leads-mcp

Server Quality Checklist

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

Latest release: v1.0.1
Disambiguation5/5
Each tool targets a distinct action: email lookup, email verification, domain email listing, person/company enrichment, people/company search, and combined reporting. No two tools overlap in purpose, and the descriptions clearly differentiate them.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern (find-email, verify-email, enrich-person, search-people, etc.), but 'domain-search' and 'lead-report' are noun-heavy deviations. The overall hyphenated lowercase style is consistent, so only minor inconsistency exists.
Tool Count5/5
With 8 tools, the set is well-scoped for a lead generation/enrichment server. Each tool serves a clear function without redundancy, and the count falls comfortably within the ideal range.
Completeness5/5
The tools cover the full lead generation workflow: discovery (search-people, search-companies), email lookup (find-email, domain-search), verification (verify-email), enrichment (enrich-person, enrich-company), and synthesis (lead-report). No major gaps or dead ends are apparent.
Average 3.6/5 across 8 of 8 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
This repository is licensed under MIT License.
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?
There are no annotations, so the description must disclose behavioral traits. It mentions 'Uses Apollo.io,' indicating an external data source, but does not disclose rate limits, data freshness, pagination, or what the response contains. This limited disclosure is insufficient for a search tool.
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 concise sentence that front-loads the primary action and criteria. There is no wasted text, and the second sentence about Apollo.io is valuable context.
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?
With no output schema and no annotations, the description should specify the return value and any caveats. It covers the search criteria but not the response format or behavior when results are empty. The strong schema coverage partially compensates, but the description lacks completeness.
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 already provides descriptions for all five parameters (100% coverage), so the description adds little beyond a high-level summary. The mention of job title, company, location, and seniority maps to the schema parameters but does not provide additional semantic detail.
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 tool searches for people using specific criteria (job title, company, location, seniority). The verb 'search' and resource 'people' are explicit, and listing the filter criteria distinguishes it from sibling tools like search-companies and enrich-person.
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 enrich-person or find-email. It does not mention any exclusions or prerequisites, leaving the agent to infer usage from the purpose.
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 full transparency burden. It mentions the action and filters but does not disclose important behaviors such as the limit parameter defaulting to 10, whether 'all' is actually capped, output format, or whether emails are verified.
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?
Two sentences, front-loaded with the primary verb and resource, and no filler. The second sentence efficiently lists the filter options.
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?
With 5 parameters and no output schema or annotations, the description does not explain the return value structure, default limit behavior, or the nuance behind 'all' (which is constrained by the limit parameter). This leaves significant gaps for an agent deciding to invoke the tool.
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 coverage is 100% with each parameter having a description. The description only names the filter categories (department, seniority, email type) without adding any semantics beyond what the schema already provides, so baseline 3 applies.
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?
Description clearly states it finds all email addresses for a company domain, using a specific verb and resource. It distinguishes itself from siblings like verify-email or enrich-person by focusing on domain-based discovery.
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?
Usage is implied: use when you need emails for a given domain. However, no explicit alternatives or exclusions are mentioned, leaving ambiguity with sibling tools like find-email.
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 full responsibility for behavioral disclosure. It mentions 'Uses Hunter.io' but does not explain that the result may be unverified, that rate limits or API keys apply, or what happens if no email is found. For a read-only lookup that may return probabilistic data, this is a significant gap.
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 two short sentences, front-loaded with the core action. 'Uses Hunter.io' is a concise implementation note that provides context without bloat. No words are wasted, and the information is immediately understandable.
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?
This tool has no output schema, no annotations, and a simple input schema. The description fails to specify return format, failure behavior, or how it differs from related tools like enrich-person or domain-search. Given the lack of structured info elsewhere, the description is incomplete for confident invocation and result interpretation.
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%, with clear descriptions for all three parameters (firstName, lastName, domain). The description's phrase 'given their name and company domain' paraphrases the schema but adds no new format, constraints, or examples beyond what the schema already provides. Thus, baseline 3 is appropriate.
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 states a specific verb ('Find') and resource ('email address'), with clear inputs ('name and company domain'). It distinguishes this from sibling tools like domain-search (which finds all emails in a domain) and verify-email (which checks an existing email). The purpose is unambiguous and directly tied to the tool's name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool when you have a person's name and company domain. It does not explicitly mention when not to use it or name alternatives, but the purpose itself differentiates it from siblings. This is more than implied usage—it states the exact precondition.
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 present, so the description must disclose behavior. It mentions combining data from all available APIs, hinting at multiple underlying calls, but fails to address read-only status, potential data absence, rate limits, or error behavior. The vagueness of 'all available APIs' adds uncertainty.
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, well-structured sentence that front-loads the main action and resource. It contains no redundant information and earns its place in terms of conciseness.
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 composite report tool and the absence of annotations and output schema, the description is too sparse. It does not explain what the report contains beyond data types, how results are returned, or how it handles missing data from any API, which leaves significant gaps.
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 coverage is 100%, with each parameter (firstName, lastName, domain) already described in the schema. The description adds no additional parameter meaning beyond what is in the schema, so 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a comprehensive lead report combining email, person profile, and company data. This distinguishes it from sibling tools like find-email or enrich-person, which handle individual data types. The verb 'generate' and resource 'lead report' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for aggregated reporting rather than individual lookups, which provides clear context. However, it does not explicitly name alternative tools or state when not to use this tool, so it falls short of full exclusion guidance.
