Hunter.io MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: account info retrieval, domain-wide email discovery, email count estimation, targeted email lookup, and email verification. There is no overlap or ambiguity between these functions, making tool selection straightforward for an agent.
Naming Consistency5/5
All tools follow a consistent 'hunter_verb_noun' pattern with snake_case, using descriptive verbs like 'account_info', 'domain_search', 'email_count', 'find_email', and 'verify_email'. This uniformity enhances predictability and readability.
Tool Count5/5
With 5 tools, this server is well-scoped for email discovery and verification tasks. Each tool serves a unique and necessary function, covering core operations without bloat or redundancy, making the count ideal for the domain.
Completeness4/5
The toolset provides comprehensive coverage for email discovery and verification workflows, including search, count, targeted lookup, and validation. A minor gap might be the absence of tools for managing saved searches or batch operations, but core functionality is complete.
Average 3/5 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
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'finds' email addresses, implying a read-only operation, but lacks details on permissions, rate limits, data sources (e.g., Hunter.io API), pagination (implied by limit/offset but not explained), or error handling. For a tool with 5 parameters and no annotations, this is a significant gap in transparency.
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: 'Find all the email addresses corresponding to a website or company name.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place.
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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'find' entails (e.g., search results from a database), return format (list of emails with metadata?), or behavioral constraints. Without annotations or output schema, the agent lacks critical context for effective 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?
Schema description coverage is 100%, so the input schema fully documents all 5 parameters (company, domain, limit, offset, type) with descriptions. The description adds no additional parameter semantics beyond implying domain/company as alternatives, which is already covered in the schema. This meets the baseline of 3 for high schema coverage.
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 all the email addresses corresponding to a website or company name.' It specifies the verb ('find') and resource ('email addresses'), and distinguishes the input type (domain or company name) from siblings like hunter_find_email (which likely finds specific emails) or hunter_verify_email (which verifies emails). However, it doesn't explicitly differentiate from hunter_email_count (which might count emails without listing them), so it's not a perfect 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 its siblings. It doesn't mention alternatives like hunter_find_email for targeted searches or hunter_email_count for just counts, nor does it specify prerequisites or exclusions (e.g., when domain vs. company is preferred). This leaves the agent to infer usage from tool names alone, which is insufficient.
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 'finds' an email address, implying a read-only operation, but doesn't address critical aspects like authentication requirements, rate limits, error handling, or what happens if no email is found. This is a significant gap for a tool with potential external API calls.
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 and wastes no space, 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.
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 (involving external data lookup), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return format, potential errors, or behavioral traits like rate limits, which are crucial for effective tool invocation in a real-world context.
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 clear parameter definitions (e.g., domain as 'The domain name of the company, e.g. "stripe.com"'). The description adds minimal value beyond the schema by mentioning 'domain and name information,' which aligns with parameters like 'first_name' and 'last_name,' but doesn't provide additional syntax or usage details. This 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.
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 as 'Find an email address using domain and name information,' which specifies the verb (find), resource (email address), and key inputs (domain and name). However, it doesn't explicitly distinguish this tool from its siblings like 'hunter_domain_search' or 'hunter_verify_email,' which may also involve email-related searches, so it lacks sibling differentiation.
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 any prerequisites, exclusions, or comparisons to sibling tools such as 'hunter_domain_search' or 'hunter_verify_email,' leaving 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?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states this is a 'Get' operation, implying read-only behavior, but doesn't specify what information is returned (e.g., account limits, usage stats, billing details), authentication requirements, rate limits, or error conditions. This is inadequate for a tool with zero 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 states the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the essential action. Every word earns its place.
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 simplicity (0 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what account information is returned, which is critical for an agent to understand the tool's utility. Without annotations or output schema, the description should provide more context about the return values.
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 has 0 parameters, and schema description coverage is 100%, so there's no parameter documentation burden. The description doesn't need to compensate for missing param info. A baseline of 4 is appropriate since no parameters exist to explain.
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 action ('Get information') and resource ('your Hunter account'), making the purpose understandable. However, it doesn't differentiate this tool from its siblings (domain_search, email_count, etc.), which are all Hunter-related but serve different functions. A 5 would require explicit sibling differentiation.
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 like the sibling tools. It doesn't mention prerequisites, context for usage, or exclusions. While the purpose is clear, the lack of comparative guidance leaves the agent without direction on 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a count of email addresses, but doesn't explain how the count is derived (e.g., from a database, API limits), whether it requires authentication, rate limits, or what the output format is. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.
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 functionality and avoids redundancy, making it easy 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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and output expectations. Without annotations or an output schema, the description should do more to compensate, but it only meets the bare minimum for clarity.
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 clear parameter descriptions for 'company' and 'domain'. The description adds minimal value beyond the schema by implying these are alternative inputs for counting emails, but doesn't provide additional context like examples, constraints, or how they interact. With high schema coverage, the baseline score of 3 is appropriate as the schema handles most of the documentation.
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: 'Know how many email addresses we have for a domain or a company.' It specifies the verb ('know how many') and resource ('email addresses'), and distinguishes the scope ('domain or a company'). However, it doesn't explicitly differentiate from siblings like hunter_domain_search or hunter_find_email, which might also involve domain-related queries.
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 mentions 'domain or a company' but doesn't clarify if this is for counting emails versus searching or verifying them, nor does it reference sibling tools like hunter_domain_search or hunter_verify_email. There's no mention of prerequisites, exclusions, or comparative contexts.
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 full burden for behavioral disclosure. While 'verify' implies a read-only check, the description doesn't mention rate limits, authentication requirements, what 'deliverable' means operationally, or what the verification process entails. It provides minimal behavioral context beyond the basic purpose.
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 states the core purpose without unnecessary words. It's appropriately sized for a simple verification tool and front-loads the essential information.
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?
For a single-parameter verification tool with no output schema, the description adequately covers the basic purpose. However, without annotations or output schema, it lacks details about the verification process, result format, or behavioral constraints that would be helpful for an AI agent 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 schema description coverage is 100%, with the single parameter 'email' already documented in the schema. The description doesn't add any additional parameter semantics beyond what's in the schema (e.g., format requirements, examples, or edge cases). Baseline 3 is appropriate 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 the verification criteria ('valid and deliverable'). However, it doesn't differentiate this email verification tool from the sibling tools (like domain_search or find_email) which have different purposes.
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 the sibling tools (hunter_domain_search, hunter_find_email, etc.). It doesn't mention prerequisites, alternatives, or specific contexts where email verification is appropriate versus other email-related operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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