Supports configuration through .env files, allowing users to store their HubSpot API access tokens and other environment variables securely for development purposes.
Available through GitHub repository, allowing users to clone and extend the server for additional HubSpot API capabilities.
Provides comprehensive tools for interacting with HubSpot CRM data, including creating and managing contacts and companies, retrieving company activity history and engagement timelines, accessing recent engagement data, getting lists of recently active companies and contacts, and performing CRM operations.
Integrates with npm packages, particularly mcp-proxy-auth for authentication, enabling secure API key verification for the SSE server.
Built with TypeScript, providing type safety and modern JavaScript features for the server implementation.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@HubSpot MCP Server create a new contact for Jane Smith at TechCorp with email jane.smith@techcorp.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A powerful Model Context Protocol (MCP) server implementation for seamless HubSpot CRM integration, enabling AI assistants to interact with your HubSpot data.
This MCP server provides a comprehensive set of tools for interacting with the HubSpot CRM API, allowing AI assistants to:
Create and manage contacts and companies in your HubSpot CRM
Retrieve detailed company activity history and engagement timelines
Access recent engagement data across your entire HubSpot instance
Get lists of recently active companies and contacts
Perform CRM operations without leaving your AI assistant interface
Related MCP server: HubSpot MCP Server
Seamless AI Integration: Connect your AI assistants directly to your HubSpot CRM data
Simplified CRM Operations: Perform common HubSpot tasks through natural language commands
Real-time Data Access: Get up-to-date information from your HubSpot instance
Secure Authentication: Uses HubSpot's secure API token authentication
Extensible Design: Easily add more HubSpot API capabilities as needed
# Clone the repository
git clone https://github.com/lkm1developer/hubspot-mcp-server.git
cd hubspot-mcp-server
# Install dependencies
npm install
# Build the project
npm run buildThe server requires a HubSpot API access token. You can obtain one by:
Going to your HubSpot Developer Account
Creating a private app with the necessary scopes (contacts, companies, engagements)
Copying the generated access token
You can provide the token in two ways:
As an environment variable:
HUBSPOT_ACCESS_TOKEN=your-access-tokenAs a command-line argument:
npm start -- --access-token=your-access-tokenFor development, create a .env file in the project root to store your environment variables:
HUBSPOT_ACCESS_TOKEN=your-access-token# Start the server
npm start
# Or with a specific access token
npm start -- --access-token=your-access-token
# Run the SSE server with authentication
npx mcp-proxy-auth node dist/index.jsThe SSE server uses the mcp-proxy-auth package for authentication. To implement authentication:
Install the package:
npm install mcp-proxy-authSet the AUTH_SERVER_URL environment variable to point to your API key verification endpoint:
export AUTH_SERVER_URL=https://your-auth-server.com/verifyRun the SSE server with authentication:
npx mcp-proxy-auth node dist/index.jsThe SSE URL will be available at:
localhost:8080/sse?apiKey=apikeyReplace apikey with your actual API key for authentication.
The mcp-proxy-auth package acts as a proxy that:
Intercepts requests to your SSE server
Verifies API keys against your authentication server
Only allows authenticated requests to reach your SSE endpoint
This MCP server is designed to work with AI assistants that support the Model Context Protocol. Once running, the server exposes a set of tools that can be used by compatible AI assistants to interact with your HubSpot CRM data.
The server exposes the following powerful HubSpot integration tools:
hubspot_create_contact
Create a new contact in HubSpot with duplicate checking
Parameters:
firstname (string, required): Contact's first name
lastname (string, required): Contact's last name
email (string, optional): Contact's email address
properties (object, optional): Additional contact properties like company, phone, etc.
