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127,142 tools. Last updated 2026-05-05 09:01

"iPhone 16e camera review summary and table creation in Italian" matching MCP tools:

  • Recover detail from camera-shake and accidental motion blur. NAFNet (ECCV 2022, SOTA on GoPro/SIDD benchmarks). Best for: handheld shake, bumped camera, whole-frame uniform blur. NOT effective for: intentional panning blur, bokeh/depth-of-field, or artistic motion effects. Also supports denoising (grainy/noisy photos). 20 sats per image (~2 min processing), pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='deblur_image'.
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  • Create multiple tasks in a single operation with escrow calculation. ⚠️ **WARNING**: This tool BYPASSES the standard payment flow by calling db.create_task() directly instead of using the REST API (POST /api/v1/tasks). This means it skips x402 payment verification and balance checks. For production use, tasks should be created via the REST API to ensure proper payment authorization and escrow handling. Supports two operation modes: - ALL_OR_NONE: Atomic creation (all tasks or none) - BEST_EFFORT: Create as many as possible Process: 1. Validates all tasks in batch 2. Calculates total escrow required 3. Creates tasks (atomic or best-effort) - **BYPASSING PAYMENT FLOW** 4. Returns summary with all task IDs Args: params (BatchCreateTasksInput): Validated input parameters containing: - agent_id (str): Your agent identifier - tasks (List[BatchTaskDefinition]): List of tasks (max 50) - payment_token (str): Payment token (default: USDC) - operation_mode (BatchOperationMode): all_or_none or best_effort - escrow_wallet (str): Optional custom escrow wallet Returns: str: Summary of created tasks with IDs and escrow details.
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  • Create multiple tasks in a single operation with escrow calculation. ⚠️ **WARNING**: This tool BYPASSES the standard payment flow by calling db.create_task() directly instead of using the REST API (POST /api/v1/tasks). This means it skips x402 payment verification and balance checks. For production use, tasks should be created via the REST API to ensure proper payment authorization and escrow handling. Supports two operation modes: - ALL_OR_NONE: Atomic creation (all tasks or none) - BEST_EFFORT: Create as many as possible Process: 1. Validates all tasks in batch 2. Calculates total escrow required 3. Creates tasks (atomic or best-effort) - **BYPASSING PAYMENT FLOW** 4. Returns summary with all task IDs Args: params (BatchCreateTasksInput): Validated input parameters containing: - agent_id (str): Your agent identifier - tasks (List[BatchTaskDefinition]): List of tasks (max 50) - payment_token (str): Payment token (default: USDC) - operation_mode (BatchOperationMode): all_or_none or best_effort - escrow_wallet (str): Optional custom escrow wallet Returns: str: Summary of created tasks with IDs and escrow details.
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  • Return a table surface's column definitions so an agent knows what keys create_row/update_row will accept. Each column has `key` (the field name in row.data), `label` (human-readable), `type` (text | longtext | url | status | owner | date | number), `position`, and, for status/owner columns, the allowed `options`. Empty array on doc-only workspaces; callers should still be able to write rows (columns auto-seed on first write). Multi-surface workspaces accept `surface_slug` to scope to a specific table sheet (use `list_surfaces` to enumerate); omit to fall through to the workspace's primary table surface.
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  • List all generated reports with status and summary info. Returns an array of report objects with id, report_type, status, title, and summary. Use the report id with atlas_get_report for details or atlas_download_report to download completed PDFs. Free.
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Matching MCP Servers

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    An MCP server that enables interaction with local camera devices to capture and process images. It allows LLMs to access video devices with configurable settings such as resolution, orientation, and image format.
    Last updated
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    MIT

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  • performance-review MCP — wraps StupidAPIs (requires X-API-Key)

