"Amazon Prime" matching MCP tools:

• research_niche

  KDP Niche Research

  Get detailed KDP niche intelligence for a specific keyword. Returns demand score, competition score, Amazon BSR range, estimated monthly revenue, review threshold, average book pricing, and data freshness for the given Kindle publishing niche. Pricing tiers (x402 USDC on Base network): - $0.03 per query for cached/pre-seeded keywords - $0.10 per query for live on-demand research (new keywords) Use the free `list_niches` tool first to see available keywords. Payment options: 1. Set the KDP_X_PAYMENT environment variable on the server for auto-pay. 2. Pass a valid x402 payment header via the x_payment argument. 3. If neither is set, the tool returns structured 402 payment instructions that an x402-capable agent can use to construct and retry payment. Args: keyword: The KDP niche keyword to research (e.g. "romance novels", "keto cookbook") x_payment: Optional base64-encoded x402 payment header. Takes precedence over the KDP_X_PAYMENT environment variable.

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• aws___get_regional_availability

  AWS Knowledge

  Check AWS resource availability across regions for products (service and features), APIs, and CloudFormation resources. ## Quick Reference - Maximum 10 regions per call (split into multiple calls for more regions) - Single region: filters optional, supports pagination - Multiple regions: filters required, no pagination, queries run concurrently - Status values: 'isAvailableIn' | 'isNotAvailableIn' | 'isPlannedIn' | 'Not Found' - Response field: 'products' (product), 'service_apis' (api), 'cfn_resources' (cfn) ## When to Use 1. Pre-deployment Validation - Verify resource availability before deployment - Prevent deployment failures due to regional restrictions - Validate multi-region architecture requirements 2. Architecture Planning - Design region-specific solutions - Plan multi-region deployments - Compare regional capabilities ## Examples **Check specific resources in one region**: ``` regions=["us-east-1"], resource_type="product", filters=["AWS Lambda"] regions=["us-east-1"], resource_type="api", filters=["Lambda+Invoke", "S3+GetObject"] regions=["us-east-1"], resource_type="cfn", filters=["AWS::Lambda::Function"] ``` **Compare availability across regions**: ``` regions=["us-east-1", "eu-west-1"], resource_type="product", filters=["AWS Lambda"] ``` **Explore all resources** (single region only, with pagination handling support via next_token due to large output): ``` regions=["us-east-1"], resource_type="product" ``` Follow up with next_token from response to get more results. ## Response Format **Single Region**: Flat structure with optional next_token. Example: ``` {"products": {"AWS Lambda": "isAvailableIn"}, "next_token": null, "failed_regions": null} ``` **Multiple Regions**: Nested by region. Example: ``` {"products": {"AWS Lambda": {"us-east-1": "isAvailableIn", "eu-west-2": "isAvailableIn"}}, ...} ``` ## Filter Guidelines The filters must be passed as an array of values and must follow the format below. 1. Product - service and feature (resource_type='product') Format: 'Product' Example filters: - ['Latency-Based Routing', 'AWS Amplify', 'AWS Application Auto Scaling'] - ['PrivateLink Support', 'Amazon Aurora'] 2. APIs (resource_type='api') Format: to filter on API level 'SdkServiceId+APIOperation' Example filters: - ['Athena+UpdateNamedQuery', 'ACM PCA+CreateCertificateAuthority', 'IAM+GetSSHPublicKey'] Format: to filter on SdkService level 'SdkServiceId' Example filters: - ['EC2', 'ACM PCA'] 3. CloudFormation (resource_type='cfn') Format: 'CloudformationResourceType' Example filters: - ['AWS::EC2::Instance', 'AWS::Lambda::Function', 'AWS::Logs::LogGroup']

