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by KeenEthics
CE Cosmos Signal icon

CE Cosmos Signal

By Consumer Edge

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Overview

Cosmos brings Consumer Edge's transaction data directly into Claude. Ask questions in plain English and get analyst-grade answers built on near-real-time spend data from millions of US credit and debit cards, covering thousands of brands and merchants — weeks ahead of company reporting. Query aggregated spend metrics to track company trends, market share, and segment performance across the consumer landscape. Each question runs through Cosmos, Consumer Edge's AI analytics engine, which selects the right metrics, handles panel weighting and merchant hierarchies, and returns a structured summary with the underlying data. Results come back as queryable tables, so Claude can run follow-up analysis, calculate custom metrics, or build charts without re-querying. Key features: Natural language querying — no SQL or schema knowledge required; ask about brands, tickers, or merchant groups directly Full KPI coverage — spend, transactions, average ticket, unique shoppers, visit frequency, and new customer acquisition Demographic and cohort breakdowns — segment any brand's performance by customer income cohort Flexible time grains — daily, monthly, or quarterly trends, including quarter-to-date reads on partial periods Analyst-ready output — answers flag accelerations, decelerations, calendar shifts, and partial-month caveats automatically Use it to: Track brand performance ahead of earnings — monthly and quarter-to-date spend growth versus prior periods Run competitive benchmarking — compare KPIs and market share shifts across any set of brands Analyze customer behavior — see where new customer growth comes from and whether cohorts are trading down Spot inflection points — decompose month-over-month changes into customer count, frequency, and ticket drivers Requires a Consumer Edge subscription with MCP access enabled.

Tools

ask_cosmos_kappa_transact

ChatGPT
Query Consumer Edge Kappa Transact data using natural language. Answers questions about Consumer Edge Kappa Transact Panel. Returns a task_id — poll get_cosmos_result(task_id='<returned task_id>') every ~10s until ready. Question formatting rules: - Preserve exact time ranges, date constraints, and metrics from the user. Do NOT broaden scope (e.g. 'last 4 weeks' stays 'last 4 weeks'). - Preserve stock tickers verbatim (e.g. keep 'LULU', don't expand to 'Lululemon'). Cosmos resolves tickers itself. - Do NOT add analytical details the user didn't specify (comparison windows, aggregations, bucketing). Cosmos picks the right approach. Sessions: pass a session_id (from a previous get_cosmos_result) to continue a conversation. Only one query per session at a time — wait for it to finish before reusing the id. Omit session_id to start fresh.

ask_cosmos_orion

ChatGPT
Query Consumer Edge Orion data using natural language. Answers questions about Consumer Edge Orion 2022 Panel. Returns a task_id — poll get_cosmos_result(task_id='<returned task_id>') every ~10s until ready. Question formatting rules: - Preserve exact time ranges, date constraints, and metrics from the user. Do NOT broaden scope (e.g. 'last 4 weeks' stays 'last 4 weeks'). - Preserve stock tickers verbatim (e.g. keep 'LULU', don't expand to 'Lululemon'). Cosmos resolves tickers itself. - Orion merchants are a two-level hierarchy: a parent merchant is the owning company (e.g. 'The Gap', 'Yum! Brands, Inc.'); a child merchant is a banner under it (e.g. 'The Gap: Old Navy', 'Taco Bell'). The same name can exist at either level and the two give different numbers, so you MUST disambiguate. Explicitly say 'parent merchant X' or 'child merchant X' in the question; if the user is ambiguous, ask before calling this tool. - One child merchant can roll up to more than one parent (e.g. 'Kroger' appears under both 'Kroger' and 'Kroger Ex Fuel'). If a child-merchant query returns multiple parent rows, don't sum them — pick the parent matching the user's intent or surface both distinctly, and explain the split. - Do NOT add analytical details the user didn't specify (comparison windows, aggregations, bucketing). Cosmos picks the right approach. Sessions: pass a session_id (from a previous get_cosmos_result) to continue a conversation. Only one query per session at a time — wait for it to finish before reusing the id. Omit session_id to start fresh.

get_cosmos_result

ChatGPT
Poll for the result of a previously submitted Cosmos query. Call this after submitting a question with an ask_cosmos_<dataset> tool. Automatically waits up to 40 seconds for completion before responding, so you only need to poll every ~10 seconds. Returns the query result on success, a status update if still running, or an error message on failure.

get_cosmos_usage

ChatGPT
Report your organization's Cosmos usage against its limits. Returns, for each limited resource, the share of its limit drawn, how many units are left, and (when relevant) a suggested action if you are close to or over it.

query_cosmos_data

ChatGPT
Run a DuckDB SQL query against tabular data from a completed Cosmos query. Available after get_cosmos_result returns data tables. The result includes an "Available Data" section listing table names like data_0, data_1, etc. that this tool queries with standard SQL. Strongly preferred for any calculation, aggregation, or comparison on Cosmos results — averages, totals, growth rates, ranking. It computes exact values over the full returned rows, which is more reliable than estimating from the table preview.

ask_cosmos_kappa_transact

Claude

ask_cosmos_orion

Claude

get_cosmos_result

Claude

get_cosmos_usage

Claude

query_cosmos_data

Claude

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