What can you do with this server?

Score companies against an ideal customer profile (ICP) and inspect available scoring templates. * Score a company domain with score_icp_fit, returning icp_score (0–100), icp_tier (A–D), lead_tag, per-signal breakdown, and optional explanation. * Define the ICP using a prebuilt template, custom JSON scoring_config, or plain-English icp_description with an LLM key/provider. * Optionally fetch hiring and tech-stack signals before scoring. * Provide enrichment/signal inputs such as headcount, industry, revenue, funding, social presence, jobs/keywords, CRM/sequencing tool usage, and tech stack. * Tune scoring with tier thresholds, funding recency, minimum score output, previous score comparison, and max spend cap. * List the six available prebuilt ICP templates with list_icp_templates locally, without an Apify run or charge. * Calls are read-only/idempotent but require APIFY_TOKEN and consume Apify credits.

Which integrations are available for this server?

Allows using OpenAI's API (e.g., GPT models) to interpret plain-English Ideal Customer Profile descriptions and convert them into scoring configurations.

mcp-icp-fit-scorer

by mambalabsdev

Overview Schema Source Related Servers Discussions Admin

TypeScript

Remote

ICP Fit Scorer MCP Server

Smithery Glama score MCP Registry npm version npm downloads license mcpservers.org

An MCP server that scores a company against your ideal customer profile. It wraps the Mamba Labs ICP Fit Scorer actor on Apify and returns a Clay-ready flat JSON row to any MCP client.

What's Inside

Related MCP server: Company Firmographic Enricher MCP Server

What it does

Give it a company domain and a definition of your ICP, and it scores the company on weighted signals, returning a 0 to 100 score, an A to D tier, and a per-signal breakdown. Define your ICP three ways: a prebuilt template, a JSON scoring config, or a plain-English description (which uses your own LLM key). Turn on fetch_signals and the actor will gather hiring and tech-stack signals for you before scoring. One flat row, ready for Clay, a CRM, or an AI agent workflow. All of the scoring runs on Apify. This package is a thin client that calls the actor and hands back the result.

Quick start

You need Node.js 18 or newer and an Apify account with an API token.

Add this to your Claude Desktop config:

{
  "mcpServers": {
    "mamba-icp-scorer": {
      "command": "npx",
      "args": ["-y", "@mambalabsdev/mcp-icp-fit-scorer"],
      "env": {
        "APIFY_TOKEN": "your-apify-token"
      }
    }
  }
}

Get your token at https://console.apify.com/account/integrations, paste it in, and restart Claude Desktop. The score_icp_fit and list_icp_templates tools will be available.

Prerequisites

Node.js 18 or newer
An Apify account with an API token

Example prompts

"Score clay.com against the b2b_saas template and fetch its signals."
"How well does stripe.com fit an ICP of mid-market fintech companies? Explain the score."
"Score figma.com with my scoring config and include the per-signal breakdown."
"Rate openai.com against this ICP description: enterprise AI teams hiring for go-to-market."

Inputs

Every input the tool accepts, generated from the server's own tool list.

