PILLAR MCP Server

PILLAR is the first Revenue Architecture Operating System (RAOS) with a native MCP (Model Context Protocol) server. Query your revenue architecture — account health, pipeline, signals, renewals, scoring, plays, tasks, financial cascade, market intelligence, AI-generated narratives, and governed writes — directly from any AI assistant.

Quick Start

1. Generate an API Key

Navigate to Settings > Integrations and scroll to the API Keys & MCP Server section. Click Generate API Key and save the key — it’s only shown once.

2. Configure Your AI Assistant

Add the following to your Claude Desktop, Cursor, or VS Code MCP configuration:

{
  "mcpServers": {
    "pillar": {
      "url": "https://app.pillargtm.com/api/mcp",
      "headers": {
        "Authorization": "Bearer pk_live_your_key_here"
      }
    }
  }
}

3. Start Querying

Ask your AI assistant natural language questions about your revenue data:

“What’s my pipeline summary?”
“Which accounts are at risk this quarter?”
“Show me critical signals and the top 3 save plays that have worked on similar accounts”
“Simulate what NRR going from 108% to 115% would mean for ARR and AE headcount”
“What’s the top expansion opportunity in the West territory right now?”
“Ask PILLAR: who’s at risk of churning next quarter and why?”

Endpoint

POST https://app.pillargtm.com/api/mcp
GET  https://app.pillargtm.com/api/mcp  (server discovery)

Authentication: Bearer API key in the Authorization header.

Available Tools (129)

PILLAR’s MCP surface spans 129 tools across 14 categories, covering every layer of the revenue architecture. The vertical_intelligence category (63 tools live) is the competitive moat — it carries PILLAR’s canonical district + federal-program datasets that horizontal Revenue AI platforms structurally cannot answer.

Coverage as of May 2026: The vertical_intelligence category is backed by 51 jurisdictions (50 states + DC + federal) and 26 federal datasets (8 IPEDS components + 8 Higher Ed sources + 10 K-12 sources), exposed through 63 MCP tools. Per-district coverage is at 51/51 jurisdictions for assessment proficiency (5.03M cells across ~19,700 LEAs), cohort graduation (391k cells), accountability status (24k cells), and engagement/chronic absenteeism (104k cells) — the four priority district-grain tables. K-12 state funding allocations (NEW): 46 of 51 jurisdictions (90.2%), 114,699 per-LEA rows, $957B+ captured from primary state aid programs (CA LCFF, TX FSP, NY Foundation Aid, IL EBF, FL Net State FEFP, OH Net State Funding, WA BEA, MI Bulletin 1014, etc.).** **HE state aid (NEW): 7,876 per-institution rows across 58 jurisdictions,$ 7.94B captured — IPEDS SFA per-institution state grant aid (3,669 institutions FY22-23 + 3,693 FY21-22) plus 11 state-specific programs (TX TEXAS Grant, CA Strong Workforce, 9 MI scholarship/grant programs). Per-state DOE deep ingest covers 27+ states at the recent-year grain; federal EDFacts SY 2020-21 backfill closes the long tail to 51/51 for the priority surfaces. The schema, ingest pipeline, MCP wrappers, and 550+ build-time-enforced Guarantees are runtime-truth — every commit blocks merge unless the canonical-shape validators (G-X-31 through G-X-40) accept every row landing in the 26 federal-data + 47 state-funding tables. All Round 8 tools (Scorecard Field-of-Study, FSA CDR/GE/HCM/distress-score, SHEEO SHEF, NC-SARA, Carnegie 2025, IPEDS HR/ADM/AY/AL/EF-CIP, CCD School Universe, EDGE Locale, CRDC ×2, OSEP IDEA-B, McKinney-Vento, Title III, Migrant Ed, Perkins V, NSLP-CEP, NIEER) are live and MCP-callable today.

The list below is representative — the source of truth is the tool catalog at src/lib/mcp/tool-catalog.ts, and new tools ship continuously as vertical-intelligence surfaces (K-12 state-calendar procurement windows, federal Title program eligibility, cooperative-contract lookups, NCES district enrichment, state DOE assessment + accountability + graduation) come online.

