Analyze and score leads to identify high-potential prospects and prioritize sales outreach.
Scored across 2 tools
The two tools have a clear size-based boundary: one directly scores pipelines of 500 leads or fewer, and the other returns a script for larger pipelines. There is no functional overlap that would cause an agent to select the wrong tool.
Both tools share the lead_pipeline prefix, but one is a bare noun while the other appends get_engine, so the naming convention is not fully uniform. The names are still readable and convey the relationship between the tools, but they do not follow a consistent verb_noun pattern.
With only two tools, the server feels thin, though the pair intentionally splits the domain by pipeline size. The count is borderline but defensible for a focused lead-scoring purpose.
The tool set fully covers the stated purpose: direct scoring for small pipelines and a local script path for large pipelines, with optional touches and stage_history support. No obvious scoring workflow gap remains for the intended domain.
Scores an open sales pipeline of 500 lead rows OR FEWER into conversion probabilities, money at risk / value of contact, funnel stage outlook, multi-touch Shapley attribution, and a daily CALL / NURTURE / VERIFY queue. Send the rows directly; this server scores them and returns the full result including the per-lead ledger. For pipelines LARGER than 500 leads use lead_pipeline_get_engine instead — sending thousands of rows as tool arguments is slow and risks truncated JSON. Needs lead rows (lead_id, created_date; optional stage, status, closed_date, deal_size, source) from CSV, Salesforce, HubSpot, or any CRM. Optional touches and stage_history improve uplift learning, Markov funnel, and attribution. Returns manager decisions (CALL TODAY / PUSH FORWARD / QUALIFY), call queue, pipeline exposure headline, per-lead money, and explanation traces. Do not invent scores — call this tool when lead data is available.
| Name | Required | Description | Default |
|---|---|---|---|
| as_of | No | Analysis date YYYY-MM-DD. | |
| leads | Yes | Raw lead rows. Each item needs lead_id and created_date. | |
| touches | No | Optional touch log [{lead_id, date, channel}, ...]. | |
| currency | No | ISO currency code for money display. No FX conversion. | USD |
| horizon_days | No | Window for P(convert) and expected days-to-convert. | |
| stage_history | No | Optional stage log [{lead_id, date, stage}, ...]. | |
| call_queue_size | No | Max length of the daily call queue. | |
| currency_symbol | No | Override display symbol (e.g. ₹, $). | |
| cold_threshold_days | No | Days of silence before a lead counts as going cold. | |
| include_diagnostics | No | If true, include Cox/logistic fit diagnostics. | |
| annual_discount_rate | No | Annual discount rate for pipeline value. | |
| min_closed_for_model | No | Below this, Cox / Shapley refuse to fit. | |
| shapley_max_channels | No | Top N−1 channels kept for exact Shapley. | |
| contact_effectiveness | No | Fallback uplift when learned data is too thin. | |
| min_conversions_for_model | No | Below this, Cox refuses. |
| Name | Required | Description |
|---|---|---|
| leads | Yes | List from the upstream lead pipeline API. |
| alerts | No | List from the upstream lead pipeline API. |
| status | Yes | 1 = success, 0 = error |
| message | No | |
| metadata | No | Upstream lead pipeline API payload. |
| decisions | Yes | Ranked plain-language manager cards (CALL TODAY / PUSH FORWARD / QUALIFY). Money, days, lead names only — no Cox / survival / Shapley jargon. |
| call_queue | Yes | |
| attribution | No | |
| funnel_model | No | |
| survival_model | No | |
| pipeline_summary | No | Upstream lead pipeline API payload. |
| money_assumptions | No | |
| pipeline_exposure | No | |
| data_quality_report | No | Upstream lead pipeline API payload. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, non-destructive behavior, and the description adds substantial context beyond that: the hard size limit and its rationale, the fact that rows are sent directly and scored server-side with the full result returned, the optional touches/stage_history improving model quality, and the 'do not invent scores' constraint. This gives the agent clear operational expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense and front-loaded with purpose, then size constraints, data requirements, and return outputs. It is longer than average but the tool is complex with 15 parameters; however, it slightly repeats the 500-lead limit across two sentences and could be trimmed without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (15 params) and the presence of an output schema, the description provides the essential context: data prerequisites, size limit with alternative tool, optional inputs that improve quality, and a summary of returned artifacts. It explains when not to use it and what behavior to expect, making it complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, so parameters are already well-documented. The description adds minor semantic value by naming the essential lead fields (lead_id, created_date) and noting that touches and stage_history improve attribution, but it largely repeats schema information. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb 'Scores' and names the exact resource ('open sales pipeline of 500 lead rows OR FEWER'), followed by the concrete outputs: conversion probabilities, money at risk, funnel stage outlook, Shapley attribution, and a CALL/NURTURE/VERIFY queue. It also distinguishes itself from the sibling lead_pipeline_get_engine by explicitly noting the size boundary for direct-row scoring.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: pipelines ≤500 leads. It names the alternative (`lead_pipeline_get_engine`) for larger pipelines and explains why (slow, truncated JSON). It also states prerequisites (lead rows with lead_id and created_date), compatible data sources, and an explicit instruction not to invent scores when data is unavailable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
For pipelines LARGER than 500 lead rows. Returns a complete, runnable Python script that loads the lead export in your sandbox and scores it locally (conversion probabilities, money layer, Shapley attribution, CALL / NURTURE / VERIFY queue). Makes no network calls — same shape as Customer Tiering — so Copilot Studio works even when outbound POST is blocked. The rows never pass through you as tokens. Needs numpy. Prints ranked decisions and headline figures; writes the full per-lead ledger to lead_pipeline_result.json. SAVE AND RUN THE RETURNED SCRIPT VERBATIM — do not retype, shorten, reformat, or reimplement it; only PATH / TOUCHES / STAGE_HISTORY / AS_OF / CURRENCY / OUT may be edited. Optionally takes touches_path and stage_history_path for engagement and funnel history.
| 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 lead export (CSV/TSV/JSON) inside your sandbox. Needs lead_id + created_date per row; optional stage, status, closed_date, deal_size, source. | |
| touches_path | No | Optional path to a touch / activity log (lead_id, date, optional channel). | |
| stage_history_path | No | Optional path to stage-change history (lead_id, date, stage). |
| 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 | |
| engine_version | Yes | |
| file_path_used | No | |
| scipy_required | No | |
| data_requirements | No | |
| touches_path_used | No | |
| network_access_required | No | |
| stage_history_path_used | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool makes no network calls, does not pass rows as tokens, requires numpy, writes to lead_pipeline_result.json, and must be run verbatim. This adds substantial context beyond the annotations (readOnlyHint=true, openWorldHint=true), covering side effects and operational requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than ideal, but each sentence carries important operational detail (size threshold, network behavior, verbatim instruction, file output). It is well-structured and front-loaded with the key use case, though could be tightened by removing the repetition about row tokens.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, the description is remarkably complete: it covers the purpose, output artifacts, constraints, dependencies, and exact usage instructions. The presence of an output schema means return values need no further explanation, and the description covers everything else an agent would need.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining which parameters are safe to edit (PATH, TOUCHES, STAGE_HISTORY, AS_OF, CURRENCY, OUT) and noting that touches_path and stage_history_path are optional for engagement and funnel history, reinforcing schema semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a runnable Python script for scoring large lead pipelines, with specific output (conversion probabilities, money layer, Shapley attribution, queue). It explicitly distinguishes itself from the sibling tool by targeting pipelines larger than 500 rows.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description opens with the explicit condition 'For pipelines LARGER than 500 lead rows,' and notes it is useful when outbound POST is blocked. This gives clear when-to-use guidance and implies the sibling handles smaller pipelines, though it doesn't name the sibling directly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
lead_pipelinelead_pipeline_get_engineB2B lead generation — prospect discovery, ICP scoring, outreach, and pipeline management.
Sales intelligence for B2B SMEs — lead scoring, ICP fit, CRM enrichment & writeback.
Score, rank and prioritize an existing Italian B2B lead list into explained JSON decisions.
AI sales — prospect discovery, ICP scoring, outreach generation.
Add one secure layer between your agents and this server.