`score_icp_fit`

Input	Type	Required	Description
`company_domain`	string	yes	The primary domain of the company to score. Example: clay.com
`company_name`	string	no	Optional display name of the company.
`template`	one of `full_signal`, `saas_outbound`, `b2b_services`, `fintech`, `smb_local`, `enterprise`	no	Name of a prebuilt scoring config, one of the six the actor ships: full_signal, saas_outbound, b2b_services, fintech, smb_local, enterprise. Alternative to scoring_config or icp_description. Call list_icp_templates to see them.
`scoring_config`	object	no	JSON object of scoring weights. Alternative to template or icp_description.
`icp_description`	string	no	Plain-English description of your target ICP. Requires llm_api_key. Alternative to template or scoring_config.
`llm_api_key`	string	no	Your OpenAI or Anthropic API key. Required only when using icp_description.
`llm_provider`	one of `openai`, `anthropic`	no	LLM provider to use with icp_description: openai or anthropic.
`fetch_signals`	boolean	no	If true, the actor fetches hiring and tech-stack signals for the company automatically before scoring.
`include_explanation`	boolean	no	If true, adds a score_explanation string to the output describing how the score was derived.
`tier_thresholds`	object	no	Optional. Minimum score for each tier as { "tier_a": number, "tier_b": number, "tier_c": number }. Scores at or above tier_a are A, tier_b are B, tier_c are C, else D. Defaults to 80 / 60 / 40.
`funded_within_days`	integer	no	Optional. How recent a funding round must be to count for the recently_funded signal, in days. Defaults to 540 (18 months).
`min_score_to_output`	integer	no	If set, rows scoring below this threshold are skipped from output (not pushed to dataset). Skipped rows are logged only.
`previous_score`	integer	no	Previous ICP score for this company. If provided, output includes score_change and score_trend fields.
`gtm_hiring_signal`	string	no	Whether the company is actively hiring for GTM/sales roles. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`gtm_role_count`	string	no	Number of open GTM/sales roles. Scores via the gtm_role_count_strong signal when at or above min_gtm_roles (default 2). Accepts a numeric string (e.g. "8"). Sent as a string for Clay compatibility and coerced to integer at runtime.
`uses_hubspot`	string	no	Whether the company uses HubSpot. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`uses_salesforce`	string	no	Whether the company uses Salesforce. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`uses_clay`	string	no	Whether the company uses Clay. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`crm_detected`	string	no	Whether any CRM was detected. Accepts a boolean-like string ("true"/"false") or any non-empty CRM name (e.g. "Salesforce"). Sent as a string for Clay compatibility and coerced to boolean at runtime. Auto-derived from uses_hubspot/uses_salesforce if not set.
`seq_tool_detected`	string	no	Whether a sales sequencing tool (Outreach, SalesLoft, Apollo, Lemlist) was detected. Accepts a boolean-like string ("true"/"false") or any non-empty tool name (e.g. "Outreach"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`tech_stack`	string	no	Comma-separated list of technologies. Used to auto-detect CRM/sequencing tools if booleans are not set.
`headcount`	string	no	Current employee headcount. Accepts a numeric string (e.g. "3000"). Sent as a string for Clay compatibility and coerced to integer at runtime.
`headcount_min`	integer	no	Minimum headcount for the headcount_in_range signal.
`headcount_max`	integer	no	Maximum headcount for the headcount_in_range signal.
`headcount_in_range`	boolean	no	Override: whether headcount is in your target range.
`employee_band`	string	no	Firmographic employee band from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g), e.g. "201-500". Scores via employee_band_match when it is in target_employee_bands.
`revenue_estimate`	string	no	Estimated annual revenue in dollars from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Scores via revenue_in_range. Accepts a numeric string (e.g. "50000000"). Coerced to integer at runtime.
`hq_location`	string	no	Headquarters location from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Carried for reference; not currently scored.
`founded_year`	string	no	Year the company was founded, from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Carried for reference; not currently scored. Accepts a numeric string (e.g. "2015").
`recently_funded`	boolean	no	Override: whether the company was recently funded (within funded_within_days, default 540).
`last_funding_date`	string	no	ISO date of last funding round (legacy field; latest_funding_date is preferred). Used to auto-detect recently_funded if the boolean is not set.
`latest_funding_date`	string	no	ISO date of the latest funding round (from C1 Funding & Press Signal Scanner when it ships). Drives recently_funded against funded_within_days.
`latest_funding_amount`	string	no	Dollar amount of the latest funding round (from C1 when it ships). Scores via well_funded when at or above min_funding_amount (default 1000000). Accepts a numeric string (e.g. "50000000").
`funding_stage`	string	no	Funding stage (e.g. seed, series_a, series_b, growth). Used to infer recently_funded.
`industry`	string	no	The company's industry (from the Company Firmographic Enricher, Actor ID YlUtLWjfPpqykmB8g).
`industry_match`	boolean	no	Override: whether the company's industry matches your target list.
`target_industries`	string	no	Comma-separated list of target industries for the industry_match signal.
`social_platforms_found`	string	no	Number of official social platforms found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Scores via social_presence when at or above min_social_platforms (default 2). Accepts a numeric string.
`total_followers`	string	no	Total social followers across platforms, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Scores via strong_social_following when at or above min_total_followers (default 1000). Accepts a numeric string.
`has_linkedin`	string	no	Whether a company LinkedIn page was found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h) or the Domain to LinkedIn URL Resolver (Actor ID 3HtnSaqPHOg1Qg5gx). Contributes to social_presence. Accepts a boolean-like string.
`has_twitter`	string	no	Whether a company X/Twitter profile was found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Contributes to social_presence. Accepts a boolean-like string.
`job_count`	string	no	Number of open jobs found, from the Job Board Keyword Signal Scanner (Actor ID 4DvqpvhMR74NLcDDY). Scores via active_hiring_volume when at or above min_job_count (default 3). Accepts a numeric string.
`keyword_match_count`	string	no	Number of target-keyword matches found, from the Job Board Keyword Signal Scanner (Actor ID 4DvqpvhMR74NLcDDY). Scores via keyword_signal_match when at or above min_keyword_matches (default 1). Accepts a numeric string.
`max_charge_usd`	number	no	Optional spending cap for this call, in USD. The actor stops before any charge that would take the run past this amount and keeps every row already charged.