Tier 0 — Core Surface

The foundational tool set that shipped with the original MCP server. Still the most-invoked tier for day-to-day agent workflows.

Core Intelligence

Tool	Description
`get_dashboard`	Full GTM health snapshot: ARR, pipeline, NRR, at-risk accounts, signal count, forecast health
`get_pipeline_summary`	Pipeline totals: open, commit, best-case, weighted pipeline, NRR, ARR
`search_accounts`	Search accounts by name, segment, or territory with health/risk/priority scores
`get_account_360`	Full account detail: scores, contacts, signals, opportunities, district intelligence
`get_active_signals`	Active signals filtered by severity (CRITICAL/WARNING/INFO) or family (RENEWAL/PIPELINE/EXPANSION/ACCOUNT/COVERAGE)
`get_account_health`	Health, risk, and priority scores with scoring decomposition showing which factors drive each score
`get_renewal_risk`	Upcoming renewals with risk scores, filtered by days out (30/60/90)

Operational

Tool	Description
`get_forecast`	Quarterly forecast: won, commit, probable, upside with override amounts and rep-level detail
`get_revenue_bowtie`	Full acquire → close → retain/expand funnel with conversion rates at each stage
`get_territory_economics`	Territory P&L: revenue, costs, yield ratios, health classification per territory
`get_leads`	Lead funnel with ICP fit scores, behavioral scores, and funnel stage. Filter by status or score threshold
`acknowledge_signal`	Update a signal’s status (ACKNOWLEDGED, IN_PROGRESS, RESOLVED). Write.
`create_play`	Create an intervention play for an account (SAVE_PLAY, EXPANSION_PLAY, ONBOARDING_PLAY). Write.
`complete_play`	Complete a play with mandatory outcome (RENEWED, CHURNED, EXPANDED, LOST) and auto-update linked renewal. Write.
`get_board_report`	Executive summary for board meetings: ARR, NRR, GRR, pipeline coverage, risk distribution, EBITDA
`get_scoring_rules`	Scoring rule summary with weights and categories

Data Readiness

Tool	Description
`get_data_readiness`	Check if CRM data is ready for scoring: readiness level (ready/degraded/blocked), scoring-ready account count, blocking issues
`get_affected_records`	Identify specific accounts with data quality issues. Filter by issue code (no_contacts, no_recent_activity, missing_segment, zero_arr, score_imbalance, negative_arr)

Flywheel & Benchmarks

Tool	Description
`get_calibration_stats`	Flywheel scoring calibration dashboard: outcome distribution, weight drift from baseline, top rules by calibration impact
`get_calibration_history`	Scoring weight calibration history: pending feedback count, current weights, and chronological calibration event log
`get_benchmark_percentiles`	Blueprint benchmark percentile rankings: pillar scores and operational metrics ranked against anonymized peer cohort
`list_benchmark_cohorts`	List available benchmark cohorts with member counts and snapshot data. Cohorts group orgs by industry, revenue range, and maturity
`get_benchmark_opt_in_status`	Check whether the organization is opted in to anonymized benchmarking and which cohort is assigned

Connector Observability

Tool	Description
`list_connectors`	List all configured data connectors across CRM, email/calendar, product usage, and support ticketing with per-provider status, last sync, and health
`get_sync_log`	Recent connector sync runs from the unified sync log. Shows successes, failures, records synced, and timing. Filter by connector name
`get_account_data_sources`	Per-account breakdown of which data sources contributed to its score: CRM, product analytics, support tickets, or contracts provenance

Tier A — Plays, Tasks, Expansion (8 tools)

Plays Intelligence

Tool	Description
`list_plays`	List plays in flight. Filter by account, state (PENDING, ACTIVE, COMPLETED, DISMISSED), or owner. Answers “what’s already in flight?”
`get_play_effectiveness`	Template-level win rate, ARR impact, health delta, and which templates are underperforming. Filterable by segment/tier/territory
`get_play_recommendations`	System-suggested plays per account based on signal patterns, scoring, and historical effectiveness

