MCP Analytics

Name: MCP Analytics
Author: MCP Analytics

AgentA2AMCP

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

by MCP Analyticsrepository ↗homepage ↗v1.0.5

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Evidence

Everything here was measured by our prober or read from a registry. Nothing is self-reported.

30-day availability (some checks stopped at authentication)203/209 checks ok

Some successful checks reached the service and some stopped at an authentication response; figures combine both.

when	check	result	http	latency	detail
4 h ago	mcp initialize	ok	200	476 ms	server MCP Analytics 1.0.7 · protocol 2024-11-05 · 27 tools
7 h ago	mcp initialize	ok	401	469 ms	auth required
10 h ago	mcp initialize	ok	200	518 ms	server MCP Analytics 1.0.7 · protocol 2024-11-05 · 27 tools
13 h ago	mcp initialize	ok	401	475 ms	auth required
17 h ago	mcp initialize	ok	200	463 ms	server MCP Analytics 1.0.7 · protocol 2024-11-05 · 27 tools
20 h ago	mcp initialize	ok	401	479 ms	auth required
24 h ago	mcp initialize	ok	200	496 ms	server MCP Analytics 1.0.7 · protocol 2024-11-05 · 27 tools
27 h ago	mcp initialize	ok	401	504 ms	auth required
30 h ago	mcp initialize	ok	200	521 ms	server MCP Analytics 1.0.7 · protocol 2024-11-05 · 27 tools
2 d ago	mcp initialize	ok	401	488 ms	auth required

Tools observed via MCP handshake

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 (Answer, Analytics, Custom Analytics), 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 uplo
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 an
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_libr
tools_schema — Get an analysis's parameter schema. ALWAYS call before path_tool_run.
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 r
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.
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
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. R
package_status — Read an analytics package back: status, every run under it, the report link once delivered.
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 delive
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 tem
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 aft
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.
path_resolver — Which path a question about data should take (reuse, rerun, answer, tool_run, estimate, bespoke, ask or none), with the reason, every path's score and time, and
path_reuse — Hand back the report already delivered for this question on this data. Time: as path_resolver quotes.
path_rerun — Run a delivered analysis again, on its own dataset or a new one: the same tools and mappings, no rebuild. Time: as path_resolver quotes.
path_answer — A read of the data (a total, an average, a count, the top group), computed on every row, in this response. When the question is not a read it says so and names
path_tool_run — Run one or more tested library tools on this dataset, with charts and a written report. No custom build. Time: as path_resolver quotes.
path_estimate — A rough answer over a sample and the layout of the full report, for the person to confirm before anything is ordered. Time: as path_resolver quotes.
path_bespoke — Order a custom analysis built for this data from a confirmed estimate, optionally with library tools alongside. Needs the person's yes and an idempotency key; a

Declared by sources

What the publisher says

Attributed to the source that supplied each field. Treated as claims, not facts.

No long description beyond the summary.

Declared 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 create_analysis; 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 create_analysis and run_analysis.
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 create_analysis.
tools schema — Get an analysis's parameter schema. ALWAYS call before run_analysis.
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 order_analytics_package.
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.

kind A2A Agent Card

name A2A Agent Card

repoUrl Official MCP Registry

summary A2A Agent Card

version A2A Agent Card

description A2A Agent Card

homepageUrl A2A Agent Card

publisherUrl A2A Agent Card

publisherName A2A Agent Card

Provenance

Where this record came from

Every source is kept verbatim. Field changes are logged as events.

Official MCP Registryai.mcpanalytics/analytics

first seen 2 mo ago · fetched 2 d agoopen ↗

A2A Agent Cardhttps://mcpanalytics.ai/.well-known/agent.json

first seen 5 d ago · fetched 5 d agoopen ↗

Field history (12 recent events)

5 d ago · declared · homepageUrl → "https://mcpanalytics.ai/docs/quickstart"
5 d ago · declared · description → "The statistical analyst in your AI chat: bring a dataset and a question, get back a citable, re-runnable report with it
5 d ago · declared · publisherUrl → "https://mcpanalytics.ai"
5 d ago · declared · kind → "agent"
5 d ago · declared · summary → "The statistical analyst in your AI chat: bring a dataset and a question, get back a citable, re-runnable report with it
5 d ago · declared · publisherName → "MCP Analytics"
5 d ago · declared · protocols → ["a2a","mcp"]
5 d ago · declared · version → "1.0.5"
5 d ago · declared · homepageUrl → "https://mcpanalytics.ai"
5 d ago · declared · name → "MCP Analytics"
5 d ago · declared · summary → "A statistical analyst in your AI chat: bring data and a question, get a citable, re-runnable report."
5 d ago · declared · description → "A statistical analyst in your AI chat: bring data and a question, get a citable, re-runnable report."

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Evidence

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