`list_icp_templates`

No inputs. Returns the six prebuilt template names score_icp_fit accepts, with the actor's note on how they differ. It answers locally: no Apify run, no token, and no charge.

Define your ICP with exactly one of template, scoring_config, or icp_description.

This server exposes the single-company scoring path. The actor also supports batch inputs (a dataset or CSV of companies) and a results webhook. For those, run the actor directly on Apify.

Output

The tool returns the actor's flat JSON row for the scored company, including icp_score (0 to 100), icp_tier (A to D), the per-signal breakdown, and an optional explanation. See the Apify Store page for the full output schema.

Example output

{
  "company_domain": "ramp.com",
  "icp_score": 87,
  "icp_tier": "A",
  "lead_tag": "priority",
  "score_hiring": 25,
  "score_tech_stack": 22,
  "score_headcount": 20,
  "score_funding": 20,
  "score_industry": 0,
  "run_date": "2026-05-28"
}

Features

User-defined JSON scoring config with custom weights
Returns icp_score (0 to 100), icp_tier (A to D), and lead_tag
Per-signal point breakdown: hiring, tech stack, headcount, funding, industry
Replaces 6+ manual formula columns in Clay

Pricing

You pay per event on Apify, billed to your own Apify account. Prices from the live pricing record, read 2026-10-16:

Event	Price per event (USD)	Fires when
`apify-default-dataset-item`	$0.05 on the Free plan, down to $0.0425 on higher Apify plans	Single result in the default dataset.
`apify-actor-start`	$0.00005	Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).

The actor's Apify page carries the current prices. Running this server consumes Apify credits.

How each call runs

Each call starts the actor run, polls it until it finishes, then reads the dataset. The run is allowed 1,800 seconds. If the run is still going when this call stops waiting, the call returns the run id and a console link instead of a timeout, so the result is never lost.

Full actor documentation

This server is a thin client and holds no scoring logic. For the complete input and output reference, pricing, and run history, see the Apify Store page:

https://apify.com/mambalabs/icp-account-lead-scoring-fit-scorer-0-100-for-clay

Mamba Labs GTM Suite

This server is one of 58 Mamba Labs MCP servers, each backed by a dedicated Apify actor and published under @mambalabsdev on npm. The ones closest to this server:

Actor	Immutable Actor ID
GTM Hiring Signal Scraper	`D7O1SA2EqwHGsGr1P`
Tech Stack Signal Detector	`qyd7nNyqFPelQViBx`
GTM Signals Aggregator	`xKdRfnfFNkdMpFuNs`
Job Board Keyword Signal Scanner	`4DvqpvhMR74NLcDDY`
Domain to LinkedIn URL Resolver	`3HtnSaqPHOg1Qg5gx`
ICP Fit Scorer	`W161DT8W4kW55dMFh`
Domain Deliverability Checker	`0tVgxI7A6o9jMlxmc`
Company Firmographic Enricher	`YlUtLWjfPpqykmB8g`
Company Social Presence Mapper	`4k6CCemkgBDz18m2h`
Company Identity Resolver	`lr8fTRAmZCBZmuwwh`
Company Change Event Feed	`oX44rS0fkEJ3rXLWe`
Funding and Press Signal Scanner	`FS13X6dhQVgX3XOM6`

To get twenty one of them in one install, use @mambalabsdev/mcp-gtm-suite.