Tasks & Activities

Tool	Description
`list_tasks`	Platform task list with filters (assignee, status, account, source, priority). Includes summary counts and overdue flag
`get_activity_timeline`	Email, call, meeting, task, and play event timeline. Filter by account, user, or date range

Expansion Whitespace

Tool	Description
`get_expansion_summary`	Org-wide expansion whitespace: total addressable expansion ARR, top candidate accounts, product-level whitespace distribution
`get_expansion_signals`	Layer-I signals tagged as expansion triggers (adoption surge, champion promoted, new use case detected)
`get_expansion_affinity`	Product co-adoption matrix and cross-sell probability per product pair

Tier B — Financial Cascade (9 tools)

Tool	Description
`simulate_nrr_impact`	Simulate ARR / headcount / ROI impact of moving NRR from current to target. Returns AE and CSM-equivalent headcount, retain-vs-acquire cost comparison
`get_revenue_impact`	ARR-at-stake analysis across renewal window, expansion opportunity value, churn exposure, net revenue position
`get_budget_variance`	GTM budget variance vs plan per period (rep cost, marketing spend, CS cost)
`get_procurement_calendar`	Procurement windows by account: fiscal year alignment, renewal overlap. Filterable by territory/segment/upcoming
`get_procurement_forecast`	Procurement-window-aware forecast (different from stage-based `get_forecast`)
`get_cohort_curves`	Customer retention + NRR cohort curves by acquisition period. Investor-grade
`list_cohort_definitions`	Available cohort slicing definitions (acquisition quarter, segment, deal size, territory)
`get_forecasted_renewals`	ML-forecasted renewal outcomes combining base rates + current risk scores
`get_customer_readiness`	Implementation and adoption readiness: onboarded, stuck, milestone status, time-to-value risk

Tier C — Market Intelligence (5 tools)

Tool	Description
`get_tam_sam`	TAM / SAM / SOM sizing by vertical, filterable by fiscal year
`get_territory_equity`	Territory fairness analysis: ARR balance, account distribution, quota-to-pipeline ratios
`simulate_headcount_change`	Project ARR uplift, ramp lag, cost impact, and breakeven quarter of adding/removing reps
`get_district_intelligence`	K-12 district enrichment (enrollment, demographics, bond measures, superintendent changes) via Starbridge
`get_data_health`	Org-wide data health dashboard: freshness lag, orphan counts, duplicate counts, pipeline blockers

Tier D — AI Orchestration (7 tools)

Tool	Description
`ask_pillar`	Ask PILLAR’s native RAG layer a question. Returns grounded answer with citations from CRM + signals + scoring
`list_narratives`	List cached AI-generated narratives (board briefings, forecast commentary, account deep-dives)
`get_narrative`	Full prose body + citations for a specific narrative
`generate_action_plan`	AI-authored per-account action plan: owner assignments, sequenced tasks, expected outcomes, SLAs
`generate_account_intelligence`	Multi-source account brief (CRM + signals + scoring + district intel + contact web)
`generate_coaching_insights`	Rep-level forecast accuracy, deal-velocity patterns, stuck-deal flags, 1:1 talking points
`generate_board_narrative`	Board-grade prose from the current dashboard data

Tier E — Scoring Transparency (5 tools)

Tool	Description
`get_scoring_backtest`	Historical accuracy of scoring models: how well did risk/priority/expansion scores predict actual outcomes over 12 months
`get_scoring_profile`	Org’s customized scoring rule weights and active configuration
`get_forecast_weights`	Custom forecast probability weights per stage + category mappings
`get_contracts_config`	Contracts object configuration: date models, term lengths, expansion vs new-biz classification
`get_scoring_thresholds`	Org’s custom score-severity thresholds (CRITICAL vs WARNING vs INFO per model)

Tier F — Governed Writes (5 tools)

Tool	Description
`create_task`	Create a platform task. Source type distinguishes NBA / signal / play / MQL handoff / manual. Governance log entry automatic. Write.
`update_task`	Update status, assignee, priority, or due date. Write.
`update_signal_status`	Set signal to ACKNOWLEDGED / IN_PROGRESS / RESOLVED / SNOOZED / DISMISSED with optional reason. Write.
`set_renewal_disposition`	Set renewal disposition directly (RENEWED / CHURNED / EXPANDED / LOST / PENDING). Write.
`opt_in_benchmarks`	Opt the org in or out of anonymized peer-cohort benchmarking. Write.

