Apollo.io MCP Server

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific Apollo.io API endpoints: employees_of_company finds employees, get_person_email retrieves emails, organization_enrichment and people_enrichment enrich data, organization_job_postings finds job postings, and organization_search and people_search perform searches. There is no overlap in functionality, making tool selection unambiguous.
Naming Consistency4/5
The tools follow a consistent snake_case naming convention, but the pattern is not fully uniform: most use a verb_noun structure (e.g., organization_search, people_enrichment), while employees_of_company and get_person_email deviate slightly in phrasing. Overall, naming is readable and mostly predictable with minor inconsistencies.
Tool Count5/5
With 7 tools, the server is well-scoped for its purpose of accessing Apollo.io's data enrichment and search APIs. Each tool serves a specific function, covering key operations like searching, enriching, and retrieving data for organizations and people, without being overly sparse or bloated.
Completeness4/5
The tool set provides comprehensive coverage for core Apollo.io use cases, including search and enrichment for both organizations and people, plus specific utilities like email retrieval and job postings. A minor gap exists in lacking update or deletion tools, but this is reasonable given the API's likely read-only nature, and agents can work effectively with the provided operations.
Average 2.8/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
No annotations are provided, so the description carries full burden. It states the tool 'enrich data' but doesn't disclose behavioral traits like whether it's a read-only operation, what data sources it uses, rate limits, authentication requirements, or what 'enrich' entails. The description is too minimal 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with zero wasted words, making it appropriately concise. However, it lacks front-loading of critical information and could benefit from more structure to clarify the tool's value.
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, no output schema, and a vague purpose, the description is incomplete. It doesn't explain what 'enrich' means, what data is returned, or how this tool fits among siblings. For a tool with 2 parameters and complex sibling relationships, more context is needed.
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 (domain and company name). The description adds no additional meaning about these parameters, such as whether both are required, their relationship, or examples. With high schema coverage, baseline 3 is appropriate.
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 'enrich data for 1 company', which provides a general purpose but lacks specificity. It doesn't clearly distinguish what 'enrich' means or how it differs from sibling tools like organization_search or people_enrichment. The purpose is vague rather than tautological.
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 on when to use this tool versus alternatives like organization_search or people_enrichment. The description mentions it's for '1 company' but doesn't specify use cases, prerequisites, or exclusions. Without any contextual direction, the agent must infer usage.
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 but offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, what authentication might be needed, rate limits, or what the search returns (e.g., partial matches, exact matches). The phrase 'find organizations' implies a query operation but lacks detail.
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, efficient sentence with no wasted words. It's front-loaded with the core purpose, though it could be more informative. The brevity is appropriate but borders on under-specification.
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 2 parameters and no annotations or output schema, the description is incomplete. It doesn't explain what the search returns, how results are filtered, or any limitations. Given the complexity and lack of structured data, more context is needed 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 both parameters fully. The description adds no meaning beyond what the schema provides—it doesn't explain how the parameters interact (e.g., AND/OR logic) or provide examples. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'find organizations' which is clear but vague. It specifies the endpoint name but doesn't differentiate from sibling tools like 'organization_enrichment' or 'people_search'. The verb 'find' is generic rather than specific about 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 on when to use this tool versus alternatives. The description doesn't mention sibling tools like 'organization_enrichment' or 'people_search', nor does it specify appropriate contexts or exclusions for using this search endpoint.
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 but only states it 'find[s] people' without disclosing behavioral traits such as search scope, result limits, authentication needs, or rate limits. It lacks details on what the tool actually returns or how it behaves operationally.
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, efficient sentence with no wasted words. It's front-loaded and to the point, though it could benefit from more detail given the lack of annotations and output schema.
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, no output schema, and a vague description, the tool definition is incomplete. It doesn't explain what 'find[s] people' entails in terms of results, format, or limitations, leaving significant gaps for a search tool with three parameters.
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 parameters are documented in the schema. The description adds no meaning beyond the schema, as it doesn't explain parameter usage, interactions, or examples. Baseline 3 is appropriate since the schema handles parameter documentation adequately.
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[s] people' which gives a vague purpose without specifying how it differs from sibling tools like 'people_enrichment' or 'organization_search'. It mentions the endpoint name but doesn't clarify the specific verb-resource combination beyond basic searching.
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 on when to use this tool versus alternatives like 'people_enrichment' or 'organization_search'. The description only states what it does without context, 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.
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 what the tool does but doesn't cover critical aspects like whether it's a read-only operation, potential rate limits, authentication needs, or what the output format might be (e.g., list of employees with details). This leaves significant gaps for an agent to understand how to handle the tool effectively.
