A
licenseA
qualityF
maintenanceRevenue intelligence MCP server: RFM analysis, 14.5-point ICP scoring, pipeline health scoring. Embeds Artefact Formula methodology. HubSpot integration.
7
Business Source 1.1
Customer Intelligence & Segmentation
by ai.saifs
Server Details
Analyze customer data to make segmentation and predict which customer to focus on for more sales.
Ownership verified
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Tool Definition Quality
A4.6/5.0
Tool DescriptionsA
Average 4.6/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a distinct, non-overlapping role: customer_tiering performs direct analysis, customer_tiering_get_prep_code generates prep code, and customer_tiering_score_stats validates and scores prepared data. No two tools could be confused for the same action.
Naming Consistency4/5
All tools share the customer_tiering prefix, but the first is a bare noun while the others follow a verb_noun pattern (get_prep_code, score_stats). Slight deviation from a strict verb_noun convention, but the prefix makes the family recognizable.
Tool Count5/5
With 3 tools, the set is tightly scoped to a single domain. Each tool earns its place: the main analysis tool, a code generator, and a validation/scoring tool. This is an ideal size for the stated purpose.
Completeness5/5
The tools cover both a direct path and an optimized sandbox path, with no obvious dead ends. The main tool handles all tiering and decision needs, while the auxiliary tools enable a cheaper alternative without missing functionality.
Available Tools
2 toolscustomer_tieringScore Customer Book (A/B/C/D)ARead-onlyInspect
Scores a customer book of 500 transaction rows OR FEWER into A/B/C/D tiers. Send the rows directly; this server runs the survival model (BG/NBD), spend model (Gamma-Gamma), tier migration, money layer and decision cards, and returns the full result including a per-customer ledger with explanation traces. For books LARGER than 500 rows use customer_tiering_get_engine instead — sending thousands of rows as tool arguments is slow and risks truncated JSON. Optionally accepts rep_contacts, which lets the money layer learn contact uplift from data rather than assuming it. Customer identifiers are CLEANED HERE before scoring: capitalisation, spacing, punctuation, legal-suffix and word-order variants (ACME PVT LTD / Acme Pvt. Ltd. / Acme Private Limited) are merged into one account by rule, so send the values exactly as they appear in the source and do not pre-normalise them. Anything that needs context instead of rules — 'Acme & Co' vs 'Acme Pvt Ltd', a name under two codes — comes back in identity_cleaning.review_candidates, unmerged, for you to judge from the surrounding rows and re-send via identity_overrides. Pass customer_name alongside a coded customer_id to have accounts reported by name.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | Analysis date YYYY-MM-DD. Defaults to latest transaction date. | |
| currency | No | ISO currency code for money display (e.g. INR, USD, EUR). No FX conversion. | USD |
| rep_contacts | No | Optional rep-activity log [{customer_id, date}, ...]. Builds touched vs untouched Markov matrices and learns contact uplift per tier (≥ 10 touched transitions). Observational, not causal. | |
| transactions | Yes | Purchase rows. Each item needs customer_id, date, and amount (> 0, finite). Send customer_id exactly as the source has it: capitalisation, spacing, punctuation, legal-suffix and word-order variants are merged here by rule. Do not pre-normalise. Add customer_name when the source has a name as well as a code. | |
| horizon_months | No | CLV projection horizon in months. | |
| currency_symbol | No | Override display symbol (e.g. ₹, $, R$). | |
| identity_overrides | No | Your judgement calls on the review_candidates a previous call reported: {raw or reported customer value -> the account it belongs to}. Spelling variants are merged automatically and need no entry here; use this only for groups the rules deliberately left separate, and only after reading the surrounding rows or asking the user. | |
| risk_period_months | No | Window over which neglect churn risk is assessed. | |
| include_diagnostics | No | If true, include MLE params and multi-start fit diagnostics. | |
| rep_queue_max_items | No | Max length of the daily SAVE/GROW/VERIFY rep queue. | |
| annual_discount_rate | No | Annual discount rate for CLV (e.g. 0.10 = 10%). | |
| assumed_upgrade_prob | No | Fallback P(upgrade | contacted). Used only when learned data from rep_contacts is insufficient for that tier. | |
| contact_effectiveness | No | Fallback fraction of at-risk churn recovered by one contact. Used only when rep_contacts is missing or too sparse for that tier. | |
| min_customers_for_model | No | Below this count, BG/NBD refuses to fit. | |
| rep_queue_min_save_revenue | No | Ignore SAVE candidates with lifetime revenue below this. | |
| min_repeat_buyers_for_model | No | Below this repeat-buyer count, BG/NBD refuses to fit. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| alerts | No | List from the upstream customer tiering API. |
| status | Yes | 1 = success, 0 = error |
| message | No | |
| metadata | No | Upstream customer tiering API payload. |
| customers | Yes | List from the upstream customer tiering API. |
| decisions | Yes | Ranked plain-language manager cards (CALL NOW / GROW / CHECK). Money, days, account names only — no P(alive) / CLV / RFM jargon. |
| rep_queue | Yes | |
| book_summary | No | Upstream customer tiering API payload. |
| book_exposure | No | |
| uplift_targets | No | List from the upstream customer tiering API. |
| migration_model | No | |
| identity_cleaning | Yes | What the server did to the customer identifiers before scoring: which spelling variants it merged by rule, which groups it deliberately left separate for YOU to rule on (review_candidates — read the rows and decide, then re-run with identity_overrides), how names were chosen for display, and the counts before and after. Always relay the merges and the open questions; the tiers depend on them. |