Built by Mamba Labs | npm | Apify Store

License

MIT

Built by Mamba Labs. https://apify.com/mambalabs

Available Tools

2 tools

list_icp_templatesList ICP TemplatesA

Read-onlyIdempotent

List the prebuilt ICP scoring templates score_icp_fit accepts in its template input, with the actor's own note on how they differ. Answers locally from the server: no Apify run, no token needed, and no charge. Use it before score_icp_fit when you have no scoring_config or icp_description of your own.

Name	Required	Description	Default
No parameters

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive and openWorld=false, so the safety profile is covered. The description goes further with cost/auth/latency facts the annotations cannot convey: 'answers locally from the server: no Apify run, no token needed, and no charge.' That is genuine added context, though return shape/ordering is left implicit.

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?

Two sentences, front-loaded with what it returns and then the decision rule for using it. Every clause earns its place; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only helper with no output schema, the description covers what is returned (templates plus the actor's note on how they differ) and when to call it. It stops short of describing result format or size, a minor gap given no output schema exists.

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?

The tool takes zero parameters, so the baseline is 4; there is no parameter semantics to document. The description correctly implies the call is argument-free by framing it as a local lookup.

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?

States a specific verb and resource (list prebuilt ICP scoring templates) and immediately ties the resource to the sibling that consumes it (score_icp_fit's template input). An agent can distinguish this from score_icp_fit without opening either schema.

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?

Gives an explicit trigger condition: 'Use it before score_icp_fit when you have no scoring_config or icp_description of your own.' It names the alternative tool and the exact circumstance that selects this one, leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

score_icp_fitScore ICP FitA

Read-onlyIdempotent

Score a company against your ideal customer profile (ICP) using weighted signals. Returns a 0 to 100 icp_score, an A to D icp_tier, and a per-signal breakdown as a flat, Clay-ready JSON row. Define your ICP with a prebuilt template, a JSON scoring_config, or a plain-English icp_description (which requires llm_api_key). Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.