Tier G — Vertical Intelligence (63 tools live)

The competitive moat. Horizontal revenue platforms (Salesforce, HubSpot, Gong, Clari) cannot ship these tools because they don’t maintain federal education datasets, accreditor calendars, per-state procurement timing, or canonicalized state DOE assessment data. PILLAR does — the Tier G surface answers questions the customer’s own procurement office and CFO operate in.

K-12 + HigherEd specific tools backed by maintained federal datasets (IPEDS — now including the 5 IPEDS-extension components Human Resources / Admissions / Academic Year Tuition / Academic Libraries / Enrollment by CIP — plus NCES CCD, EDFacts, F-33 district finance, College Scorecard institution + field-of-study, Carnegie Classification 2025, FSA Cohort Default Rates / Gainful Employment / NSLDS aggregate / HCM, SHEEO SHEF, NC-SARA, CRDC, OSEP IDEA Part B, McKinney-Vento, Title III ELA, Migrant Education, Perkins V CTE, NCES EDGE Locale Codes, NSLP/CEP, NIEER State of Preschool), accreditor cycles, state fiscal calendars, Title program formulas, and all 50 states + DC canonicalized into a unified per-LEA assessment / accountability / graduation surface (51/51 jurisdictions covered for assessment proficiency, cohort graduation, accountability status, and engagement/chronic absenteeism via per-state DOE deep ingest for 27+ states + federal EDFacts SY 2020-21 backfill closing the long tail). K-12 — State DOE academic outcomes (NCES LEAID-keyed, 51 jurisdictions unified)

Tool	Description
`get_district_assessment_proficiency`	Per-LEA proficiency cell (subject × grade × subgroup × year) across all 51 jurisdictions (27+ states via per-state DOE deep ingest at recent-year grain; federal EDFacts SY 2020-21 fills the remaining 24 jurisdictions to 51/51), normalized to a unified `pct_proficient_or_above` column with documented per-state policy footprint
`get_district_assessment_trend`	Year-over-year proficiency trend with `continuity_break` flags across assessment-family transitions (PARCC→MCAP, FSA→FAST, AIR→Cambium)
`get_district_assessment_benchmarks`	District proficiency vs state aggregates (P25 / median / P75) AND NAEP state + national, with mandatory `standards_comparability_note` whenever cross-family figures juxtapose
`get_district_assessment_subgroup_gaps`	Ranked gap-from-all-students per subgroup; suppression envelope preserved (suppressed cells appear with `gap_pct: null`, never silently dropped)
`get_district_accountability_status`	Canonical CSI / TSI / LRAP / REC / GS status with the `is_intervention_priority` boolean derived from a single source of truth
`get_district_graduation_metrics`	Multi-year-window graduation rates (4yr / 5yr / 6yr / 7yr) with explicit `extension_coverage` documenting which extensions are available
`get_district_engagement_metrics`	Chronic absenteeism, attendance, dropout per LEA × subgroup with worst-subgroup ranking
`get_district_advanced_coursework_metrics`	AP/IB/dual-credit enrollment + pass rates with `ZERO_PASSING_NOTE` flag for the “access without success” pattern
`get_district_ccmr_metrics`	College/Career/Military Readiness composites with explicit `CCMR_COMPOSITE_NOTE` warning that composites aren’t comparable across states
`get_district_growth_metrics`	YoY improvement scores with `GROWTH_VS_LEVEL_NOTE` to prevent conflating growth with absolute proficiency
`get_district_early_childhood_metrics`	Pre-K + early literacy with `coverage_scope_note` (frequently public-school-enrolled only)
`get_district_graduation_pathway_metrics`	TX-specific graduation pathway codes with state-attribution preservation (no silent cross-state collapse)
`get_district_refusal_rates`	NY 3-8 assessment opt-out rates per LEA × subject × subgroup
`get_state_naep_comparison`	State assessment trend vs NAEP trend for the 11 Tier-1 states; surfaces `naep_disagrees: true` when state cut-score recalibrations diverge from federal benchmark
`get_ingestion_health`	Per-state ingest freshness + last-publish lag

