Hatch MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count5/5
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.
Completeness4/5
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.
Average 3.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness3/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines3/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines2/5
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.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![hatch-mcp-server MCP server](https://glama.ai/mcp/servers/Meerkats-Ai/hatch-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/Meerkats-Ai/hatch-mcp-server)

Score Badge

Copy to your README.md:

[![hatch-mcp-server MCP server](https://glama.ai/mcp/servers/Meerkats-Ai/hatch-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/Meerkats-Ai/hatch-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Meerkats-Ai/hatch-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server