Name	Required	Description
`industry`	No	The company's industry (from the Company Firmographic Enricher, Actor ID YlUtLWjfPpqykmB8g).
`template`	No	Name of a prebuilt scoring config, one of the six the actor ships: full_signal, saas_outbound, b2b_services, fintech, smb_local, enterprise. Alternative to scoring_config or icp_description. Call list_icp_templates to see them.
`headcount`	No	Current employee headcount. Accepts a numeric string (e.g. "3000"). Sent as a string for Clay compatibility and coerced to integer at runtime.
`job_count`	No	Number of open jobs found, from the Job Board Keyword Signal Scanner (Actor ID 4DvqpvhMR74NLcDDY). Scores via active_hiring_volume when at or above min_job_count (default 3). Accepts a numeric string.
`uses_clay`	No	Whether the company uses Clay. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`tech_stack`	No	Comma-separated list of technologies. Used to auto-detect CRM/sequencing tools if booleans are not set.
`has_twitter`	No	Whether a company X/Twitter profile was found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Contributes to social_presence. Accepts a boolean-like string.
`hq_location`	No	Headquarters location from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Carried for reference; not currently scored.
`llm_api_key`	No	Your OpenAI or Anthropic API key. Required only when using icp_description.
`company_name`	No	Optional display name of the company.
`crm_detected`	No	Whether any CRM was detected. Accepts a boolean-like string ("true"/"false") or any non-empty CRM name (e.g. "Salesforce"). Sent as a string for Clay compatibility and coerced to boolean at runtime. Auto-derived from uses_hubspot/uses_salesforce if not set.
`founded_year`	No	Year the company was founded, from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Carried for reference; not currently scored. Accepts a numeric string (e.g. "2015").
`has_linkedin`	No	Whether a company LinkedIn page was found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h) or the Domain to LinkedIn URL Resolver (Actor ID 3HtnSaqPHOg1Qg5gx). Contributes to social_presence. Accepts a boolean-like string.
`llm_provider`	No	LLM provider to use with icp_description: openai or anthropic.
`uses_hubspot`	No	Whether the company uses HubSpot. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`employee_band`	No	Firmographic employee band from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g), e.g. "201-500". Scores via employee_band_match when it is in target_employee_bands.
`fetch_signals`	No	If true, the actor fetches hiring and tech-stack signals for the company automatically before scoring.
`funding_stage`	No	Funding stage (e.g. seed, series_a, series_b, growth). Used to infer recently_funded.
`headcount_max`	No	Maximum headcount for the headcount_in_range signal.
`headcount_min`	No	Minimum headcount for the headcount_in_range signal.
`company_domain`	Yes	The primary domain of the company to score. Example: clay.com
`gtm_role_count`	No	Number of open GTM/sales roles. Scores via the gtm_role_count_strong signal when at or above min_gtm_roles (default 2). Accepts a numeric string (e.g. "8"). Sent as a string for Clay compatibility and coerced to integer at runtime.
`industry_match`	No	Override: whether the company's industry matches your target list.
`previous_score`	No	Previous ICP score for this company. If provided, output includes score_change and score_trend fields.
`scoring_config`	No	JSON object of scoring weights. Alternative to template or icp_description.
`icp_description`	No	Plain-English description of your target ICP. Requires llm_api_key. Alternative to template or scoring_config.
`recently_funded`	No	Override: whether the company was recently funded (within funded_within_days, default 540).
`tier_thresholds`	No	Optional. Minimum score for each tier as { "tier_a": number, "tier_b": number, "tier_c": number }. Scores at or above tier_a are A, tier_b are B, tier_c are C, else D. Defaults to 80 / 60 / 40.
`total_followers`	No	Total social followers across platforms, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Scores via strong_social_following when at or above min_total_followers (default 1000). Accepts a numeric string.
`uses_salesforce`	No	Whether the company uses Salesforce. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`revenue_estimate`	No	Estimated annual revenue in dollars from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Scores via revenue_in_range. Accepts a numeric string (e.g. "50000000"). Coerced to integer at runtime.
`gtm_hiring_signal`	No	Whether the company is actively hiring for GTM/sales roles. Accepts a boolean-like string ("true"/"false"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`last_funding_date`	No	ISO date of last funding round (legacy field; latest_funding_date is preferred). Used to auto-detect recently_funded if the boolean is not set.
`seq_tool_detected`	No	Whether a sales sequencing tool (Outreach, SalesLoft, Apollo, Lemlist) was detected. Accepts a boolean-like string ("true"/"false") or any non-empty tool name (e.g. "Outreach"). Sent as a string for Clay compatibility and coerced to boolean at runtime.
`target_industries`	No	Comma-separated list of target industries for the industry_match signal.
`funded_within_days`	No	Optional. How recent a funding round must be to count for the recently_funded signal, in days. Defaults to 540 (18 months).
`headcount_in_range`	No	Override: whether headcount is in your target range.
`include_explanation`	No	If true, adds a score_explanation string to the output describing how the score was derived.
`keyword_match_count`	No	Number of target-keyword matches found, from the Job Board Keyword Signal Scanner (Actor ID 4DvqpvhMR74NLcDDY). Scores via keyword_signal_match when at or above min_keyword_matches (default 1). Accepts a numeric string.
`latest_funding_date`	No	ISO date of the latest funding round (from C1 Funding & Press Signal Scanner when it ships). Drives recently_funded against funded_within_days.
`min_score_to_output`	No	If set, rows scoring below this threshold are skipped from output (not pushed to dataset). Skipped rows are logged only.
`latest_funding_amount`	No	Dollar amount of the latest funding round (from C1 when it ships). Scores via well_funded when at or above min_funding_amount (default 1000000). Accepts a numeric string (e.g. "50000000").
`social_platforms_found`	No	Number of official social platforms found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Scores via social_presence when at or above min_social_platforms (default 2). Accepts a numeric string.

