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These walkthroughs show the kinds of prompts that work well against the Alphacast MCP server. Each one uses several tools in sequence — your client (Claude Desktop, Cursor, etc.) decides the order based on the prompt, but the underlying tool calls are noted so you can debug if something looks off.

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Find inflation series and download them

“Find the latest Argentina inflation series in the Alphacast catalog, then download the most recent one as CSV.”
What the model does:
  1. search_catalog with query: "Argentina inflation" and asset: "datasets".
  2. Picks the top result and confirms with get_dataset to verify columns and date range.
  3. Calls download_dataset with format: "csv" to get the rows.
Tips:
  • Add excludeDeprecated: true to the search if you want only currently-maintained series.
  • If your model is going to summarize the data instead of save it, ask for format: "json" so it can iterate over rows directly.

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Profile a dataset before downloading

“Profile dataset 12345 and tell me if it’s worth downloading — what entities does it cover and how fresh is it?”
What the model does:
  1. get_dataset_profile with datasetId: 12345 and sampleRows: 5.
  2. Reports the entity breakdown, date range and frequency, total row count, and a small sample — without downloading the full dataset.
Tips:
  • Profiling is much faster than downloading when you just need to decide whether a dataset is relevant.
  • Combine with search_catalog to profile multiple search results in sequence before committing to a full download.

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Author a chart from an existing pipeline

“Show me how to configure a line chart for pipeline 4821, validate the config, and add it as a chart-data step.”
What the model does:
  1. get_pipeline with pipelineId: 4821 to see the step list and output columns.
  2. get_grapher_config_reference with chartType: "LineChart" to get the minimal config template and text length limits.
  3. Fills in the template using column names from the pipeline’s output.
  4. validate_grapher_config with pipelineId: 4821 and stepOrder to confirm the config is valid against the actual upstream data.
  5. add_pipeline_step to append a chart-data step with the validated config.
Tips:
  • Always validate before adding the step — the validator catches missing column references and invalid option values that would fail silently otherwise.
  • Use get_chart_details on an existing chart to clone its style rather than building from scratch.

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Catalog discovery for a research question

“I want to research how Brazil’s central bank rate compares to inflation. Find the relevant series across providers and tell me which combinations are available.”
What the model does:
  1. list_providers with query: "Brazil" to surface BCB, IBGE, etc.
  2. For each candidate provider: browse_provider to find the relevant categories.
  3. search_provider with focused keywords ("selic", "IPCA").
  4. get_series on the top candidates to confirm date range and frequency.
  5. Reports the combinations that align in frequency and date coverage.
Tips:
  • Use search_provider to narrow results before fetching full series data — it keeps the conversation fast and avoids token bloat.
  • If two providers carry the same series, prefer the one whose license field is most permissive for your use case.

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Compare a series across multiple providers

“Find unemployment rate series for the United States from FRED, BLS, and OECD. Pull a recent preview from each and tell me how they differ.”
What the model does:
  1. search_provider on fred with query: "unemployment rate", then on bls and oecd with similar queries.
  2. get_series on the top candidate from each provider.
  3. Compares frequency, seasonal adjustment, and date coverage across the three.
Tips:
  • The same indicator can have different methodology across providers (e.g., seasonally adjusted vs. raw, monthly vs. quarterly). The metadata returned by get_series calls this out — read it before treating the series as interchangeable.