MCP Analytics

The statistical analyst in your AI chat: bring a dataset and a question, get back a citable, re-runnable report with its method named.

Skills

• account link
  Direct link to the right account page for anything not doable in chat: billing, browser upload, report management. Hand the user the link and guide them.
• about
  Platform documentation and info: how it works, tiers, usage.
• agent advisor
  AI help desk: which analysis fits your question, interpreting results, fixing errors. Multi-turn.
• datasets upload
  Get your data in. Pass `data` as an array of row objects to create the dataset immediately and get a dataset_ref ready for path_resolver; omit it to get an upload link for a file only the user can reach. Add replace_ref (uuid://ID:KEY) with data to REFRESH an existing dataset in place; schedules and tools holding that reference read the new data on their next run.
• datasets list
  List and search your uploaded datasets, with fuzzy matching on name, description, and tags. Returns each dataset's uuid:// reference for use in path_resolver and path_tool_run.
• discover tools
  Browse the analyses you can run: the ones you commissioned plus the platform Standard Library (prebuilt tools; each result tagged source:'own' or 'standard_library'). Plain-language match; no query lists everything, your own first. Nothing fits? Commission it with path_resolver.
• tools schema
  Get an analysis's parameter schema. ALWAYS call before path_tool_run.
• run analysis
  Run an analysis on your data. Returns a shareable interactive report URL with statistics you can cite, re-run and share, and the method named.
• create analysis
  Commission a NEW analysis built for your question. tier is REQUIRED. The user picks. Easiest: fuzzy_request (plain language) + dataset_ref + tier. Snapshot = instant automated report (~2-10 min). JSON = a fast computed answer, numbers + method, re-runnable tool you own (~5 min). Brief = the computed answer on a one-page report: chart, numbers, method (~7 min). Deck = commissioned deep analysis, a durable re-runnable module you own (30-45 min). Failed builds are never billed.
• modify analysis
  Modify an EXISTING analysis into a new version: reword the question, swap the method, or add a variable. Pass tool_name + changes (plain language). Rebuilds on the analysis's own dataset by default; the original stays put. Returns pipeline tracking. Follow with build_status.
• request estimate
  START HERE for a new question: free, ~30 s. A rough answer over a sample plus the layout of the complete package, every place named with the question it will answer, and a page link. Then review_estimate with the user.
• review estimate
  The estimate as you review it WITH the user: the question as understood, the estimated answer (sample, marked), every place and its question, the page link, a review checklist. Before path_bespoke.
• adjust estimate
  Apply the user's layout wishes to the estimate's page through the layout agent; a new named arrangement, nothing overwritten, no number changes.
• answer now
  A read of the data (average, count, total, highest/lowest by group, a value in a month) answered in this response, in seconds. Not a read -> immediate=false with the reason; continue with decide_path.
• decide path
  Step 0 for a new question: which path answers it on this data. One record: route (reuse | answer | package | ask | none), a score with its reason for each of answer, package, ask and none, the compiled read plan when it is a read, the method family and the library's tool fit when it is a package, and the one question to ask when something is missing. Deterministic, read-only.
• find precedent
  Before estimating: how did we answer this objective before, on this data or any data? Prior packages and library runs with their tools, mappings, bespoke module names, method and verdicts. Platform-wide, read-only.
• check tool fit
  Before naming a library tool: does it fit THIS dataset for THIS question? Column mapping, missing required inputs, method-fit verdict, the places it delivers. Read-only.
• order analytics package
  Order what the estimate promised after reviewing it: library tools that fit, a bespoke build, or both, computed on the whole dataset; one reviewed page delivered. Credits per tool run; failed runs never billed.
• package status
  Read an analytics package back: status, every run under it, the report link once delivered.
• rerun package
  Run a delivered package again, on its own data or new data: the same tools, the same curated objects, the same layout, as a new package with its own link.
• my objects
  List and search the objects you own across every question: the curated charts, tables and figures of each delivered package, grouped by objective.
• build status
  Check a commissioned build in-chat: stage progress, queue position, rejection reason if the data didn't match the objective, honest ETA, report link when delivered.
• ask library
  Ask a question across all your delivered analyses: a synthesized answer with citations back to specific reports.
• reports list
  Your report library: every analysis delivered, with status and links. Pass semantic_query to search report content in plain language.
• warehouse
  Query your org's data warehouse free: browse the catalog (tables with column roles + computed metrics), semantically find data, plain-language ask, or named templates. Requires warehouse enablement (business plans).
• schedules
  Standing re-runs of analyses you own: action='create' (weekly/monthly against a re-runnable data reference, connector:// or an https:// link; report emailed after each run), 'list', or 'cancel'.
• reports view
  Get a shareable browser link for a report, viewable without authentication.
• report cards
  Browse a delivered report's individual cards (charts, tables, insights) inline in chat.

How to call

https://api.mcpanalytics.ai/auth0

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