Example:
{
"firstname": "John",
"lastname": "Doe",
"email": "john.doe@example.com",
"properties": {
"company": "Acme Inc",
"phone": "555-123-4567",
"jobtitle": "Software Engineer"
}
}hubspot_create_company
Create a new company in HubSpot with duplicate checking
Parameters:
name (string, required): Company name
properties (object, optional): Additional company properties
Example:
{
"name": "Acme Corporation",
"properties": {
"domain": "acme.com",
"industry": "Technology",
"phone": "555-987-6543",
"city": "San Francisco",
"state": "CA"
}
}hubspot_get_company_activity
Get comprehensive activity history for a specific company
Parameters:
company_id (string, required): HubSpot company ID
Returns detailed engagement data including emails, calls, meetings, notes, and tasks
hubspot_get_recent_engagements
Get recent engagement activities across all contacts and companies
Parameters:
days (number, optional, default: 7): Number of days to look back
limit (number, optional, default: 50): Maximum number of engagements to return
Returns a chronological list of all recent CRM activities
hubspot_get_active_companies
Get most recently active companies from HubSpot
Parameters:
limit (number, optional, default: 10): Maximum number of companies to return
Returns companies sorted by last modified date
hubspot_get_active_contacts
Get most recently active contacts from HubSpot
Parameters:
limit (number, optional, default: 10): Maximum number of contacts to return
Returns contacts sorted by last modified date
hubspot_update_contact
Update an existing contact in HubSpot (ignores if contact does not exist)
Parameters:
contact_id (string, required): HubSpot contact ID to update
properties (object, required): Contact properties to update
Example:
{
"contact_id": "12345",
"properties": {
"email": "updated.email@example.com",
"phone": "555-987-6543",
"jobtitle": "Senior Software Engineer"
}
}hubspot_update_company
Update an existing company in HubSpot (ignores if company does not exist)
Parameters:
company_id (string, required): HubSpot company ID to update
properties (object, required): Company properties to update
Example:
{
"company_id": "67890",
"properties": {
"domain": "updated-domain.com",
"phone": "555-123-4567",
"industry": "Software",
"city": "New York",
"state": "NY"
}
}The server is designed to be easily extensible. To add new HubSpot API capabilities:
Add new methods to the HubSpotClient class in src/hubspot-client.ts
Register new tools in the setupToolHandlers method in src/index.ts
Rebuild the project with npm run build
This project is licensed under the MIT License - see the LICENSE file for details.
HubSpot, CRM, Model Context Protocol, MCP, AI Assistant, TypeScript, API Integration, HubSpot API, CRM Integration, Contact Management, Company Management, Engagement Tracking, AI Tools
Create a new company in HubSpot
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Company name | |
| properties | No | Additional company properties |
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 creates a company but lacks critical details: it doesn't specify required permissions, whether the operation is idempotent, potential side effects (e.g., triggering workflows), or error handling. This is inadequate for a mutation 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens on success (e.g., returns a company ID) or failure, nor does it cover behavioral aspects like authentication needs or rate limits, leaving significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters (name and properties). The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and resource ('new company in HubSpot'), making the purpose immediately understandable. It distinguishes from siblings like hubspot_update_company (update vs. create) and hubspot_get_active_companies (get vs. create), though it doesn't explicitly mention these distinctions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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., authentication), when not to use it, or compare it to siblings like hubspot_update_company for existing companies, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new contact in HubSpot
| Name | Required | Description | Default |
|---|---|---|---|
| firstname | Yes | Contact's first name | |
| lastname | Yes | Contact's last name | |
| No | Contact's email address | ||
| properties | No | Additional contact properties |
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 creates a contact, implying a write operation, but doesn't cover critical aspects like required permissions, whether the operation is idempotent, error handling for duplicate emails, or what the response looks like (e.g., success/failure, contact ID). This leaves significant gaps for an agent to use it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration, earning a top score for brevity and clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a write operation with no annotations and no output schema, the description is insufficient. It doesn't explain the behavioral traits (e.g., mutation effects, error cases) or what to expect upon success, leaving the agent without key context needed for reliable tool invocation in a HubSpot environment.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting all four parameters (firstname, lastname, email, properties) with their types and purposes. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline of 3 for adequate coverage without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and resource ('new contact in HubSpot'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'hubspot_update_contact' beyond the creation vs. update distinction, which is implicit but not explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'hubspot_update_contact' for existing contacts or 'hubspot_get_active_contacts' for retrieval. It also doesn't mention prerequisites, such as needing HubSpot access or when creation is appropriate versus other operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get most recently active companies from HubSpot
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of companies to return (default: 10) |
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 mentions 'most recently active' but doesn't specify what 'active' means, how recency is determined, or any limitations like rate limits, permissions required, or pagination behavior. 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.
