DataLayer MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DATALAYER_API_KEY | Yes | Your API key from datalayer.sh | |
| DATALAYER_API_URL | No | Override API base URL (default: https://api.datalayer.sh) |
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
} |
| logging | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| enrich_companyB | Get a full company profile with signals — industry, headcount, revenue, tech stack, funding, traffic, Google ad spend, employee growth rate. |
| enrich_personB | Get a full contact profile — name, email, phone, job title, seniority, LinkedIn, current employer. |
| search_companiesA | Search 60M+ companies by industry, location, size, tech stack, funding, traffic, growth, and more. Returns paginated results. |
| search_peopleB | Search 300M+ contacts by title, seniority, function, company, location, and more. Returns paginated results. |
| lookup_personA | Find a specific person by email, phone, LinkedIn URL, or name + company domain. |
| lookup_companyC | Find a specific company by domain, LinkedIn URL, or name. |
| company_employeesC | List employees at a company, filterable by seniority and function. |
| company_headcountC | Get headcount breakdown by department (15 departments) for a company. |
| company_technographicsC | Get the full tech stack of a company across 16 categories — CRM, cloud, marketing automation, analytics, and more. |
| company_jobsB | Get open job counts by department — a hiring intent signal. |
| find_intent_signalsA | Find companies showing buying intent — scored by web traffic, Google ad spend, hiring velocity, employee growth, and funding. Costs 5 credits per result. |
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 11 tools
Each tool has a clearly distinct purpose with no significant overlap: company-focused tools (employees, headcount, jobs, technographics, enrich, lookup, search) and person-focused tools (enrich, lookup, search) are well-separated, and even within categories (e.g., company_employees vs. company_headcount vs. company_jobs) target different data aspects. The descriptions reinforce these distinctions, making misselection unlikely.
Tool names follow a highly consistent verb_noun pattern throughout, using snake_case uniformly: company_employees, company_headcount, company_jobs, company_technographics, enrich_company, enrich_person, find_intent_signals, lookup_company, lookup_person, search_companies, search_people. This predictability aids agent navigation and understanding.
With 11 tools, the count is well-scoped for a data layer server covering company and person enrichment, search, and intent signals. Each tool earns its place by addressing a specific need (e.g., detailed profiles, filtered searches, headcount breakdowns), avoiding bloat while providing comprehensive coverage for the domain.
The toolset offers complete CRUD-like coverage for the data domain: lookup and search for discovery, enrich for detailed profiles, and specialized tools for headcount, jobs, technographics, and intent signals. There are no obvious gaps; agents can perform end-to-end workflows from finding entities to analyzing their attributes and signals.