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 must carry the burden of behavioral disclosure. It states that it uses Apollo.io and returns a full profile, but it does not disclose important constraints such as the need to provide at least one identifier (the schema marks all parameters optional) or what happens if insufficient data is given. The description also omits the 'domain' parameter, which may lead to confusion about accepted inputs.
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, consisting of just two sentences. The key action and inputs are front-loaded, and the mention of Apollo.io is a useful additional context. Every word earns its place with no redundancy.
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?
Without an output schema, the description should provide more detail about the returned 'full professional profile.' It also fails to explain that parameters are optional but at least one is presumably needed. However, the tool is simple and the description covers the primary use case, so it is adequate but not fully complete.
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, so each parameter is individually documented. The description adds a slight grouping ('name, email, or LinkedIn URL') that implies these are alternative identifiers, but it does not clarify the role of 'domain' or how parameters combine. This is baseline 3 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 verb ('Get') and resource ('full professional profile for a person'), and further specifies the input methods (name, email, or LinkedIn URL). It distinguishes itself from sibling tools like enrich-company (for companies) and find-email (for finding emails) by focusing on person enrichment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when you need a full professional profile from identifying information. However, it does not explicitly mention when not to use it or name alternative tools (e.g., search-people), so it stops short of full explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the specific checks performed (MX, SMTP, disposable, catch-all), which is useful behavioral detail. However, it does not mention whether the check sends an email, the return format, possible errors, or limitations (e.g., SMTP checks being unreliable), leaving some transparency gaps.
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, well-structured sentence that front-loads the primary action and then adds relevant detail. Every word contributes value, with no redundant or filler content.
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 tool, the description provides a clear picture of what is checked, but it omits what the output looks like. Without an output schema, the agent may not know whether the result is a boolean, a score, or a detailed report. It also does not mention any side effects, but the simple nature of the tool makes this a moderate gap rather than a severe one.
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 already describes the email parameter thoroughly ('Email address to verify'), and the description does not add additional meaning to this parameter beyond its overall purpose. Schema coverage is 100%, so the baseline of 3 is appropriate; the parameter is adequately documented but not enriched beyond the schema.
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 begins with a strong verb ('Verify') and specifies the resource ('email address') and the exact purpose ('deliverability'). It enumerates specific checks (MX, SMTP, disposable, catch-all), making its function clear and distinguishing it from sibling tools like find-email or domain-search.
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 the tool should be used when you have an existing email address to check for deliverability, but it does not explicitly state when to use this tool instead of alternatives like find-email or domain-search. There are no exclusions or alternative suggestions, so guidance is partially implied rather than explicit.
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 bears the full burden of behavioral disclosure. It only mentions 'Uses Apollo.io', which indicates an external dependency but does not disclose rate limits, permissions, or potential side effects. The read-only nature is implied but not explicit.
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?
A single, front-loaded sentence that efficiently conveys the tool's purpose and scope without redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple search nature, the description plus fully-described schema covers the essential aspects. However, the absence of an output schema and any mention of return format or pagination leaves a minor gap, preventing a perfect score.
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 describes all five parameters with 100% coverage, including formats like employeeRanges and locations. The description merely restates the criteria without adding new meaning, so the baseline of 3 is appropriate.
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 'Search for companies' with specific filter criteria (name, industry, location, employee count), using a specific verb and resource. It distinguishes itself from sibling tools like search-people by explicitly targeting companies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys a clear usage context: finding companies based on named attributes. However, it does not explicitly mention when not to use it or point to alternatives, so it falls short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the type of data returned (industry, size, funding, tech stack) but says nothing about data freshness, source, accuracy, or error behavior. This is a moderate level of transparency for a read-only enrichment tool but lacks detail on potential 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 sentence that front-loads the core action and resource, with no filler. It lists representative data fields to convey scope without wasting words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool without an output schema, the description provides a solid overview of purpose and return categories. The phrase 'and more' leaves some ambiguity about the full set of fields, but the tool's simplicity and the listed examples make it sufficiently complete for an agent to invoke 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 covers the only parameter with a description and example, achieving 100% coverage. The description adds 'for a domain' but does not provide additional semantic detail beyond what the schema already offers, so it stays at the baseline.
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 verb 'get' and resource 'comprehensive company intelligence' clearly state the tool's function. It explicitly scopes to a domain, and the example data fields (industry, size, funding, tech stack) distinguish it from sibling tools like enrich-person or find-email.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates the intended use case: when you have a domain and want company data. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to know when to select this tool over siblings like search-companies (which requires search criteria rather than a specific domain).
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:

[![leads-mcp- MCP server](https://glama.ai/mcp/servers/AtomicIntuition/leads-mcp-/badges/card.svg)](https://glama.ai/mcp/servers/AtomicIntuition/leads-mcp-)

Score Badge

Copy to your README.md:

[![leads-mcp- MCP server](https://glama.ai/mcp/servers/AtomicIntuition/leads-mcp-/badges/score.svg)](https://glama.ai/mcp/servers/AtomicIntuition/leads-mcp-)

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/AtomicIntuition/leads-mcp-'

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