Productplan MCP Server vs Claude Plugin

whoami

Show the connected AIOProductOS identity (org, member) AND the org's products (id, name, is_primary). Read-only; returns the identity plus the product list. For a multi-product org, call this first to get the product ids, then pass one as `product_id` to any product-scoped tool; omit product_id to use the primary.

pm_meta

List the org's PM lists, statuses, members, and features as id+name pairs. Read-only; returns arrays for resolution only (list_features carries the richer catalogue). Call it to turn a name into an id before create_task / update_task — never guess an id.

get_product_brain

A grounded snapshot of the org's product so YOU can reason about it. Returns one JSON object with: revenue + top paying accounts (ranked by MRR), web + product analytics headline metrics, the feature list, recent verbatim customer signals (newest first), and open-work counts — each block empty when that source isn't flowing yet. The time-windowed sections (revenue, cost, web + product analytics, feature usage) honour `window` (7 | 30 | 90 days, default 30). Single call, no pagination. Start here to ground, then go deeper with the dedicated list_* reads and the analytics tools. Optional product_id (the org's primary product when omitted).

get_weekly_signal_memo

The Weekly Product Signal Memo — the last 7 days of customer signal clustered into themes (insights grouped by feature, ranked by the revenue behind them) with verbatim quotes, week-over-week deltas (new / repeated / stronger / weaker), concluded experiments, and shipped releases. Deterministic — every count is off real rows, no fabricated quotes. Optional `week` (ISO 'YYYY-Www') for a past week; `generate=1` rebuilds + persists the current week now. Read-only apart from that rebuild; returns the persisted memo, empty when the requested week has none. Open a weekly review with it, then drill into a theme with list_insights.

get_roadmap_drift

Planned vs shipped features over a window: a drift score (0-100, 100 = perfect alignment), counts (planned / shipped / on-time / slipped / unplanned / orphaned), median slip days, and the top slipped + unplanned ships. Deterministic, no LLM cost. window = week | month | quarter (default quarter); optional product_id. Read-only; returns the drift report, zeroed when nothing was planned or shipped in the window. Use it in planning reviews to check delivery against the roadmap, then open the slipped features with list_features.

get_customer_360

Everything about ONE customer, resolved by id, email, domain, or company name: profile, subscription + MRR, how many users sit under the account, and their verbatim feedback. Read-only; returns the matched account, or an empty result when nothing matches the query. The money + people + voice join on one record — call it before answering anything about a specific account.

analyze_nps

NPS for the product: the standard −100…100 score AND revenue-weighted NPS (each respondent weighted by their account MRR), plus detractor accounts ranked by MRR-at-risk (highest first). Surfaces when your biggest customers are the unhappy ones even if the headline looks fine. Computed deterministically off survey responses inside `window_days` (default 90, valid 1–365); returns an empty result when none fall in the window. product_id optional (primary product when omitted). Quantify sentiment after get_product_brain, then dig into a detractor with get_customer_360.

analyze_nrr

Net Revenue Retention (revenue-weighted) next to logo retention (count-weighted), the expansion/contraction/churn split, and the accounts that lost the most MRR (ranked, highest loss first). The divergence is the point: '92% of logos but 78% of revenue' means a big account churned. Computed deterministically off subscription movements inside `window_days` (default 90, valid 1–365); empty when none fall in the window. Quantify revenue health, then follow the top-losing accounts into get_customer_360.

analyze_funnel

Build a conversion funnel from the product's own events: distinct users per step, step-to-step conversion %, and drop-off, evaluated in the exact order you pass. Needs product-analytics events flowing; returns empty counts when none match. Pass `steps` as an ordered list of 2+ event names — call it with NO steps first to get the menu of available event names rather than guessing them. Optional product_id and window_days (default 30, valid 1–365). Pairs with analyze_paths to see where the drop-offs go.

get_retention

Weekly cohort retention for the product: users grouped by first-seen week (one row per cohort, newest last), with the share still active each subsequent week — a lower-triangular grid. Needs product-analytics events flowing; returns empty cohorts when the product has none. window_days default 56 = 8 weekly cohorts (min 7; roughly one extra cohort per added 7 days). product_id optional (primary product when omitted).

analyze_paths

Trace what users do AFTER a start event — the journey flow (Sankey) from the product's own events. Returns the next-step transitions ranked by user count (most common first), empty when no events match. Pass `start` to anchor on an event, or omit for the most common start (call analyze_funnel with no steps to list the event names). Optional product_id and window_days (default 30, valid 1–365).