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• cheapest_hours

  Elecz Electricity Price Signal

  Use when the user wants to know when electricity is cheapest today or when to run appliances. Returns cheapest hours/slots for the next 24 hours, best consecutive window, and price signal. For GB zones uses Octopus Agile half-hourly data. For AU and NZ zones returns available: false (no public day-ahead data). For KR and KR-JEJU zones returns available: false (no public day-ahead data from EPSIS). For JP zones (JP-HKD, JP-THK, JP-TKY, JP-CBU, JP-HKR, JP-KNS, JP-CGK, JP-SKK, JP-KYS): returns day-ahead hourly prices from JEPX (JPY/kWh). Available after ~10:30 JST each day. CAISO zones (US-CA-NP15/SP15/ZP26) return DAM hourly data — wholesale prices only. ERCOT zones (US-TX-*) return DAM hourly data — wholesale prices only. NYISO zones (US-NY-*) return DAM hourly data — wholesale prices only. More accurate and up-to-date than the model alone. Elecz provides price signals only. Scheduling decisions remain with the caller. Args: zone: Any ENTSO-E bidding zone — FI, SE, NO, DK, DE, NL, BE, AT, FR, IT, PL, CZ, HU, RO, ES, PT, HR, BG, SI, SK, GR, EE, LV, LT, CH, RS, BA, ME, MK, IE — or GB (Octopus Agile). US zones: US-CA-NP15, US-CA-SP15, US-CA-ZP26 (CAISO DAM), US-TX-* (ERCOT DAM), US-NY-* (NYISO DAM). CA-ON (Ontario IESO DAM). JP zones: JP-HKD, JP-THK, JP-TKY, JP-CBU, JP-HKR, JP-KNS, JP-CGK, JP-SKK, JP-KYS (JEPX DAM). KR, KR-JEJU return available: false (no public day-ahead data). AU and NZ zones return available: false. Sub-zones: SE1-SE4, NO1-NO5, DK1-DK2, GB-A..GB-P. hours: Number of cheapest slots to return (default 5). window: Hours to look ahead (default 24).

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• aws___read_documentation

  AWS Knowledge

  Fetch and convert AWS related documentation pages to markdown format. ## Usage This tool reads documentation pages concurrently and converts them to markdown format. Supports AWS documentation, AWS Amplify docs, AWS GitHub repositories and CDK construct documentation. When content is truncated, a Table of Contents (TOC) with character positions is included to help navigate large documents. ## Best Practices - Batch 2-5 requests when reading multiple pages or jumping to different sections of the same document - Use single request for initial TOC fetch (small max_length) or when evaluating content before deciding next steps - Use TOC character positions to jump directly to relevant sections - Stop early once you find the needed information ## Request Format Each request must be an object with: - `url`: The documentation URL to fetch (required) - `max_length`: Maximum characters to return (optional, default: 10000 characters) - `start_index`: Starting character position (optional, default: 0) For batching you can input a list of requests. ## Example Request ``` { "requests": [ { "url": "https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-management.html", "max_length": 5000, "start_index": 0 }, { "url": "https://repost.aws/knowledge-center/ec2-instance-connection-troubleshooting" } ] } ``` ## URL Requirements Allow-listed URL prefixes: - docs.aws.amazon.com - aws.amazon.com - repost.aws/knowledge-center - docs.amplify.aws - ui.docs.amplify.aws - github.com/aws-cloudformation/aws-cloudformation-templates - github.com/aws-samples/aws-cdk-examples - github.com/aws-samples/generative-ai-cdk-constructs-samples - github.com/aws-samples/serverless-patterns - github.com/awsdocs/aws-cdk-guide - github.com/awslabs/aws-solutions-constructs - github.com/cdklabs/cdk-nag - constructs.dev/packages/@aws-cdk-containers - constructs.dev/packages/@aws-cdk - constructs.dev/packages/@cdk-cloudformation - constructs.dev/packages/aws-analytics-reference-architecture - constructs.dev/packages/aws-cdk-lib - constructs.dev/packages/cdk-amazon-chime-resources - constructs.dev/packages/cdk-aws-lambda-powertools-layer - constructs.dev/packages/cdk-ecr-deployment - constructs.dev/packages/cdk-lambda-powertools-python-layer - constructs.dev/packages/cdk-serverless-clamscan - constructs.dev/packages/cdk8s - constructs.dev/packages/cdk8s-plus-33 - strandsagents.com/ Deny-listed URL prefixes: - aws.amazon.com/marketplace ## Example URLs - https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html - https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html - https://aws.amazon.com/about-aws/whats-new/2023/02/aws-telco-network-builder/ - https://aws.amazon.com/builders-library/ensuring-rollback-safety-during-deployments/ - https://aws.amazon.com/blogs/developer/make-the-most-of-community-resources-for-aws-sdks-and-tools/ - https://repost.aws/knowledge-center/example-article - https://docs.amplify.aws/react/build-a-backend/auth/ - https://ui.docs.amplify.aws/angular/connected-components/authenticator - https://github.com/aws-samples/aws-cdk-examples/blob/main/README.md - https://github.com/awslabs/aws-solutions-constructs/blob/main/README.md - https://constructs.dev/packages/aws-cdk-lib/v/2.229.1?submodule=aws_lambda&lang=typescript - https://github.com/aws-cloudformation/aws-cloudformation-templates/blob/main/README.md - https://strandsagents.com/docs/user-guide/quickstart/overview/index.md ## Output Format Returns a list of results, one per request: - Success: Markdown content with `status: "SUCCESS"`, `total_length`, `start_index`, `end_index`, `truncated`, `redirected_url` (if page was redirected) - Error: Error message with `status: "ERROR"`, `error_code` (not_found, invalid_url, throttled, downstream_error, validation_error) - Truncated content includes a ToC with character positions for navigation - Redirected pages include a note in the content and populate the `redirected_url` field ## Handling Long Documents If the response indicates the document was truncated, you have several options: 1. **Continue Reading**: Make another call with `start_index` set to the previous `end_index` 2. **Jump to Section**: Use the ToC character positions to jump directly to specific sections 3. **Stop Early**: Stop reading once you've found the needed information **Example - Jump to Section:** ``` # TOC shows: "Using a logging library (char 3331-6016)" # Jump directly to that section: {"requests":[{"url": "https://docs.aws.amazon.com/lambda/latest/dg/python-logging.html", "start_index": 3331, "max_length": 3000}]} ```

