mcp-icp-fit-scorer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| APIFY_TOKEN | Yes | Your Apify API token. Get it at https://console.apify.com/account/integrations. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| score_icp_fitA | 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. |
| list_icp_templatesA | 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
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.
Both names follow a consistent verb_noun snake_case pattern (list_icp_templates, score_icp_fit). The convention is predictable and readable throughout.
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.
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.