Customer Retention LTV
PYPI · CUSTOMER-RETENTION-LTV-MCP · 2 COMPONENTS
Customer LTV, churn risk, repurchase, lifecycle email, and winback intelligence.
Last scanned (1 hour ago)
Scores higher than 61% of PyPI servers
Category CRM and Sales
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security94
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Declares code that runs automatically (build-time code we could not rule out (a Hatch, PDM or setuptools build hook, a dynamic version we could not read statically, shipped VCS metadata or a stray root module)) and that we could not recognise. It may be perfectly ordinary, but we have not established what it does. View diagnostics → Partial
- 1 of 22 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency6
- Repository check failed: no source repository is declared. See how to fix → View diagnostics → Fail
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Licence could not be checked: the licence is shipped as a file inside the package, not declared as a standard (SPDX) licence identifier, so we cannot tell whether it is OSI-approved.Unverified
- Actively maintained (last published 175 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability68
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 282 tokens (~25/item across 11 items; 11 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management67
- Stability observed for 20 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage66
- 91% of tools have a non-trivial description (not blank, and not just the tool's name).Partial
- 0% of tool parameters carry a description.Fail
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 11 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 11 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 4 Nov 26 −5
- Stability: pass → 0.63 functional
- 1 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 31 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 29 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- Stability: 0.97 → pass security
- 28 Oct 26 0
- Stability: pass → 0.97 functional
- 26 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 24 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 23 Oct 26 −15
- Malware scan: pass → unverified ▼ security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/customer-retention-ltv-mcp@0.1.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_hook | unreviewed | legacy backend root module |
Background: Why install scripts are a supply-chain risk →
Dependencies 22 packages
| Packages resolved | 22 |
|---|---|
| Stale | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
get_best_performing_segments Get Best Performing Segments ~20
Return highest-LTV customer profiles and traits.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
get_churn_risk_segment Get Churn Risk Segment ~32
Return customers most likely to lapse, grouped by lifecycle segment.
| Name | Type | Req | Description |
|---|---|---|---|
| risk_level | string | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_email_flow_performance Get Email Flow Performance ~30
Return lifecycle flow revenue per recipient and funnel drop-off.
| Name | Type | Req | Description |
|---|---|---|---|
| flow_name | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_license_status Get License Status ~27
Return payment/license readiness for AtulHritik-Mcp-Suite without exposing secrets.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
get_live_connector_status Get Live Connector Status ~18
Return retention connector readiness without exposing secrets.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
get_ltv_by_channel Get Ltv By Channel ~30
Return LTV and payback economics by acquisition source.
| Name | Type | Req | Description |
|---|---|---|---|
| period_days | integer | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
get_repurchase_rate_by_channel Get Repurchase Rate By Channel ~37
Return cohort repurchase rates by acquisition source.
| Name | Type | Req | Description |
|---|---|---|---|
| cohort_end | string | yes | – |
| cohort_start | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
health Health ~4
No description provided.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
test_klaviyo_connection Test Klaviyo Connection ~25
Run a read-only Klaviyo flows/profiles smoke check.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
test_shopify_retention_connection Test Shopify Retention Connection ~22
Run a read-only Shopify customer/order smoke check.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
trigger_winback_campaign Trigger Winback Campaign ~37
Safely stage a win-back campaign action for approval.
| Name | Type | Req | Description |
|---|---|---|---|
| flow_id | string | yes | – |
| segment_id | string | yes | – |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the Customer Retention LTV MCP server?
Customer Retention LTV runs locally as a PyPI package, launched with uvx customer-retention-ltv-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · customer-retention-ltv-mcp
claude mcp add atul0016-customer-retention-ltv -- uvx customer-retention-ltv-mcp
What is the Customer Retention LTV MCP server?
Customer Retention LTV is an MCP server listed in the public MCP registry as io.github.atul0016/customer-retention-ltv. Customer LTV, churn risk, repurchase, lifecycle email, and winback intelligence. This page covers its PyPI package (customer-retention-ltv-mcp).
Is the Customer Retention LTV MCP server safe to use?
Customer Retention LTV scores 66 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Customer Retention LTV MCP server expose?
Customer Retention LTV exposes 11 tools: health, get_license_status, get_live_connector_status, test_shopify_retention_connection, test_klaviyo_connection, and 6 more. Their descriptions and schemas cost roughly 282 tokens of context every time the server is loaded.
Is the Customer Retention LTV MCP server still maintained?
Customer Retention LTV is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.