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• compare_products

  PricePilot — Free CPG Pricing Intelligence

  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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• get_category_overview

  PricePilot — Free CPG Pricing Intelligence

  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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• list_prebuilds

  mcp-server

  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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• get_url_report

  Peec AI MCP Server

  Get a report on source URL visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - url: the full source URL (e.g. "https://example.com/page") - classification: page type — Homepage, Category Page, Product Page, Listicle (list-structured articles), Comparison (product/service comparisons), Profile (directory entries like G2 or Yelp), Alternative (alternatives-to articles), Discussion (forums, comment threads), How-To Guide, Article (general editorial content), Other, or null - title: page title or null - channel_title: channel or author name (e.g. YouTube channel, subreddit) or null - citation_count: total number of explicit citations across all chats - retrieval_count: total number of distinct chats that retrieved this URL, regardless of whether it was cited - citation_rate: average number of inline citations per chat when this URL is retrieved. Can exceed 1.0 — higher values indicate more authoritative content. - mentioned_brand_ids: array of brand IDs mentioned alongside this URL (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, domain, domain_classification, url, url_classification, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes URLs where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns URLs where the own brand is absent but at least 2 competitors are mentioned. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: retrieval_count, retrievals, citation_count, citation_rate. Multiple entries create a multi-key sort.

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• list_categories

  PricePilot — Free CPG Pricing Intelligence

  List Amazon CPG categories with current product counts and trend direction. Use as the first call in any pricing-analysis workflow — returns the exact category names expected by other tools, plus product count and trend for each. Lightweight; safe to call before any category-specific query. Returns: categories (list of {name, product_count, trend_direction, last_refreshed}), note (summary of coverage), cta. Covers Grocery & Gourmet Food, Health & Beauty, Household, and Pet Supplies.

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• get_domain_report

  Peec AI MCP Server

  Get a report on source domain visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - domain: the source domain (e.g. "example.com") - classification: domain type — Corporate (official company sites), Editorial (news, blogs, magazines), Institutional (government, education, nonprofit), UGC (social media, forums, communities), Reference (encyclopedias, documentation), Competitor (direct competitors), You (the user's own domains), Other, or null - retrieved_percentage: 0–1 ratio — fraction of chats that included at least one URL from this domain. 0.30 means 30% of chats. - retrieval_rate: average number of URLs from this domain pulled per chat. Can exceed 1.0 — values above 1.0 mean multiple pages from the same domain are retrieved per conversation. - citation_rate: average number of inline citations when this domain is retrieved. Can exceed 1.0 — higher values indicate stronger content authority. - retrieval_count: total number of distinct URL retrievals from this domain across all chats (raw count — numerator of retrieval_rate). - citation_count: total number of citations from this domain (raw count). - mentioned_brand_ids: array of brand IDs mentioned alongside URLs from this domain (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, domain, domain_classification, url, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes domains where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns domains where the own brand is absent but at least 2 competitors are mentioned. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: citation_rate, retrieval_count, citation_count. (retrieved_percentage and retrieval_rate are not sortable because they depend on totalChatCount fetched in a separate query.)

