Apollo.io MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some potential overlap between organization_search and organization_enrichment, as both involve company data retrieval. However, their descriptions clarify that search is for finding organizations while enrichment is for augmenting data on a known company, which helps mitigate confusion. The other tools target clearly different resources like employees, emails, job postings, and people.
Naming Consistency4/5
The naming follows a consistent snake_case pattern throughout, which is good. However, there is a minor inconsistency: most tools use a verb_noun format (e.g., organization_search, people_enrichment), but employees_of_company uses a noun_of_noun structure, deviating slightly from the pattern. This does not severely impact readability but is a noticeable deviation.
Tool Count5/5
With 7 tools, the count is well-scoped for a sales intelligence or recruitment domain, covering key operations like searching and enriching data for both organizations and people. Each tool appears to earn its place without feeling excessive or sparse, aligning well with the server's apparent purpose of data enrichment and lookup.
Completeness4/5
The tool set provides solid coverage for core workflows in the domain, including search and enrichment for both organizations and people, plus specific functions like finding employees and emails. A minor gap is the lack of update or delete operations, but this is reasonable for a read-heavy enrichment server. The surface supports common agent tasks without obvious dead ends.
Average 2.7/5 across 7 of 7 tools scored. Lowest: 1.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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

Behavior1/5
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 but offers none. It doesn't indicate whether this is a read-only operation, what authentication might be required, whether there are rate limits, what the typical response format looks like, or any other behavioral characteristics. The description is completely silent on these critical operational aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (just 7 words) but this brevity comes at the cost of being under-specified rather than efficiently informative. While it's front-loaded with the core function, it lacks the necessary detail to be genuinely helpful to an AI agent trying to understand when and how to use this tool effectively.
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?
For a search tool with 3 parameters and no output schema, the description is inadequate. While the schema covers the parameters well, the description fails to explain what kind of results to expect, how results are structured, or any limitations of the search. With no annotations and no output schema, the description should provide more operational context about what this tool actually returns.
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?
With 100% schema description coverage, the input schema already documents all three parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Use the People Search endpoint to find people' is tautological - it essentially restates the tool name 'people_search' with minimal additional information. While it indicates a search function, it lacks specificity about what kind of people search (e.g., professional contacts, employees, etc.) and doesn't distinguish it from sibling tools like 'employees_of_company' or 'people_enrichment'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. With sibling tools like 'employees_of_company', 'people_enrichment', and 'organization_search' available, there's no indication of what makes this tool distinct or when it should be preferred over those other options for finding people information.
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. It only states it 'find organizations' without disclosing behavioral traits like whether this is a read-only operation, what permissions might be needed, rate limits, pagination behavior, or what the response format looks like. For a search tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words, making it appropriately concise. However, it's front-loaded with minimal information, which could be improved with more context, but it's not verbose or poorly structured.
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 search tool. It doesn't explain what 'find' entails, the return values, or how results are structured. With 2 parameters and no behavioral context, it should provide more guidance to be fully helpful.
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 schema already documents both parameters with clear descriptions. The tool description adds no additional meaning about parameters beyond what's in the schema. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'find organizations' which gives a basic purpose, but it's vague about what 'find' means (search by what criteria?) and doesn't distinguish it from sibling tools like 'organization_enrichment' or 'people_search'. It uses the generic verb 'find' rather than specifying the search mechanism.
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?
No guidance is provided about when to use this tool versus alternatives like 'organization_enrichment' or 'people_search'. The description doesn't mention any prerequisites, context, or exclusions for usage. It's a generic statement with no operational 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 carries full burden. It states the action ('Find employees') but lacks behavioral details such as what data is returned (e.g., employee names, roles, contact info), whether it's a read-only operation, potential rate limits, authentication needs, or error handling. This leaves significant gaps for an agent to understand the tool's 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 front-loads the core purpose ('Find employees of a company') and specifies input methods. There is no wasted text, making it highly concise and well-structured 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 the complexity of a search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of employees, details), behavioral traits, or usage context relative to siblings. This leaves the agent under-informed for effective tool selection and 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?
Schema description coverage is 100%, so the schema already documents all parameters (company, website_url, linkedin_url) with descriptions. The description adds minimal value by mentioning input options but doesn't provide additional semantics like format examples, constraints, or how parameters interact (e.g., if multiple are provided). Baseline 3 is appropriate as 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: 'Find employees of a company' with specific input options (company name or website/LinkedIn URL). It uses a clear verb ('Find') and resource ('employees of a company'), but doesn't explicitly differentiate from sibling tools like 'people_search' or 'organization_search', which might have overlapping functionality.
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 'people_search' or 'organization_search'. It mentions input options but doesn't specify scenarios, prerequisites, or exclusions for usage, leaving the agent to guess about context.