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so safety is covered. The description adds real context beyond them: it requires an APIFY_TOKEN, consumes Apify credits per call, and makes llm_api_key mandatory only for icp_description. It does not discuss failure modes or rate limits.

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?

Three tight sentences, front-loaded with purpose then outputs then configuration modes then operational cost. Every sentence carries information; no padding. Slightly dense rather than perfectly optimized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 43-parameter tool with no output schema, the description does the essential work: it names the return shape (0-100 score, A-D tier, per-signal flat row) and the three config paths. It omits anything about how weighted signals combine or default thresholds, which it could have sketched, but nothing critical for invocation is missing.

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 description coverage is 100%, so all 43 parameters are already documented in the schema, including the 'Alternative to...' relationships between template, scoring_config, and icp_description. The description restates that mutual exclusivity but adds no syntax or coercion detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('Score a company against your ideal customer profile') and immediately names the concrete outputs (icp_score, icp_tier, per-signal breakdown). An agent can distinguish this from the sibling list_icp_templates, which only enumerates configs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context for the three mutually exclusive ways to define an ICP (prebuilt template, JSON scoring_config, or plain-English icp_description), plus the conditional llm_api_key requirement for the last one. It stops short of explicitly routing the agent to list_icp_templates for discovery, so it is not quite a 5.

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.

2 tool updatesv1.0.6Oct 13, 2026

Addedlist_icp_templates

Changedscore_icp_fit37 fields changed

addedInput schema / properties / crm_detected

Added value: +{
+  "description": "Whether any CRM was detected. Accepts a boolean-like string (\"true\"/\"false\") or any non-empty CRM name (e.g. \"Salesforce\"). Sent as a string for Clay compatibility and coerced to boolean at runtime. Auto-derived from uses_hubspot/uses_salesforce if not set.",
+  "type": "string"
+}

addedInput schema / properties / employee_band

Added value: +{
+  "description": "Firmographic employee band from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g), e.g. \"201-500\". Scores via employee_band_match when it is in target_employee_bands.",
+  "type": "string"
+}

addedInput schema / properties / founded_year

Added value: +{
+  "description": "Year the company was founded, from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Carried for reference; not currently scored. Accepts a numeric string (e.g. \"2015\").",
+  "type": "string"
+}

addedInput schema / properties / funded_within_days

Added value: +{
+  "description": "Optional. How recent a funding round must be to count for the recently_funded signal, in days. Defaults to 540 (18 months).",
+  "type": "integer"
+}

addedInput schema / properties / funding_stage

Added value: +{
+  "description": "Funding stage (e.g. seed, series_a, series_b, growth). Used to infer recently_funded.",
+  "type": "string"
+}

addedInput schema / properties / gtm_hiring_signal

Added value: +{
+  "description": "Whether the company is actively hiring for GTM/sales roles. Accepts a boolean-like string (\"true\"/\"false\"). Sent as a string for Clay compatibility and coerced to boolean at runtime.",
+  "type": "string"
+}

addedInput schema / properties / gtm_role_count

Added value: +{
+  "description": "Number of open GTM/sales roles. Scores via the gtm_role_count_strong signal when at or above min_gtm_roles (default 2). Accepts a numeric string (e.g. \"8\"). Sent as a string for Clay compatibility and coerced to integer at runtime.",
+  "type": "string"
+}

addedInput schema / properties / has_linkedin

Added value: +{
+  "description": "Whether a company LinkedIn page was found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h) or the Domain to LinkedIn URL Resolver (Actor ID 3HtnSaqPHOg1Qg5gx). Contributes to social_presence. Accepts a boolean-like string.",
+  "type": "string"
+}

addedInput schema / properties / has_twitter

Added value: +{
+  "description": "Whether a company X/Twitter profile was found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Contributes to social_presence. Accepts a boolean-like string.",
+  "type": "string"
+}