| money_assumptions | No | |
| data_quality_report | No | Upstream customer tiering API payload. |
Tool Definition Quality
A4.6/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark read-only, non-destructive, open-world. The description adds crucial behavioral context: required data sources, returned decision artifacts, 'Observational, not causal' caveat, and the warning 'Do not invent tiers.' No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
It's a long paragraph but front-loaded with purpose and rich with usable heuristics; no filler. Slightly dense, so 4 rather than 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 15-param tool with output schema, the description covers input expectations, outputs, alternative paths, and data-source compatibility without needing to detail return types.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and every param has a description. The tool description reinforces key requirements but does not add significant extra semantics beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'DEFAULT tool for sales customer-book questions' and enumerates concrete tasks (segment A/B/C/D, call lists, churn, CLV). It explicitly contrasts with sibling tools via the 'do NOT use this tool' path, making purpose distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides an explicit invoke-for list and an explicit when-not-to-use condition with alternative tool names, offering strong usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
customer_tiering_get_engineCustomer Tiering — Get Scoring Script (large books)Read-onlyInspect
For books LARGER than 500 transaction rows. Returns a complete, runnable Python script that scores the book into A/B/C/D tiers with survival modelling (BG/NBD), spend modelling (Gamma-Gamma), tier migration, a money layer and plain-language decision cards. Run it in your code sandbox against the user's transaction file. The rows never pass through you as tokens, so a 10,000-row book costs the same to run as a 600-row one. Needs numpy. Prints ranked decisions and headline figures; writes the full per-customer ledger to customer_tiering_result.json beside the input file. No customer data reaches this server on this path. SAVE AND RUN THE RETURNED SCRIPT VERBATIM — every block of it is required for the computation. Do not retype it from memory, shorten it, reformat it, split it up, or reimplement the maths with pandas/sklearn; only the PATH / AS_OF / CURRENCY / OUT / OVERRIDES / CONTACTS lines at the bottom may be edited. The script cleans customer identities itself before scoring — merging capitalisation and spelling variants by rule, printing what it merged, and listing the similar-but-unproven groups for you to rule on via OVERRIDES — so do not pre-clean the file or edit those rules. Optionally takes contacts_path, a log of rep calls or visits (customer_id + date only). It is not required and the book scores fine without it, but it is valuable: with it the money layer MEASURES what a contact is worth per tier from touched-vs-untouched tier transitions instead of assuming a flat rate, so ask for it whenever the user mentions a CRM, a call log or a visit register.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | Analysis date YYYY-MM-DD. Defaults to latest in file. | |
| currency | No | ISO currency code for display (INR, USD, EUR...). | USD |
| file_path | Yes | Path to the transaction file (CSV/TSV/JSON) inside your sandbox. The file needs one row per PURCHASE with three things: a customer identifier, a date, and an amount > 0. Column names are matched flexibly (customer_id / AccountId / Party Name; date / invoice_date / Invoice Date; amount / revenue / Invoice Amount), so most CRM and Excel exports work unchanged, including a separate name column used to label accounts when the id is a code. A pre-aggregated per-customer summary will NOT work — the models need purchase timing. The script cleans name variants itself ('Acme Pvt Ltd' and 'ACME PVT LTD' become one account), so pass the file as it is. | |
| contacts_path | No | Optional path to a rep-contact log (CSV/TSV/JSON) inside your sandbox: one row per call, visit or WhatsApp touch, needing only a customer identifier and a date — no amount. Column names are matched as leniently as the transaction file, and the ids go through the same identity cleaning, so a log spelling 'ACME PVT LTD.' still joins 'Acme Pvt Ltd'. Strictly optional; without it the book scores exactly as it would anyway. With it, what a contact is worth stops being an assumed flat rate and is MEASURED per tier by comparing tier transitions that followed a contact against those that did not (any tier with at least 10 touched transitions; thinner tiers keep the assumption). Worth asking for whenever the user mentions a CRM, a call log or a visit register. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| notes | No | |
| script | Yes | The runnable scoring script. |
| currency | No | |
| language | No | |
| requires | No | |
| writes_file | No | |
| instructions | Yes | |
| code_integrity | Yes | Rules for running the script: it must be saved and executed verbatim, which lines may be edited, why every block matters, and how to verify the copy is intact. |
| engine_version | Yes | |
| file_path_used | No | |
| scipy_required | No | |
| data_requirements | No | What the input file must contain: required fields and their accepted aliases, formats handled, minimum data volumes, and what will not work. |
| identity_cleaning | Yes | How the script cleans customer identities before scoring: which variants it merges by rule, which similar-but-unproven groups it leaves for the caller to judge and how to feed that decision back via OVERRIDES, how accounts get their display names, and what it writes out. |
| contacts_path_used | No | |
| network_access_required | No |
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