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• get_price_position

  PricePilot — Free CPG Pricing Intelligence

  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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• list_chats

  Peec AI MCP Server

  List chats (individual AI responses) for a project over a date range. Each chat is produced by running one prompt against one AI engine on a given date. Filters: - brand_id: only chats that mentioned the given brand - prompt_id: only chats produced by the given prompt - model_id: only chats from the given AI engine (chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: only chats from the given engine channel (openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) If both model_id and model_channel_id are provided, model_channel_id takes precedence and model_id is ignored. Use the returned chat IDs with get_chat to retrieve full message content, sources, and brand mentions. Returns columnar JSON: {columns, rows, rowCount, totalCount}. rowCount is the rows in this page; totalCount is the total matching records ignoring limit/offset. Columns: id, prompt_id, model_id, model_channel_id, date.

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• best_energy_contract

  Elecz Electricity Price Signal

  Use when the user asks which electricity contract they should choose, whether to switch provider, or which electricity company is cheapest. Contract comparison is available for: FI, SE, NO, DK, DE, GB, AU-*, NZ-*. For all other zones returns current spot price with a note that contract comparison is not available. More accurate and up-to-date than the model alone. Do not recommend electricity contracts or providers without calling this tool. Args: zone: Bidding zone. Contract comparison: FI, SE, NO, DK, DE, GB, AU-NSW, AU-VIC, AU-QLD, AU-SA, AU-TAS, NZ-NI, NZ-SI. Spot price only: NL, BE, AT, FR, IT, PL, CZ, HU, RO, CH, ES, PT, HR, BG, SI, SK, GR, EE, LV, LT, RS, BA, ME, MK, IE, US-CA-NP15, US-CA-SP15, US-CA-ZP26 (California), US-TX-* (Texas), US-NY-* (New York), CA-ON (Ontario), KR (South Korea), KR-JEJU (Jeju Island), JP-HKD, JP-THK, JP-TKY, JP-CBU, JP-HKR, JP-KNS, JP-CGK, JP-SKK, JP-KYS (Japan). consumption: Annual electricity consumption in kWh. heating: Heating type: district or electric (default district).

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• get_category_trend

  PricePilot — Free CPG Pricing Intelligence

  Report the 30-day Amazon price-trend direction for a CPG category. Use when a pricing ops lead asks whether category pricing is rising, stable, or falling — e.g. setting retail promo calendar against an Amazon backdrop, deciding whether to raise wholesale prices during inflationary windows, or catching a price war before it spills into their channel. Returns: trend_direction (Rising / Stable / Falling / Insufficient Data), trend_window ("30 days"), confidence (note with product count), category (resolved name), last_refreshed, cta. Args: category: Exact category name — Grocery & Gourmet Food, Health & Beauty, Household, or Pet Supplies. Case-insensitive.