addedInput schema / properties / headcount

Added value: +{
+  "description": "Current employee headcount. Accepts a numeric string (e.g. \"3000\"). Sent as a string for Clay compatibility and coerced to integer at runtime.",
+  "type": "string"
+}

addedInput schema / properties / headcount_in_range

Added value: +{
+  "description": "Override: whether headcount is in your target range.",
+  "type": "boolean"
+}

addedInput schema / properties / headcount_max

Added value: +{
+  "description": "Maximum headcount for the headcount_in_range signal.",
+  "type": "integer"
+}

addedInput schema / properties / headcount_min

Added value: +{
+  "description": "Minimum headcount for the headcount_in_range signal.",
+  "type": "integer"
+}

addedInput schema / properties / hq_location

Added value: +{
+  "description": "Headquarters location from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Carried for reference; not currently scored.",
+  "type": "string"
+}

addedInput schema / properties / industry

Added value: +{
+  "description": "The company's industry (from the Company Firmographic Enricher, Actor ID YlUtLWjfPpqykmB8g).",
+  "type": "string"
+}

addedInput schema / properties / industry_match

Added value: +{
+  "description": "Override: whether the company's industry matches your target list.",
+  "type": "boolean"
+}

addedInput schema / properties / job_count

Added value: +{
+  "description": "Number of open jobs found, from the Job Board Keyword Signal Scanner (Actor ID 4DvqpvhMR74NLcDDY). Scores via active_hiring_volume when at or above min_job_count (default 3). Accepts a numeric string.",
+  "type": "string"
+}

addedInput schema / properties / keyword_match_count

Added value: +{
+  "description": "Number of target-keyword matches found, from the Job Board Keyword Signal Scanner (Actor ID 4DvqpvhMR74NLcDDY). Scores via keyword_signal_match when at or above min_keyword_matches (default 1). Accepts a numeric string.",
+  "type": "string"
+}

addedInput schema / properties / last_funding_date

Added value: +{
+  "description": "ISO date of last funding round (legacy field; latest_funding_date is preferred). Used to auto-detect recently_funded if the boolean is not set.",
+  "type": "string"
+}

addedInput schema / properties / latest_funding_amount

Added value: +{
+  "description": "Dollar amount of the latest funding round (from C1 when it ships). Scores via well_funded when at or above min_funding_amount (default 1000000). Accepts a numeric string (e.g. \"50000000\").",
+  "type": "string"
+}

addedInput schema / properties / latest_funding_date

Added value: +{
+  "description": "ISO date of the latest funding round (from C1 Funding & Press Signal Scanner when it ships). Drives recently_funded against funded_within_days.",
+  "type": "string"
+}

addedInput schema / properties / llm_provider / enum
```
Added value: +[
+  "openai",
+  "anthropic"
+]
```

addedInput schema / properties / min_score_to_output

Added value: +{
+  "description": "If set, rows scoring below this threshold are skipped from output (not pushed to dataset). Skipped rows are logged only.",
+  "type": "integer"
+}

addedInput schema / properties / previous_score

Added value: +{
+  "description": "Previous ICP score for this company. If provided, output includes score_change and score_trend fields.",
+  "type": "integer"
+}

addedInput schema / properties / recently_funded

Added value: +{
+  "description": "Override: whether the company was recently funded (within funded_within_days, default 540).",
+  "type": "boolean"
+}

addedInput schema / properties / revenue_estimate

Added value: +{
+  "description": "Estimated annual revenue in dollars from the Company Firmographic Enricher (Actor ID YlUtLWjfPpqykmB8g). Scores via revenue_in_range. Accepts a numeric string (e.g. \"50000000\"). Coerced to integer at runtime.",
+  "type": "string"
+}

addedInput schema / properties / seq_tool_detected

Added value: +{
+  "description": "Whether a sales sequencing tool (Outreach, SalesLoft, Apollo, Lemlist) was detected. Accepts a boolean-like string (\"true\"/\"false\") or any non-empty tool name (e.g. \"Outreach\"). Sent as a string for Clay compatibility and coerced to boolean at runtime.",
+  "type": "string"
+}

