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131,189 tools. Last updated 2026-05-07 22:22

"A platform for shopping online or learning about the Amazon region" matching MCP tools:

  • Fetch complete campaign detail for one campaign. Required input: campaign_id. Output includes a campaign object with public campaign fields plus creator_wallet_address, and a separate funding_progress object with goal_amount, funded_amount, and percent_funded. Does not include creator_email. zooidfund does not verify campaign accuracy. Agents are responsible for their own verification. The platform makes no representations about campaign claims.
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  • List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.
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  • Find clusters of related learnings that are ripe for compression. When many similar solutions get linked together (e.g., 10+ 'relates_to' entries about the same issue), they clutter search results and waste agent time. Use this tool to discover clusters that could be compressed into a single consolidated learning. WORKFLOW: 1. Call get_compression_candidates with min_cluster_size=3 (or higher) 2. Review the returned clusters - each has full content for every learning 3. Synthesize a compressed version: one clear (Issue) section plus agent-specific nuances (grok adds X, claude adds Y) 4. Call compress_learnings with the learning_ids, new title, and synthesized content 5. Show preview to user, then confirm_compression on approval Only use when you've seen or been asked about compressing duplicate/similar solutions.
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  • Lists pre-configured reports (prebuilds) available for a connector. **What is a prebuild?** A prebuild is a standardized report maintained by Quanti for a given connector (e.g., Campaign Stats for Google Ads). It defines the BigQuery table structure (columns, types, metrics) and the associated API query. **When to use this tool:** - When the user asks "what reports are available for [connector]?" - When the user doesn't know which data or metrics exist for a connector - BEFORE get_schema_context, to explore available reports for a connector - To understand the data structure before writing SQL **Difference with get_schema_context:** - list_prebuilds → discover which reports/tables EXIST for a connector (catalog) - get_schema_context → get the actual BigQuery schema for the client project (effective data) **Response format:** Returns a JSON with for each prebuild: its ID, name, description, BigQuery table name, and the list of fields (name, type, description, is_metric). Fields marked is_metric=true are aggregatable metrics (impressions, clicks, cost...), others are dimensions (date, campaign_name...). **SKU examples**: googleads, meta, tiktok, tiktok-organic, amazon-ads, amazon-dsp, piano, shopify-v2, microsoftads, prestashop-api, mailchimp, kwanko
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  • Compare multiple product prices against an Amazon CPG category's peers. Use when a multi-channel CPG brand needs to stack-rank their SKUs — e.g. identifying which SKUs are underpriced relative to Amazon peers, flagging products where the Amazon Buy Box sits materially below the retail MSRP, or building a cross-channel price-audit table for an ops review. Replaces manual store walks and spreadsheet comparisons. Returns: comparisons (list, per product: name, price, percentile_rank, position, vs_median), category, category_trend, sample_size, last_refreshed, cta. Args: products: List of items, each a dict with 'name' (string) and 'price' (number in dollars). Minimum 1 item; 3-20 is the useful range. category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive.
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  • Remove all checked-off items from the shopping list at once. Use after a shopping trip when the user has bought everything marked. To remove a single item, use remove_shopping_list_item instead.
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Matching MCP Servers

Matching MCP Connectors

  • Amazon product search demand over time, with growth for any keyword. Free key at trendsmcp.ai

