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.
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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. |
TDQS
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral context beyond that: ID variants are merged server-side by rule, ambiguous identities are surfaced unmerged in review_candidates, rep_contacts learning is 'observational, not causal', and full result includes per-customer ledgers with explanation traces. No contradiction with annotations.
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?
The description is long but every paragraph earns its place: core action, size limit and sibling routing, optional rep_contacts, and the identity-cleaning workflow are all present. Some instructions repeat the schema (e.g., 'do not pre-normalise'), but the repetition is intentional emphasis rather than filler.
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 tool with 16 parameters, nested objects, and an output schema, the description is unusually complete. It covers the pipeline, the boundary condition for the sibling tool, the identity-cleaning model the agent must understand, and where ambiguous cases surface. The output schema covers return values, so further return-format detail is unnecessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics for key parameters: rep_contacts learns uplift from data rather than assuming it, transaction customer_id must be sent exactly as sourced without pre-normalisation, customer_name is only for reporting labels, and identity_overrides is for judgement calls on review_candidates. This elevates it above baseline.
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 a specific verb and resource: 'Scores a customer book of 500 transaction rows OR FEWER into A/B/C/D tiers.' It names the exact models run and what is returned (per-customer ledger with explanation traces), and it clearly distinguishes itself from the sibling by the 500-row boundary.
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?
Explicit when-to-use guidance is present: '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.' It also tells agents not to pre-normalise identifiers and to use identity_overrides only after judging review_candidates, which prevents misuse.
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)ARead-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 |
TDQS
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool readOnly and non-destructive, but the description adds substantial behavioral context: rows never pass through as tokens, no customer data reaches the server, the script writes a result JSON, requires numpy, cleans identities itself, and prints merged variants for review. This is far beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every sentence carries operational weight. It front-loads the threshold, the deliverable, and the key warning, then systematically covers constraints, optional inputs, and output behavior. The repeated emphasis on running the script verbatim is justified given the likely failure mode.
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 tool that returns a script, the description covers prerequisites, execution environment, required input format, input-size rationale, optional input value, output behavior, and anti-patterns. The presence of an output schema means return values do not need to be inventoried again. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema for file_path (do not pre-clean, purchase timing required) and especially contacts_path (explains why it is valuable and when to ask for it). as_of and currency are only covered by the schema, so the description does not fully elevate to 5.
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 names a specific resource ('complete, runnable Python script'), a precise action ('scores the book into A/B/C/D tiers'), and a clear scope condition ('books LARGER than 500 transaction rows'). It also enumerates the methods, making the tool's purpose impossible to confuse with a generic scoring tool.
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?
The description gives explicit when-to-use guidance: books larger than 500 transaction rows. It also warns against pre-cleaning the file, instructs the agent to run the returned script verbatim, and specifies when to ask for contacts_path ('whenever the user mentions a CRM, a call log or a visit register'). This is strong, actionable routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
4 tool updates
- Changed
customer_tiering6 fields changed- added
Input schema / properties / identity_overridesAdded value: +{ + "additionalProperties": { + "type": "string" + }, + "description": "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.", + "type": "object" +} - changed
Input schema / properties / transactions / descriptionPrevious value: -"Raw purchase rows. Each item needs customer_id, date, and amount (> 0, finite)."New value: +"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." - changed
Input schema / properties / transactions / items / properties / customer_id / descriptionPrevious value: -"Stable customer identifier."New value: +"Customer identifier exactly as the source has it. Spelling, case, spacing and legal-suffix variants are merged server-side before scoring; do not normalise it yourself." - added
Input schema / properties / transactions / items / properties / customer_nameAdded value: +{ + "description": "Optional readable name for this account, when customer_id is a code. Used only to label the account in the result — grouping still happens on customer_id. Send it whenever the source has both, so the answer names accounts the way the user does.", + "type": "string" +} - added
Output schema / properties / identity_cleaningAdded value: +{ + "additionalProperties": true, + "description": "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.", + "type": "object" +} - changed
Output schema / requiredPrevious value: -[ - "status", - "decisions", - "customers", - "rep_queue" -]New value: +[ + "status", + "decisions", + "customers", + "rep_queue", + "identity_cleaning" +]
- Added
customer_tiering_get_engine - Removed
customer_tiering_get_prep_code - Removed
customer_tiering_score_stats
3 tool updates
- First observed
customer_tiering - First observed
customer_tiering_get_prep_code - First observed
customer_tiering_score_stats
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TDQS
A4.5/5.0
Disambiguation4/5
Both tools perform the same A/B/C/D tiering workflow, but the explicit 500-row threshold and direct-vs-script delivery make them distinguishable. An agent could still misselect if it ignores the size boundary, so it is not a perfect 5.
Naming Consistency3/5
Both names share the customer_tiering prefix and use snake_case, but one is a bare action while the other appends get_engine, describing delivery mechanism rather than a distinct domain operation. The pattern is readable but not fully consistent.
Tool Count3/5
Two tools is borderline for a server whose purpose sounds broader than its actual scope. The two-path design is justified, though, since direct row submission would be impractical for large books.
Completeness4/5
The pair covers the core tiering workflow: direct scoring, large-scale script generation, identity cleaning, optional contact uplift, and result outputs. Missing pieces like override management are handled through parameters rather than separate tools, so agents are not left at a dead end.