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 'Get email address', which implies a read-only operation, but doesn't disclose any behavioral traits like authentication requirements, rate limits, error handling, or what happens if no email is found. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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: 'Get email address for a person using their Apollo ID'. It is front-loaded with the core action and resource, with no wasted words. Every part of the sentence contributes directly to understanding the tool's purpose, making it highly concise and well-structured.
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 (a simple lookup with one parameter) and lack of annotations or output schema, the description is incomplete. It doesn't explain what the return value looks like (e.g., email string, error messages), behavioral aspects like permissions or limitations, or how it differs from sibling tools. For a tool with no structured data beyond the input schema, more context is needed to be fully helpful.
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 meaning beyond the input schema. It mentions 'using their Apollo ID', which aligns with the 'apollo_id' parameter in the schema. Since schema description coverage is 100% (the parameter is fully described as 'Apollo.io person ID'), the baseline is 3. The description doesn't provide additional syntax, format details, or constraints, so it meets but doesn't exceed the baseline.
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: 'Get email address for a person using their Apollo ID'. It specifies the verb ('Get'), resource ('email address'), and key identifier ('Apollo ID'), making the action unambiguous. However, it does not explicitly differentiate this tool from sibling tools like 'people_enrichment' or 'people_search', which might also retrieve person-related data, so it doesn't fully distinguish from alternatives.
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 'people_enrichment' or 'people_search', nor does it specify prerequisites, such as needing an Apollo ID. The context is implied (use when you have an Apollo ID and want an email), but there's no explicit when/when-not or alternative recommendations.
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 enriches data but doesn't explain what 'enrich' entails (e.g., adding details like industry, size, location), whether it's a read-only or mutative operation, or any constraints like rate limits or authentication needs. This lack of detail makes it inadequate for a tool with potential complexity.
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 function without any fluff. It's front-loaded with the core action ('enrich data') and resource ('1 company'), making it easy to parse. Every word earns its place, achieving optimal 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 lack of annotations and output schema, the description is incomplete for a data enrichment tool. It doesn't explain what enriched data is returned (e.g., company details, metrics), how results are structured, or any behavioral traits like error handling. For a tool that likely provides rich output, this leaves significant gaps in understanding its full 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 documentation for 'domain' and 'name'. The description adds no additional parameter semantics beyond implying it processes '1 company', which aligns with the schema but doesn't provide extra context like how parameters interact or which is prioritized. 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: 'enrich data for 1 company' using the Organization Enrichment endpoint. It specifies the verb ('enrich') and resource ('data for 1 company'), making the intent unambiguous. However, it doesn't differentiate from sibling tools like 'organization_search' or 'people_enrichment', 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 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 when to prefer this over 'organization_search' for enrichment versus search, or over 'people_enrichment' for company versus individual data. There's no context about prerequisites, exclusions, or typical use cases, leaving the agent without operational 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 full burden for behavioral disclosure. It states the tool 'finds' job postings, implying a read operation, but doesn't mention permissions, rate limits, pagination, or what the response includes. This leaves significant gaps for a tool with no output schema.
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 with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose 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. It doesn't explain what the tool returns (e.g., list of postings, metadata), error conditions, or behavioral traits. For a tool with 1 parameter and 100% schema coverage, it minimally covers purpose but 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 schema already documents the 'organization_id' parameter. The description adds no additional meaning about the parameter beyond implying it's for a 'specific organization', which is redundant with the schema. Baseline 3 is appropriate when 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: 'find job postings for a specific organization' with a specific verb ('find') and resource ('job postings'). It distinguishes from siblings like 'organization_search' or 'employees_of_company' by focusing on job postings, but doesn't explicitly contrast them.
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 'organization_search' or 'people_search'. It mentions 'specific organization' but doesn't clarify prerequisites, exclusions, or comparative use cases with sibling tools.
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 enriches data for one person but doesn't describe what 'enrich' entails (e.g., adding contact info, job history, social profiles), whether it requires authentication, rate limits, or what the output looks like. This is a significant gap for a tool with no annotations or output schema.
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 core function. It's appropriately sized for a tool with a clear purpose and well-documented schema, with zero wasted words or 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 the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'enrichment' means, what data is returned, or how parameters are used. For a data enrichment tool with multiple input options and no structured output documentation, this leaves significant gaps for the agent.
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 schema already documents all 6 parameters with clear descriptions. The description adds no additional parameter semantics beyond implying these fields are used for enrichment. This meets the baseline of 3 when schema does the heavy lifting, but doesn't compensate with extra context like which parameters are most important or how they interact.
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: 'enrich data for 1 person' with the specific verb 'enrich' and resource 'person'. It distinguishes from siblings like 'employees_of_company' (multiple people) and 'get_person_email' (specific data point). However, it doesn't specify what 'enrich' means in terms of data types or sources, keeping it at 4 rather than 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 when to choose this over 'people_search' (which might find multiple people) or 'organization_enrichment' (which enriches company data). There's no context about prerequisites or exclusions, leaving the agent to guess based on tool names alone.
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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