HigherEd — IPEDS institutional intelligence (UNITID-keyed)

Tool	Description
`get_ipeds_enrollment_trend`	10-year enrollment trend for a higher-ed institution (by IPEDS UNITID): total + FTE headcount, Pell-eligible share, first-generation share, trend direction, CAGR, and a computed enrollment-cliff risk score (0-100).
`score_institution_tuition_dependency`	Tuition-and-fees / total-revenue ratio over 5 years with dependency score. Higher = more exposed to enrollment-cliff → vendor ability-to-pay risk.
`get_pell_grant_institutional_share`	% of undergrads receiving Pell, total Pell dollars, 5-year trend, and federal-budget-sensitivity score. Higher Pell reliance = more exposure to federal appropriation risk.

K-12 — Federal Title dollars (NCES LEAID-keyed)

Tool	Description
`get_district_title_allocations`	Per-district federal Title dollars. Title I-A nationally via F-33 C14 (~15.6k districts × 5 FYs); Title III-A via F-33 C36 (~5.6k districts × 5 FYs); aggregate `OTHER-FED-RESTR` bucket via F-33 C25 (~15k districts × 5 FYs — bundles II-A / IV-A / V / VI / VII / VIII / CTE non-Perkins / ESSER); Titles II-A and IV-A as a 3-district sample (LAUSD / Chicago / NYC) from EDFacts. Every row carries `source`, `reporting_lag_months`, and the aggregate bucket has `is_aggregate: true` with `aggregate_includes` listed so DRAFTER can’t mistake it for a specific Title.

Public-sector — Procurement calendars + compliance

Tool	Description
`get_fiscal_year_procurement_windows`	When a given state’s K-12 districts open and close their buying windows — and which months are off-limits for procurement conversations.
`get_state_higher_ed_budget_cycle`	A state’s HigherEd budget calendar: legislative session windows, governor sign-off month, earliest disbursement month.
`get_federal_title_programs`	Catalog of ESEA / ESSA Title programs (I-A, I-B, I-C, I-D, II-A, III-A, IV-A, IV-B, V, VI, VII, VIII) with FY2025 federal appropriations, ESSA evidence-tier requirements, and product-category fit for EdTech vendors.
`get_accreditation_review_cycle`	A higher-ed institution’s next accreditation self-study, site visit, and decision dates across 8 regional + national accreditors (SACSCOC, HLC, MSCHE, NECHE, NWCCU, WSCUC, ACCJC, DEAC).
`score_cooperative_contract_eligibility`	Whether a target district / institution can buy through a named cooperative (NASPO ValuePoint, TIPS, Sourcewell, CRC, OMNIA Partners, E&I, MHEC, PEPPM, etc.) — turns a 3-month procurement cycle into 30 days.

The canonicalization claim

PILLAR canonicalizes 51 jurisdictions (50 state DOEs + DC + federal) with a documented policy footprint, a structural honesty layer, and sixteen independent layers of accuracy verification: Round 1-5 reconciliation layer (closes “is the state-DOE proficiency number right?”)

Macro-level reconciliation against state-published statewide aggregates with 24-state coverage (G-X-25)

Micro-level spot-checks against 17 hand-validated district fixtures across 13 states including the load-bearing LDOE R36→036 alias (G-X-26)

External NAEP trend-direction cross-validation for the 11 Tier-1 states with a live MCP route at /api/vertical/state-naep-comparison (G-X-27)

Silent-corruption canary on every ingested cell with queue-backed weekly review via the value_unknown_alarms table (G-X-28)

Federal Title pass-through reconciliation between EDFacts allocations and SEA-published disbursements (G-X-29)

Per-district Title allocation spot-checks closing the loop on “we know proficiency AND federal allocation are right for the same district” (G-X-30)

Round 8 federal-data canonical-shape layer (closes “is the federal-dataset row right?”)