  • Capture photos remotely from mobile devices via S3-backed upload URLs

  • Describe a specific table. ⚠️ WORKFLOW: ALWAYS call this before writing queries that reference a table. Understanding the schema is essential for writing correct SQL queries. 📋 PREREQUISITES: - Call search_documentation_tool first - Use list_catalogs_tool, list_databases_tool, list_tables_tool to find the table 📋 NEXT STEPS after this tool: 1. Use generate_spatial_query_tool to create SQL using the schema 2. Use execute_query_tool to test the query This tool retrieves the schema of a specified table, including column names and types. It is used to understand the structure of a table before querying or analysis. Parameters ---------- catalog : str The name of the catalog. database : str The name of the database. table : str The name of the table. ctx : Context FastMCP context (injected automatically) Returns ------- TableDescriptionOutput A structured object containing the table schema information. - 'schema': The schema of the table, which may include column names, types, and other metadata. Example Usage for LLM: - When user asks for the schema of a specific table. - Example User Queries and corresponding Tool Calls: - User: "What is the schema of the 'users' table in the 'default' database of the 'wherobots' catalog?" - Tool Call: describe_table('wherobots', 'default', 'users') - User: "Describe the buildings table structure" - Tool Call: describe_table('wherobots_open_data', 'overture', 'buildings')
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  • List rows in a workspace's table surface. Returns rows with their data (a JSON object of column-name to value), creation time, the principal who created/updated each row, AND the row's `surface_slug` (the sheet it lives on). Empty array if no rows have been added yet. Multi-surface workspaces: pass `surface_slug` to scope to one sheet; omit to return rows from every surface in the workspace (back-compat: pre-multi-surface clients keep working).
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  • Get full detail for a specific parliamentary bill. Returns sponsors, current stage, long title, summary, and Royal Assent date if enacted. Summary text is capped per max_summary_chars — check summary_truncated in the response to see if it was cut.
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  • Get the JSON schema definition of a project in FLAT format. Returns the schema structure where each table name maps directly to field definitions. This is the same format required for create_project and update_schema. USE CASES: Review current schema before making updates, copy schema as template for new projects, verify schema structure after deployment, learn the correct schema format by example. The returned schema will be in FLAT format: {table_name: {field_name: {type, properties}}}
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  • Find out if there's money on the table. Returns a snapshot of supply/demand balance, unfilled opportunities, suggested pricing, and estimated earning potential in a hub. Call this before deciding whether to post a bid or ask.
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  • [READ] (CLIENT-SIDE) List Shillbot tasks awaiting your client review across all of your campaigns. Each entry is a task in 'submitted' state — agent has submitted content, you haven't yet called shillbot_approve_task or shillbot_reject_task on it. Use this to populate a review queue / inbox. Requires a registered wallet (the calling wallet must be the campaign client).
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  • Retrieve WHOIS registration data: registrar, creation/expiry dates, nameservers, status. Use to verify domain ownership, age, expiration; for full audit use domain_report. Free: 100/hr, Pro: 1000/hr. Returns {domain, whois: {registrar, creation_date, expiry_date, updated_date, name_servers, status, raw_length, error}, summary}.
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  • Search recipes by keyword across titles, descriptions, tags, and full source code. Use for any iOS, SwiftUI, or backend topic — e.g. subscription, authentication, camera, animation, chart, onboarding, paywall, infrastructure.
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  • Submit a completed medical intake questionnaire for provider review. All fields from intake_questions must be completed. Returns an intake ID and estimated provider review time. The intake is reviewed by a licensed US healthcare provider who makes all prescribing decisions. Requires authentication.
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  • Upload a DOCX file and create a draft signing envelope via DocuSign. Returns a review URL — the user must review and send from DocuSign. Never auto-sends. Authentication is handled automatically via OAuth — no API key needed.
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  • Full account profile: email, Telegram link status, current balance, lifetime spend, total orders, active API keys, and account creation date.
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  • Permanently delete a short creation request and all generated videos. Credits are NOT refunded. This action is irreversible.
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  • Update an existing event by eventId. Dates/times are interpreted in the provided timezone. Can update individual fields (summary, description, location) OR update times (must provide all: startDate, endDate, and optionally startTime, endTime). WARNING: Empty strings/arrays CLEAR fields. Cannot move between calendars. Returns updated event {id, summary, start, end, updated, ...}.
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  • Build a Tableau dashboard from a MySQL table (end-to-end). Pipeline: MySQL → schema inference → chart suggestion → workbook creation → live MySQL connection → .twb output. Requires mysql-connector-python for schema inference. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MySQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username. password: Database password (used for schema inference only; not stored in the workbook). port: Server port (default 3306). output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.
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