addedInput schema / properties / social_platforms_found

Added value: +{
+  "description": "Number of official social platforms found, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Scores via social_presence when at or above min_social_platforms (default 2). Accepts a numeric string.",
+  "type": "string"
+}

addedInput schema / properties / target_industries

Added value: +{
+  "description": "Comma-separated list of target industries for the industry_match signal.",
+  "type": "string"
+}

addedInput schema / properties / tech_stack

Added value: +{
+  "description": "Comma-separated list of technologies. Used to auto-detect CRM/sequencing tools if booleans are not set.",
+  "type": "string"
+}

changedInput schema / properties / template / description

Previous value: -"Name of a prebuilt scoring config. Alternative to scoring_config or icp_description."New value: +"Name of a prebuilt scoring config, one of the six the actor ships: full_signal, saas_outbound, b2b_services, fintech, smb_local, enterprise. Alternative to scoring_config or icp_description. Call list_icp_templates to see them."

addedInput schema / properties / template / enum

Added value: +[
+  "full_signal",
+  "saas_outbound",
+  "b2b_services",
+  "fintech",
+  "smb_local",
+  "enterprise"
+]

addedInput schema / properties / tier_thresholds

Added value: +{
+  "additionalProperties": {},
+  "description": "Optional. Minimum score for each tier as { \"tier_a\": number, \"tier_b\": number, \"tier_c\": number }. Scores at or above tier_a are A, tier_b are B, tier_c are C, else D. Defaults to 80 / 60 / 40.",
+  "type": "object"
+}

addedInput schema / properties / total_followers

Added value: +{
+  "description": "Total social followers across platforms, from the Company Social Presence Mapper (Actor ID 4k6CCemkgBDz18m2h). Scores via strong_social_following when at or above min_total_followers (default 1000). Accepts a numeric string.",
+  "type": "string"
+}

addedInput schema / properties / uses_clay

Added value: +{
+  "description": "Whether the company uses Clay. Accepts a boolean-like string (\"true\"/\"false\"). Sent as a string for Clay compatibility and coerced to boolean at runtime.",
+  "type": "string"
+}

addedInput schema / properties / uses_hubspot

Added value: +{
+  "description": "Whether the company uses HubSpot. Accepts a boolean-like string (\"true\"/\"false\"). Sent as a string for Clay compatibility and coerced to boolean at runtime.",
+  "type": "string"
+}

addedInput schema / properties / uses_salesforce

Added value: +{
+  "description": "Whether the company uses Salesforce. Accepts a boolean-like string (\"true\"/\"false\"). Sent as a string for Clay compatibility and coerced to boolean at runtime.",
+  "type": "string"
+}

1 tool updatev1.0.3Jun 2, 2026
- First observedscore_icp_fit

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored 2026-10-16 10:10 across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: list_icp_templates is a local, free discovery helper while score_icp_fit performs the actual scoring. There is no overlap in function or confusion about when to use each.

Naming Consistency5/5

Both names follow a consistent verb_noun snake_case pattern (list_icp_templates, score_icp_fit). The convention is predictable and readable throughout.

Tool Count3/5

With only two tools, the surface is thin: one is essentially a metadata helper for the other, leaving a single real operation. It is well-scoped for a narrow purpose but feels minimal rather than complete.

Completeness3/5

The core scoring workflow is covered, including three ways to define an ICP (template, JSON config, or plain-English description). However, there is no batch scoring, no custom template creation/management, and no way to save or retrieve past scores, which are notable gaps for a scoring service.

Maintenance

ActivityMaintained

ResponsivenessResponsive

Resources

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ICP Fit Scorer MCP Server

What's Inside

What it does

Quick start

Prerequisites

Example prompts

Inputs

score_icp_fit

list_icp_templates

Output

Example output

Features

Pricing

How each call runs

Full actor documentation

Mamba Labs GTM Suite

License

Available Tools

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

mcp-gtm-tech-stack-signal

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`score_icp_fit`

`list_icp_templates`