IPEDS-extension shape discipline for Human Resources / Admissions / Academic Year Tuition / Academic Libraries / Enrollment by CIP — including biennial-even-year discipline on EF-CIP and pre-2014 collection_status discipline on Academic Libraries (G-X-31)

OPEID padding integrity on the institution_crosswalk join — the only authorized path between UNITID-keyed (IPEDS, Scorecard, Carnegie) and OPEID-keyed (FSA CDR/GE/NSLDS/HCM, NC-SARA) datasets (G-X-32)

College Scorecard shape validators on institution-level + field-of-study tables (G-X-33)

Carnegie 2025 four-dimension derivation discipline — is_r1/is_r2 MUST be derivable from research_activity_designation; SAEC eligibility flag MUST be true when SAEC classification is present (G-X-34)

FSA regulatory-status discipline preventing accidental publish-rate inference during the 2019-2023 GE rescission gap; CDR status enum + HCM level enum locked (G-X-35)

SHEEO SHEF + NC-SARA state-level shape with USPS-keyed JSONB integrity (G-X-36)

CRDC biennial discipline — collection year MUST be even; suspensions ≤ 2× total enrollment sanity check (G-X-37)

CCD School Universe + EDGE locale enum — title_i_status, charter_status, magnet_status, virtual_indicator, locale_code all locked to documented value sets (G-X-38)

OSEP IDEA Part B + K-12 federal program state-aggregate shape (G-X-39)

NCES EDGE entity-type-conditional ID-length (school=12 / lea=7-10 / postsecondary=1-6 digits) + NIEER 0-10 quality benchmark hard cap (G-X-40)

Each verification layer is a build-time-enforced Guarantee with a cited spec entry — see The Guarantee → Vertical Intelligence (X) for the full structural enforcement chain. Runtime-truth status (April 2026): 550 Guarantee tests pass on every commit; 0 route type errors; 26 federal datasets ingested with 890,000+ canonical rows ready for live upsert across 35 ingest scripts.

Why this differentiates PILLAR

State DOEs each express proficiency on a different scale, suppression with different sentinels, accountability in 4-tier vs 5-tier vs A-F, with subgroup labels that vary across all 51 jurisdictions. Each state DOE essentially publishes data that’s only legible inside its own bureaucracy. Without the canonicalization layer, a query like “show me districts with declining ELA proficiency under 50%, chronic absenteeism above 20%, and accountability rating in the bottom two tiers” is structurally impossible across state lines — every comparison would require state-specific knowledge of cut-scores, sentinels, and subgroup mappings. PILLAR’s vertical intelligence MCP layer makes that query a single tool call that runs in milliseconds and returns the unified answer with the comparability caveats baked in. Provenance + honest coverage limits. Every Tier G response carries row-level source strings and a response-level data_provenance section documenting reporting lag and known gaps. For example, get_district_title_allocations discloses the three dead-end paths that prevent national per-LEA II-A / IV-A coverage (the Q1 2026 ed.gov site reorg deleted the per-state workbooks; Internet Archive has zero XLSX snapshots of the ed.gov Title paths; USAspending.gov records only state-level primary awards and double-counts carryover obligations). Similarly, the per-state assessment ingestion documents per-state policy footprints (TN “Approached/Met/Exceeded” vs LA “Mastery and above” vs WI “Advanced+Meeting”) and surfaces continuity_break flags whenever year-over-year comparison crosses an assessment-family transition. Consumers should never guess at data availability — the route tells them what it has and what it doesn’t.

Compatible AI Assistants

PILLAR’s MCP server works with any MCP-compatible client:

Claude Desktop — Anthropic’s AI assistant
Claude Code — CLI development assistant
Cursor — AI-first code editor
VS Code — GitHub Copilot with MCP support
ChatGPT Desktop — OpenAI’s assistant
Windsurf — Codeium’s AI editor
Zed — High-performance editor with MCP

Security

API keys are stored as SHA-256 hashes — the raw key is never persisted
Each key is scoped to a single organization via Row-Level Security
Keys can be revoked instantly from Settings > Integrations
All data is org-scoped — you can only query your own organization’s data
MCP server inherits PILLAR’s multi-tenant isolation
All tools are defined in a single source-of-truth catalog at src/lib/mcp/tool-catalog.ts which both the external MCP server and the in-app Drafter runtime consume. No drift between surfaces.

