - enableWebSearch: Enable web search for additional context - includeSubdomains: Include subdomains in extraction **Prompt Example:** "Extract the product name, price, and description from these product pages." **Usage Example:** ```json { "name": "firecrawl_extract", "arguments": { "urls": ["https://example.com/page1", "https://example.com/page2"], "prompt": "Extract product information including name, price, and description", "schema": { "type": "object", "properties": { "name": { "type": "string" }, "price": { "type": "number" }, "description": { "type": "string" } }, "required": ["name", "price"] }, "allowExternalLinks": false, "enableWebSearch": false, "includeSubdomains": false } } ``` **Returns:** Extracted structured data as defined by your schema.
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  • Extract clean readable text from a webpage. Strips HTML, navigation, ads, scripts, and boilerplate. Returns clean text, title, description, word count, and language. Args: url: The URL to extract text from. max_length: Max characters to return (100-50000). Default 5000.
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