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 action and resource, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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. It doesn't explain what 'active' entails, how results are ordered, or what data is returned, which are critical for a tool that fetches data. This leaves the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter well-documented. The description doesn't add any additional meaning beyond the schema, such as explaining the 'active' criteria or default behavior, so it meets the baseline of 3 without compensating for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('most recently active companies from HubSpot'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'hubspot_get_company_activity' or 'hubspot_get_active_contacts', which would require more specificity to earn a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings like 'hubspot_get_company_activity' and 'hubspot_get_active_contacts', the description lacks context on selection criteria, such as whether this tool is for recent activity versus detailed activity tracking or contacts versus companies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get most recently active contacts from HubSpot
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of contacts to return (default: 10) |
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 mentions retrieving 'most recently active contacts' but doesn't explain what 'active' means, how recency is determined, whether this is a read-only operation, what permissions are required, or how results are formatted. This leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 unnecessary words. It's appropriately sized for a simple retrieval tool and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes 'active' contacts, how results are sorted, what fields are returned, or any limitations. Given the lack of structured metadata, the description should provide more operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'limit' parameter clearly documented. The description doesn't add any parameter-specific information beyond what the schema provides, which is acceptable given the high schema coverage. The baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('most recently active contacts from HubSpot'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'hubspot_get_company_activity' or 'hubspot_get_recent_engagements', 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.
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. There are multiple sibling tools for retrieving HubSpot data, but no indication of when this specific 'active contacts' tool is appropriate versus other contact or activity-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get activity history for a specific company
| Name | Required | Description | Default |
|---|---|---|---|
| company_id | Yes | HubSpot company ID |
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 it's a 'Get' operation, implying read-only behavior, but doesn't clarify permissions, rate limits, pagination, error handling, or what 'activity history' entails (e.g., types of activities, date ranges). This leaves significant gaps 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.
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 appropriately sized for a simple tool, with every word contributing to clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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. It doesn't explain what 'activity history' includes (e.g., engagement types, timestamps), return format, or error scenarios. For a tool that could involve complex data retrieval, this leaves the agent under-informed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 'company_id' well-documented in the schema as 'HubSpot company ID'. The description doesn't add any parameter-specific details beyond implying the tool acts on a company, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get activity history') and target resource ('for a specific company'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'hubspot_get_recent_engagements' which might also retrieve activity-related data, keeping it from 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.
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 a valid company ID), exclusions, or comparisons to siblings like 'hubspot_get_active_companies' or 'hubspot_get_recent_engagements', leaving the agent with minimal context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get recent engagement activities across all contacts and companies
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to look back (default: 7) | |
| limit | No | Maximum number of engagements to return (default: 50) |
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[s] recent engagement activities', which implies a read-only operation, but it doesn't disclose any behavioral traits such as authentication requirements, rate limits, pagination, or what constitutes an 'engagement activity'. This leaves significant gaps 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a simple tool and earns its place by clearly stating what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and no output schema, the description is incomplete. It doesn't explain what 'engagement activities' entail, the format of the return data, or any error conditions. For a tool that retrieves data across multiple entities, more context is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both parameters (days and limit), including defaults. The description doesn't add any parameter semantics beyond what the schema provides, such as explaining what 'engagements' include or how the lookback period works. This meets the baseline of 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'recent engagement activities across all contacts and companies', making the purpose specific and understandable. It distinguishes this tool from siblings like hubspot_get_company_activity by specifying 'across all contacts and companies' rather than focusing on a single entity, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving recent engagement activities, but it doesn't provide explicit guidance on when to use this tool versus alternatives like hubspot_get_company_activity or hubspot_get_active_contacts. No exclusions or prerequisites are mentioned, leaving the agent to infer context based on the tool's name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing company in HubSpot (ignores if company does not exist)
