Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Hatch MCP Server find John Doe's email at acme.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This is a Model Context Protocol (MCP) server for Hatch API integration. It provides tools for finding emails, phone numbers, verifying emails, finding company data, and getting LinkedIn URLs.
Find email addresses using first name, last name, and domain information
Find phone numbers using LinkedIn profile URLs
Verify if an email address is valid and active
Find detailed information about a company using its domain
Find LinkedIn URLs using name, designation, and company information
Related MCP server: Prospeo MCP Server
Clone the repository
Install dependencies:
npm installCreate a .env file based on .env.example and add your Hatch API key:
HATCH_API_KEY=your_api_key_hereBuild the project:
npm run buildTo start the server directly:
npm startTo use this MCP server with Claude, you need to add it to the MCP settings file:
For Claude VSCode extension, add it to c:\Users\<username>\AppData\Roaming\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
For Claude desktop app, add it to %APPDATA%\Claude\claude_desktop_config.json on Windows
Example configuration:
{
"mcpServers": {
"hatch": {
"command": "node",
"args": ["E:/mcp-servers/hatch/dist/index.js"],
"env": {
"HATCH_API_KEY": "your_api_key_here"
},
"disabled": false,
"autoApprove": []
}
}
}Replace your_api_key_here with your actual Hatch API key.
The server can be configured using environment variables:
HATCH_API_KEY (required): Your Hatch API key
HATCH_API_URL (optional): Custom API URL (defaults to https://api.hatchhq.ai)
HATCH_RETRY_MAX_ATTEMPTS: Maximum retry attempts for API calls (default: 3)
HATCH_RETRY_INITIAL_DELAY: Initial delay in milliseconds for retries (default: 1000)
HATCH_RETRY_MAX_DELAY: Maximum delay in milliseconds for retries (default: 10000)
HATCH_RETRY_BACKOFF_FACTOR: Backoff factor for retry delays (default: 2)
Find an email address using first name, last name, and domain information.
Parameters:
firstName (required): First name of the person
lastName (required): Last name of the person
domain (required): Company domain name
Find a phone number using LinkedIn profile URL.
Parameters:
linkedInUrl (required): LinkedIn profile URL of the person
Verify if an email address is valid and active.
Parameters:
email (required): Email address to verify
Find detailed information about a company using its domain.
Parameters:
domain (required): Company domain name
Find LinkedIn URL using name, designation, and company information.
Parameters:
name: Name of the person
designation: Job title or designation of the person
companyName (required): Company name
MIT
Find detailed information about a company using its domain.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Company domain name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'Find[s] detailed information' but doesn't specify what that information includes, whether it's read-only or has side effects, or any limitations like rate limits or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond the basic action.
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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded with the core action and resource, making it easy to understand at a glance. Every part of the sentence earns its place by conveying essential information.
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 complexity of a company data lookup tool, with no annotations and no output schema, the description is insufficient. It doesn't explain what 'detailed information' entails, the response format, or any behavioral traits like error handling. This leaves the agent with incomplete context for effective tool invocation.
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?
The input schema has 100% description coverage, with the 'domain' parameter clearly documented as 'Company domain name'. The description adds no additional semantic context beyond this, such as format examples or domain validation rules. Since the schema does the heavy lifting, the baseline score of 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 clearly states the verb 'Find' and the resource 'detailed information about a company', specifying the input 'using its domain'. It distinguishes from siblings like hatch_find_email or hatch_find_phone by focusing on company data rather than contact details. However, it doesn't explicitly mention what 'detailed information' includes, which prevents a perfect score.
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 implies usage when you have a company domain and need company information, but it doesn't provide explicit guidance on when to use this tool versus alternatives like hatch_get_linkedin_url for LinkedIn data or hatch_verify_email for email verification. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find an email address using first name, last name, and domain information.
| Name | Required | Description | Default |
|---|---|---|---|
| firstName | Yes | First name of the person | |
| lastName | Yes | Last name of the person | |
| domain | Yes | Company domain name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Find[s] an email address' but doesn't describe how it works (e.g., guessing patterns, querying a database), what happens on failures, rate limits, or authentication needs. This is inadequate for a tool with no annotation coverage.
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 a single, efficient sentence that directly states the tool's purpose and required inputs. It's front-loaded with essential information and has no wasted words, making it easy for an agent to parse quickly.
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 moderate complexity (3 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and inputs but lacks details on behavior, output format, or error handling. Without annotations or output schema, more context would be helpful for reliable use.
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?
The schema description coverage is 100%, with clear descriptions for each parameter (e.g., 'First name of the person'). The description adds no additional semantic context beyond what the schema provides, such as format examples or constraints. This meets the baseline of 3 when schema coverage is high.