Example Conversations

CRO Morning Briefing:

“What does my GTM health look like this morning?” Claude calls get_dashboard → returns ARR, pipeline, at-risk accounts, signal count, forecast health

Renewal Review with NRR simulation:

“Which renewals are at risk in the next 30 days — and what would saving all of them do to NRR?” Claude calls get_renewal_risk with days_out: 30 → totals at-risk ARR → calls simulate_nrr_impact with current and projected NRR to show ARR uplift + AE-equivalent headcount

Account Deep Dive with Effectiveness:

“Tell me about Houston ISD and recommend the best save play template for a district like this” Claude calls search_accounts → get_account_360 → get_play_effectiveness filtered to the account’s segment/tier → recommends the highest-win-rate template with evidence

Signal Triage with Task Creation:

“Show me critical signals, acknowledge the top one, and create a follow-up task for the account owner” Claude calls get_active_signals with severity: "CRITICAL" → update_signal_status → create_task with source_type: “signal”

Procurement-aware Forecast:

“What’s our forecast by procurement window instead of CRM stage?” Claude calls get_procurement_forecast → explains the difference vs stage-based forecast → flags which deals are likely to slip based on procurement alignment

Board Briefing Generation:

“Generate a board-grade narrative for Tuesday’s meeting and pull the cohort retention curves to go with it” Claude calls generate_board_narrative → get_cohort_curves → composes the pack

Scoring Credibility Check:

“How accurate has PILLAR’s renewal risk score been historically?” Claude calls get_scoring_backtest → reports hit rate, false positives, and calibration drift

Connector Health Check:

“Are all my data sources syncing correctly?” Claude calls list_connectors → get_sync_log for any connectors showing errors

Score Provenance:

“Where does the health score for Houston ISD come from?” Claude calls get_account_data_sources → returns which values came from CRM vs product analytics vs support tickets

Rate Limits

The MCP server inherits PILLAR’s standard API rate limits. For most tools, this is effectively unlimited for normal AI assistant usage patterns. If you encounter rate limiting, contact support.

​PILLAR MCP Server

​Quick Start

​1. Generate an API Key

​2. Configure Your AI Assistant

​3. Start Querying

​Endpoint

​Available Tools (129)

​Tier 0 — Core Surface

​Core Intelligence

​Operational

​Data Readiness

​Flywheel & Benchmarks

​Connector Observability

​Tier A — Plays, Tasks, Expansion (8 tools)

​Plays Intelligence

​Tasks & Activities

​Expansion Whitespace

​Tier B — Financial Cascade (9 tools)

​Tier C — Market Intelligence (5 tools)

​Tier D — AI Orchestration (7 tools)

​Tier E — Scoring Transparency (5 tools)

​Tier F — Governed Writes (5 tools)

​Tier G — Vertical Intelligence (63 tools live)

​The canonicalization claim

​Why this differentiates PILLAR

​Compatible AI Assistants

​Security

​Example Conversations

​Rate Limits

PILLAR MCP Server

Quick Start

1. Generate an API Key

2. Configure Your AI Assistant

3. Start Querying

Endpoint

Available Tools (129)

Tier 0 — Core Surface

Core Intelligence

Operational

Data Readiness

Flywheel & Benchmarks

Connector Observability

Tier A — Plays, Tasks, Expansion (8 tools)

Plays Intelligence

Tasks & Activities

Expansion Whitespace

Tier B — Financial Cascade (9 tools)

Tier C — Market Intelligence (5 tools)

Tier D — AI Orchestration (7 tools)

Tier E — Scoring Transparency (5 tools)

Tier F — Governed Writes (5 tools)

Tier G — Vertical Intelligence (63 tools live)

The canonicalization claim

Why this differentiates PILLAR

Compatible AI Assistants

Security

Example Conversations

Rate Limits