  • AI shopping comparison — search 50M+ products, compare prices, find deals

  • List devices registered in the workspace. Returns a list of Device objects with id, name, status, last_heartbeat, platform/hardware info, tags, and created_at. ``status`` is ``online`` if a heartbeat was received within the last 5 minutes, otherwise ``offline`` (or ``unknown`` for devices that have never reported). Requires the ``device:read`` scope. Device-scoped api_keys cannot call this endpoint and will receive 403.
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  • Permanently remove an item from the shopping list. To remove all checked-off items at once, use clear_checked_shopping_items instead. Get item IDs from get_shopping_list first.
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  • Return pricing-tier breakdown and category stats for an Amazon CPG category. Use when a brand is sizing up a shelf — e.g. evaluating whether a new SKU should enter at budget / midmarket / premium tier, benchmarking their retail pricing against Amazon tier structure, or preparing for a retail buyer meeting that will ask "what's the typical shelf price here?". Returns: category (resolved name), product_count (bucketed, e.g. "100+ products"), price_tiers (dict with budget / midmarket / premium dollar bands, rounded to nearest $0.50 for abstraction), median_price, trend_direction, last_refreshed, cta. Args: category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive.
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  • Add all ingredients from a saved recipe to the shopping list. Use when the user wants to shop for a specific recipe. Requires the recipe to have structured ingredient data (most recipes do after enrichment). Get recipe IDs from get_recipes first.
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  • Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.
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  • [BROWSE] List all active brands on the platform. Returns name, slug, headline, description, and product/brief counts. Use a brand slug with list_drops or list_briefs to filter by brand.
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  • Get the list of legal document templates available for generation on the platform (e.g. NDA, employment agreement, stock purchase agreement). For corporate services like 83(b) filing or registered agent, use get_available_corporate_services instead.
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  • Retrieve a list of all AWS regions. ## Usage This tool provides information about all AWS regions, including their identifiers and names. ## When to Use - When planning global infrastructure deployments - To validate region codes for other API calls - To get a complete AWS regional inventory ## Result Interpretation Each region result includes: - region_id: The unique region code (e.g., 'us-east-1') - region_long_name: The human-friendly name (e.g., 'US East (N. Virginia)') ## Common Use Cases 1. Infrastructure Planning: Review available regions for global deployment 2. Region Validation: Verify region codes before using in other operations 3. Regional Inventory: Get a complete list of AWS's global infrastructure
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  • Percentile-rank a single product price against tracked Amazon competitors in a CPG category. Use when a multi-channel CPG brand asks where their Amazon listing price sits against 100+ tracked products — e.g. checking whether a $4.99 granola is competitively positioned on Amazon, auditing whether a retail MSRP is reasonable against Amazon reality before a buyer meeting, or sanity-checking a wholesale-to-retail markup. Returns: percentile_rank (string, e.g. "72nd percentile"), price_index_label (ratio vs. category median), position (Value / Parity / Premium), category (resolved name), last_refreshed (ISO timestamp), cta (link to full per-SKU report). Args: price: Product price in dollars (e.g. 4.99). Must be > 0 and <= 10000. category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive. Call list_categories first to confirm available names.
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  • Generates a browser authorization URL for connecting a new social account to a project. This endpoint is useful for multi-user integrations where your application lets your own users, clients, or brands connect their social accounts to WoopSocial without giving them access to your WoopSocial account. A common flow is: 1. Create or select a WoopSocial project for your user, client, or brand. 2. Call this endpoint from your backend with that `projectId`, the target `platform`, and a `redirectUrl` in your application. 3. Open the returned `url` in your user's browser. 4. After OAuth completes, WoopSocial redirects the browser back to `redirectUrl` with result query parameters. 5. Use `projectId` and `socialAccountIds` from the redirect, or call `GET /social-accounts?projectId=...`, to store or confirm the connected account in your application. When `redirectUrl` is provided, the browser is redirected back to that URL after the OAuth callback is handled. On success, WoopSocial appends these query parameters to `redirectUrl`: - `status=success` - `projectId`: the project identifier from the request - `platform`: the connected social platform - `socialAccountIds`: comma-separated connected social account identifiers. This may contain one or more IDs depending on the platform OAuth flow. On failure, WoopSocial appends these query parameters to `redirectUrl`: - `status=error` - `projectId`: the project identifier from the request - `platform`: the requested social platform - `error`: an OAuth callback error code If the OAuth callback state is missing or expired, WoopSocial cannot safely determine the original `redirectUrl`, so the callback returns an HTTP error instead of redirecting. The redirect never includes OAuth tokens or credentials.
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  • Create a relationship between two learnings. Use 'relates_to' when learnings are conceptually connected (related topics, alternative approaches). Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link both 'relates_to' each other • A learning mentions another as context → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.
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  • Create a new empty shopping cart. Requires authentication — call 'authenticate' with your sk_buy_* key first.
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  • Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other
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  • [BROWSE] List all active brands on the platform. Returns name, slug, headline, description, and product/brief counts. Use a brand slug with list_drops or list_briefs to filter by brand.
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