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's purpose: 'Find an email address using first name, last name, and domain information.' It specifies the verb ('Find'), resource ('email address'), and required inputs. However, it doesn't explicitly differentiate from sibling tools like hatch_find_phone or hatch_verify_email, which prevents a score of 5.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like hatch_verify_email (for verification) or hatch_find_phone (for phone numbers), nor does it specify prerequisites or exclusions. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find a phone number using LinkedIn profile URL.
| Name | Required | Description | Default |
|---|---|---|---|
| linkedInUrl | Yes | LinkedIn profile URL of the person |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool's purpose but lacks details on behavioral traits like rate limits, authentication needs, data sources, or what happens if the phone number is not found, leaving significant gaps for an agent.
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 a single, clear sentence that directly states the tool's purpose without unnecessary words, making it appropriately sized and front-loaded for quick understanding.
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 no annotations and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., phone number format, error handling), behavioral aspects like rate limits, or usage context, which are critical for an agent to use it effectively.
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?
The input schema has 100% description coverage, with the parameter 'linkedInUrl' documented as 'LinkedIn profile URL of the person'. The description adds no additional meaning beyond this, such as URL format examples or validation rules, so it meets the baseline for high schema coverage.
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 specific action ('Find a phone number') and the resource used ('using LinkedIn profile URL'), distinguishing it from sibling tools like hatch_find_email or hatch_get_linkedin_url by focusing on phone number retrieval rather than email or URL fetching.
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 provides no guidance on when to use this tool versus alternatives, such as hatch_find_email for email retrieval or hatch_get_linkedin_url for URL fetching, nor does it mention any prerequisites or exclusions for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find LinkedIn URL using name, designation, and company information.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Name of the person | |
| designation | No | Job title or designation of the person | |
| companyName | Yes | Company name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool finds LinkedIn URLs but doesn't describe how it works (e.g., search method, accuracy, rate limits, authentication needs, or what happens if no match is found). This leaves significant gaps in understanding the tool's behavior and limitations.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and uses all information effectively, making it easy to parse and understand quickly.
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 (finding LinkedIn URLs with three parameters) and lack of annotations or output schema, the description is incomplete. It doesn't cover behavioral aspects like search methodology, error handling, or result format, leaving the agent with insufficient information for reliable use beyond basic invocation.
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?
The description adds minimal value beyond the input schema, which has 100% coverage with clear descriptions for all three parameters. It mentions the parameters (name, designation, company) but doesn't provide additional context like format examples, constraints, or how they're used in the search. With high schema coverage, the baseline score of 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 clearly states the tool's purpose: 'Find LinkedIn URL using name, designation, and company information.' It specifies the verb ('Find'), resource ('LinkedIn URL'), and required input parameters. However, it doesn't explicitly differentiate from sibling tools like hatch_find_email or hatch_find_phone, which also find contact information but for different resources.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like hatch_find_email or hatch_verify_email, nor does it specify prerequisites, exclusions, or contextual cues for selection. Usage is implied by the purpose but lacks explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify if an email address is valid and active.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to verify |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool verifies email validity and activity, but doesn't explain what 'valid and active' means operationally (e.g., syntax check, domain verification, mailbox ping). It lacks details about rate limits, authentication requirements, error handling, or what constitutes a successful verification.
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 extremely concise at just one sentence with zero wasted words. It's front-loaded with the core purpose and efficiently communicates the essential function. Every word earns its place without unnecessary elaboration.
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 no annotations and no output schema, the description is incomplete for a verification tool. It doesn't explain what verification results look like (e.g., boolean response, confidence score, detailed status). For a tool that presumably returns verification outcomes, the lack of output information creates significant ambiguity for an agent trying to use it correctly.
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?
The input schema has 100% description coverage, clearly documenting the single 'email' parameter. The description adds no additional parameter semantics beyond what the schema provides. It doesn't specify email format requirements, validation rules, or examples. The baseline score of 3 reflects adequate coverage when the schema does the heavy lifting.
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's purpose with a specific verb ('verify') and resource ('email address'), and specifies what is being verified ('valid and active'). It distinguishes itself from siblings like 'hatch_find_email' by focusing on verification rather than discovery. However, it doesn't explicitly contrast with siblings in the description text itself.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'hatch_find_email' for email discovery or other verification methods. There's no context about prerequisites, limitations, or typical use cases for email verification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose targeting different data types: company data, email, phone, LinkedIn URL, and email verification. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the specific need.
All tools follow a consistent 'hatch_verb_noun' pattern (e.g., hatch_find_company_data, hatch_verify_email). The naming is uniform, using snake_case throughout, which enhances predictability and readability.
With 5 tools, the server is well-scoped for its purpose of data lookup and verification. Each tool serves a specific, non-redundant function, making the count appropriate and manageable for the domain.
The toolset covers key data lookup operations (company, email, phone, LinkedIn URL) and email verification, providing a solid foundation. A minor gap might be the lack of a tool for updating or managing retrieved data, but the core functionality is complete for the stated purpose.
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