| Name | Required | Description | Default |
|---|---|---|---|
| company_id | Yes | HubSpot company ID to update | |
| properties | Yes | Company properties to update |
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 reveals one important behavioral trait ('ignores if company does not exist'), which is valuable context not in the schema. However, it doesn't disclose other critical behaviors: whether this is a partial or full update, what permissions are required, whether changes are reversible, rate limits, or what happens when properties are invalid. For a mutation tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that communicates the core purpose and one key behavioral constraint. Every word earns its place with no redundancy or fluff. The structure is front-loaded with the primary action and includes the important qualifier at the end.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations and no output schema, the description provides minimal but essential context about the update operation and its behavior with non-existent companies. However, it doesn't address what the tool returns, error conditions, authentication requirements, or how property updates interact with existing data. For a tool that modifies business data, this leaves important gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain property format expectations, validation rules, or provide examples. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Update') and resource ('an existing company in HubSpot'), making the purpose immediately understandable. It distinguishes from 'hubspot_create_company' by specifying it updates existing records rather than creating new ones. However, it doesn't explicitly differentiate from 'hubspot_update_contact' beyond the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context by stating it 'ignores if company does not exist', which implies it should only be used when you know the company exists or want a no-op behavior for missing companies. However, it doesn't explicitly guide when to use this versus alternatives like 'hubspot_create_company' or provide any prerequisites about required permissions or data formats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing contact in HubSpot (ignores if contact does not exist)
| Name | Required | Description | Default |
|---|---|---|---|
| contact_id | Yes | HubSpot contact ID to update | |
| properties | Yes | Contact properties to update |
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 adds useful context by stating 'ignores if contact does not exist', which clarifies idempotent behavior. However, it lacks details on permissions, rate limits, response format, or whether updates are partial/complete, leaving gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that is front-loaded with the core purpose and includes a key behavioral note. There is no wasted text, and it effectively communicates essential information in minimal words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a mutation operation with no annotations and no output schema, the description is moderately complete. It covers the basic purpose and one behavioral trait but lacks details on permissions, error responses, or update semantics. For a 2-parameter tool with high schema coverage, it's adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 ('contact_id' and 'properties') adequately. The description doesn't add any additional meaning or examples beyond what the schema provides, such as property format or validation rules. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Update') and resource ('an existing contact in HubSpot'), making the purpose specific and understandable. It distinguishes from sibling tools like 'hubspot_create_contact' by focusing on updates rather than creation. However, it doesn't explicitly differentiate from 'hubspot_update_company', which is a minor gap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'ignores if contact does not exist', which suggests it's for updating existing contacts only. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'hubspot_create_contact' or 'hubspot_update_company', nor does it mention prerequisites or error handling beyond the ignore behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
hubspot_create_companyhubspot_create_contacthubspot_get_active_companieshubspot_get_active_contactshubspot_get_company_activityhubspot_get_recent_engagementshubspot_update_companyhubspot_update_contactEach tool has a clearly distinct purpose targeting specific resources (companies, contacts, engagements) and actions (create, get, update). The 'get' tools differentiate by scope (active vs. specific activity vs. recent engagements), eliminating ambiguity. No tools appear to do the same thing, making misselection unlikely.
All tools follow a consistent 'hubspot_verb_noun' pattern with snake_case throughout. The verbs (create, get, update) are used predictably across resources, and the naming structure is uniform, making the set highly readable and predictable for agents.
With 8 tools, this server is well-scoped for HubSpot CRM operations, covering core entities (companies, contacts, engagements) with essential CRUD actions. Each tool earns its place without bloat, providing a focused yet functional surface for typical agent workflows.
The toolset offers strong coverage for companies and contacts with create, get (active/specific), and update operations, plus engagement retrieval. Minor gaps include no delete operations and limited get options (e.g., no general get_company or get_contact by ID), but agents can work around these with the provided tools.
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Marketo MCP server for AI. 130 tools to operate Marketo from Claude, Cursor, or ChatGPT.
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