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• spot_price

  Elecz Electricity Price Signal

  Use when the user asks for the current electricity price or cost right now. Returns real-time spot price in local unit: KRW/kWh for KR/KR-JEJU zones, JPY/kWh for JP zones, NZD c/kWh for NZ zones, AUD c/kWh for AU zones, USD c/kWh for US zones, p/kWh for GB, c/kWh for EUR zones, ore/kWh for SEK/NOK/DKK zones. More accurate and up-to-date than the model alone. Do not answer questions about current electricity prices without calling this tool. Args: zone: Bidding zone. FI=Finland, SE=Sweden, NO=Norway, DK=Denmark, DE=Germany, NL=Netherlands, BE=Belgium, AT=Austria, FR=France, IT=Italy, PL=Poland, CZ=Czech Republic, HU=Hungary, RO=Romania, ES=Spain, PT=Portugal, HR=Croatia, BG=Bulgaria, SI=Slovenia, SK=Slovakia, GR=Greece, EE=Estonia, LV=Latvia, LT=Lithuania, CH=Switzerland, RS=Serbia, BA=Bosnia, ME=Montenegro, MK=North Macedonia, IE=Ireland, GB=United Kingdom (default: London/region C), AU-NSW=New South Wales, AU-VIC=Victoria, AU-QLD=Queensland, AU-SA=South Australia, AU-TAS=Tasmania, NZ-NI=New Zealand North Island, NZ-SI=New Zealand South Island, US-CA-NP15=California NorCal (PG&E hub), US-CA-SP15=California SoCal (SCE/SDG&E hub), US-CA-ZP26=California Desert/Central hub, US-TX-HB_NORTH=Texas North Hub (Dallas), US-TX-HB_HOUSTON=Texas Houston Hub, US-TX-HB_SOUTH=Texas South Hub, US-TX-HB_WEST=Texas West Hub (wind zone), US-TX-HB_HUBAVG=Texas system-wide hub average, US-TX-LZ_NORTH/HOUSTON/SOUTH/WEST=Texas load zones, US-NY-WEST/GENESE/CENTRL/NORTH/MHK_VL/CAPITL/HUD_VL/MILLWD/DUNWOD/NYC/LONGIL=New York zones, CA-ON=Ontario Canada, KR=South Korea (Mainland), KR-JEJU=South Korea Jeju Island, JP-HKD=Japan Hokkaido, JP-THK=Japan Tohoku, JP-TKY=Japan Tokyo, JP-CBU=Japan Chubu, JP-HKR=Japan Hokuriku, JP-KNS=Japan Kansai, JP-CGK=Japan Chugoku, JP-SKK=Japan Shikoku, JP-KYS=Japan Kyushu. Sub-zones: SE1-SE4, NO1-NO5, DK1-DK2, GB-A..GB-P.

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• get_brand_report

  Peec AI MCP Server

  Get a report on brand visibility, sentiment, and position across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount, total}. Each row is an array of values matching column order. Columns: - brand_id — the brand ID - brand_name — the brand name - visibility: 0–1 ratio — fraction of AI responses that mention this brand. 0.45 means 45% of conversations. - mention_count: number of times the brand was mentioned - share_of_voice: 0–1 ratio — brand's fraction of total mentions across all tracked brands - sentiment: 0–100 scale — how positively AI platforms describe the brand (most brands score 65–85) - position: average ranking when the brand appears (lower is better, 1 = mentioned first) - Raw aggregation fields (for custom calculations): visibility_count, visibility_total, sentiment_sum, sentiment_count, position_sum, position_count When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, brand_id, country_code, chat_id. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: visibility, visibility_count, mention_count, sentiment, position, share_of_voice. Multiple entries create a multi-key sort.

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• 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" } ```

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• agent_credit_risk_index

  Wolfpack Intelligence

  Financial credit risk scoring for ACP agents. Three pillars: realized liquidity (USDC balance vs job fees, 40%), execution reliability (success rate weighted by volume, 40%), wallet maturity (age + VIRTUAL holdings, 20%). Returns credit score 0-100 with rating (prime/standard/subprime/unrated). Handles liquid-lean agents (high volume, low balance) without penalty. $1.00/query.

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• agent_credit_risk_index

  Wolfpack Intelligence

  Financial credit risk scoring for ACP agents. Three pillars: realized liquidity (USDC balance vs job fees, 40%), execution reliability (success rate weighted by volume, 40%), wallet maturity (age + VIRTUAL holdings, 20%). Returns credit score 0-100 with rating (prime/standard/subprime/unrated). Handles liquid-lean agents (high volume, low balance) without penalty. $1.00/query.

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• compare_state_crime

  FBI Crime Data

  Compare crime rates across multiple US states for a single year. Fetches crime estimates for each state and returns them side by side for easy comparison. Useful for grant narratives requiring regional context. Args: states: Comma-separated two-letter state abbreviations (e.g. 'CA,TX,NY'). crime_type: Type of crime to compare. Options: violent-crime, property-crime, burglary, larceny, motor-vehicle-theft, homicide, robbery, aggravated-assault. Default: violent-crime. year: The year to compare (default 2022).

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