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 and input options without any 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 complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., employee names, roles, contact info) or any behavioral traits, leaving the agent with insufficient information to use the tool confidently in varied contexts.
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%, so the input schema already documents all parameters clearly. The description adds minimal value by mentioning the input types (company name or URLs), but it doesn't provide additional context like format examples or usage tips beyond what's in the schema, aligning with the baseline score 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 with a specific verb ('Find') and resource ('employees of a company'), and it specifies the input types (company name or URLs). However, it 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 the input types but doesn't specify scenarios or exclusions, leaving the agent to infer usage based on context alone.
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 function but doesn't describe traits like whether it's read-only (implied by 'Get'), potential rate limits, authentication needs, error handling, or response format. For a tool with zero annotation coverage, this is a significant gap, as the agent lacks critical operational 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 front-loads the core purpose ('Get email address for a person') and specifies the key requirement ('using their Apollo ID'). There is zero waste or redundancy, making it easy for an AI agent to parse quickly. Every word earns its place, adhering to best practices for 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 tool's complexity (simple lookup with one parameter) and the absence of annotations and output schema, the description is incomplete. It covers the basic purpose but lacks behavioral details (e.g., what happens if the ID is invalid), usage guidelines, and output information. For a tool with no structured support, the description should provide more context to ensure reliable agent operation.
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 'apollo_id' parameter fully documented as 'Apollo.io person ID'. The description adds no additional semantic meaning beyond this, such as format examples or constraints. According to the rules, with high schema coverage (>80%), the baseline score is 3, as the schema does the heavy lifting and the description doesn't compensate with extra insights.
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 email address') and resource ('for a person'), specifying the required identifier ('using their Apollo ID'). It distinguishes from siblings like people_search or people_enrichment by focusing on retrieving a specific piece of contact information rather than broader search or enrichment operations. However, it doesn't explicitly contrast with all siblings, such as employees_of_company, which might also retrieve email data in a different context.
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 prerequisites (e.g., needing an Apollo ID), exclusions (e.g., not for bulk lookups), or direct comparisons to siblings like people_enrichment (which might include email among other data). Usage is implied by the tool name and description but not explicitly stated, leaving gaps for an AI agent to infer 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 'find[s] job postings' but doesn't describe how it behaves: e.g., whether it returns a list, supports pagination, requires authentication, has rate limits, or what happens if no job postings exist. For a read operation with zero annotation coverage, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized (one sentence) and front-loaded with the core purpose. It avoids redundancy and wastes no words, making it efficient. However, it could be slightly more structured by separating purpose from context, but this is minor.
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 read operation with one parameter) and lack of annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of job postings, error handling), behavioral traits, or usage context. For a tool with no structured output information, the description should provide more completeness to guide the agent 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 description adds minimal meaning beyond the input schema. It implies the 'organization_id' parameter is used to specify the organization, but the schema already has 100% coverage with a clear description ('Apollo.io organization ID'). No additional details on parameter usage, constraints, or examples are provided. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.
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 resource (job postings) and target (organization). It distinguishes from siblings like 'employees_of_company' or 'organization_search' by focusing on job postings rather than employees or general organization data. However, it doesn't explicitly contrast with all siblings (e.g., 'people_search' might also find job-related data).
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 prerequisites (e.g., needing an organization ID), exclusions (e.g., not for personal job searches), or comparisons to sibling tools like 'organization_search' (which might return broader organization data). The phrase 'for a specific organization' implies a context but lacks explicit usage rules.
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 enriches data for one person but doesn't describe what enrichment means, what data is returned, whether it's a read-only lookup or requires authentication, rate limits, or error conditions. 'Enrich' implies adding data, but the behavioral characteristics are undefined.
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. Every word earns its place: 'Use' establishes action, 'People Enrichment endpoint' identifies the tool, 'enrich data' specifies the operation, and 'for 1 person' clarifies scope. No wasted words or redundant 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?
For a tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'enrichment' produces, how parameters should be prioritized, what happens with partial information, or error handling. The context signals indicate complexity that the description doesn't address, leaving significant gaps for agent understanding.
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 fully documents all 6 parameters. The description adds no parameter-specific information beyond implying these are inputs for person enrichment. This meets the baseline of 3 when schema coverage is high, but the description doesn't explain how parameters interact (e.g., minimal required fields) or provide usage examples.
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 verb 'enrich' and resource 'person'. It distinguishes from siblings like 'employees_of_company' (multiple people) and 'organization_enrichment' (different resource), but doesn't explicitly differentiate from 'people_search' which might also enrich data. The purpose is specific but could be more precise about what enrichment entails.
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 prerequisites, when to choose this over 'people_search' or 'get_person_email', or what input combinations yield best results. The agent must infer usage from the tool name and parameters 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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