Namely MCP. Pull employee profiles and org data in natural conversation.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Namely manages your HRIS data directly through your agent. You use it to pull employee profiles, check organizational structure, or list job titles without logging into the Namely portal.
It gives you a natural language way to track everything from work anniversaries to company announcements.
What your AI agents can do
Get profile
Retrieves detailed information for one employee profile by name or ID.
Get team
Gathers specific data points about a defined team or group of employees.
List announcements
Pulls the titles and text of recent company-wide announcements.
Get detailed information, including contact details and roles, for specific employees or groups of people.
View the full hierarchy by listing departments, teams, or specific organizational groups within the company.
Get a complete list of job titles and the salary structures defined for your organization.
Track important dates, such as birthdays or work anniversaries, from the company's event timeline.
Retrieve a list of recent, company-wide announcements posted to the Namely feed.
Ask AI about this MCP
Supported MCP Clients
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Namely MCP Server: 10 Tools for HR Data Management
Use these tools in your AI client to query every aspect of your Namely data—from individual employee profiles to full organizational structure maps.
019d75daget profile
Retrieves detailed information for one employee profile by name or ID.
019d75daget team
Gathers specific data points about a defined team or group of employees.
019d75dalist announcements
Pulls the titles and text of recent company-wide announcements.
019d75dalist events
Lists upcoming HR events like birthdays or work anniversaries for the organization.
019d75dalist fields
Shows a list of custom data fields that have been added to employee profiles.
019d75dalist groups
Lists all major organizational groups, departments, or offices in your company.
019d75dalist jobs
Provides a full list of job titles and the salary ranges defined for those roles.
019d75dalist profiles
Generates a searchable list of all employee profiles currently in the system.
019d75dalist reports
Displays available HR report templates and their functions within Namely.
019d75dalist teams
Lists all specific, smaller teams that have been defined within the organizational structure.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Namely, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You connect Namely to your agent and you get full control of all that HR data—the employee records, the structure, the job titles—without having to log into a browser or click through ten different tabs. Your agent handles it naturally.
To find out who's working where, first, you can run list_profiles which gives you a searchable list of every single employee profile in the system. From there, if you need deep details on one person, you use get_profile; this pulls detailed info for an employee by name or ID, including their contact details and specific roles.
If you're tracking smaller units, you can run list_teams to see every defined, small team. If you want data about a particular group of people—like the marketing squad—you use get_team, which gathers specific data points for that whole crew. For mapping out the overall company structure, you first call list_groups to get all major organizational groups, departments, or offices established in your company.
To understand how the company is organized by roles, you check list_jobs to pull a full list of every job title and the salary ranges defined for those positions. You can also see what custom data fields exist on profiles using list_fields, which shows you all the specific attributes that were added beyond the standard info.
For keeping tabs on key dates, you use list_events to pull up upcoming HR events like birthdays or work anniversaries for everyone in the organization. You can also check company news by calling list_announcements; this pulls a list of recent titles and text from announcements posted across the entire feed.
For managers who need to know what reports are even available, you run list_reports, which displays all the HR report templates that Namely offers and explains their specific functions. It's about more than just names; it’s about knowing what data points you can actually pull out of the system. You shouldn't have to guess how to get the info; your agent knows.
It means you never gotta jump through hoops just to find out who works in which department, or when someone's birthday is coming up. Your agent handles all those structural queries—the departments via list_groups, the smaller teams via list_teams, and even pulling specific profile data for a whole defined group using get_team.
You’ll get your answers fast.
How Namely MCP Works
- 1 Subscribe to this server and provide your Namely Company Slug and API Token.
- 2 Tell your AI agent what data you need—for example, 'List all managers in Engineering.'
- 3 Your agent runs the appropriate tool (like
list_profilesorget_team) and returns structured HR data directly into the chat.
The bottom line is: your AI client connects to Namely, handles the API calls, and presents you with clean, conversational answers about your people.
Who Is Namely MCP For?
HR Managers who are tired of navigating complex internal portals. Operations engineers who need automated employee list generation for provisioning. Team Leads who just want to quickly verify a colleague's job title without asking an admin.
Uses the server to audit organizational structures, check department membership (list_groups), or pull employee details for payroll reports.
Automates the retrieval of accurate employee lists and role data, which is necessary before setting up new user accounts or systems access.
Verifies team rosters, checks who's in a specific group (get_team), and monitors upcoming work anniversaries for recognition planning.
What Changes When You Connect
- Find out who's on the team instantly. Instead of manually checking spreadsheets, running
list_profilesor usingget_teamgives you immediate access to specific roster details. - Stay ahead of HR deadlines. Use
list_eventsto check for upcoming birthdays and anniversaries without having to open a calendar or run an internal report. - Map your company structure fast. You can use
list_groups,list_teams, andlist_jobstogether to build out the entire organizational chart, department by department. - Get official status updates immediately. The
list_announcementstool fetches all recent company news right into your chat—no need to navigate the main feed page. - Automate compliance checks. When you pull data using
get_profile, you get verified contact and role information, which is critical for provisioning new accounts.
Real-World Use Cases
Onboarding a New Team Member
The manager needs to know who the team reports to and what their official job title is. They ask the agent: 'Who manages the Marketing department?' The agent uses get_team and list_groups to reply with the correct supervisor's profile, ensuring the new hire gets routed correctly from day one.
Auditing Organizational Changes
HR needs to confirm if a newly created department ('Product Beta') has been properly recorded. They run list_groups and cross-reference it with the full list of job titles using list_jobs to ensure all roles are accounted for before launching.
Finding Contact Info Quickly
A team lead needs Elena Rodriguez's phone number immediately. Instead of searching employee directories, they ask the agent: 'What is Elena Rodriguez’s contact info?' The agent uses get_profile and delivers the exact data point in seconds.
Checking Compliance for Reports
An operations engineer must pull a list of all available reports before building one. They use list_reports to see what Namely can export, then confirm necessary custom fields using list_fields before writing the final query.
The Tradeoffs
Asking for a full Org Chart in one prompt
The user types: 'Give me the whole org chart, including who reports to whom and their salary.' This is too broad; it forces the agent to guess which tools to run.
→
Break it down. Start by running list_groups to define the major departments. Then, for a specific department, use get_team or list_profiles. If you need roles, follow up with list_jobs.
Assuming all data is in one place
The user asks: 'Tell me about the company and its employees.' This mixes general knowledge (announcements) with specific data points.
→
Be precise. If you want announcements, use list_announcements. If you want people, start with list_profiles or a department via get_team.
Trying to export raw JSON without guidance
The user asks the agent just for 'all employee data.' The system might return an unformatted dump of profiles, making it useless.
→ Ask the agent to format the output. For example: 'List all Engineering employees and summarize their roles and contact info in a markdown table.'
When It Fits, When It Doesn't
Use this if you need to query factual, structured data about people or company operations (e.g., profiles, groups, jobs). The core strength is the ability to ask complex questions that require multiple tool calls—like 'List all QA staff in the West Coast office and their roles.' Never use this server if your goal is content creation (drafting emails), data modification (updating a profile), or summarizing unstructured documents. For those tasks, you need a different agent type; Namely handles retrieval only.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Namely. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through internal HR portals for employee details sucks.
Right now, finding out who reports to whom, or verifying someone's job title, means logging into the Namely portal. You click 'Directory,' then you filter by department, maybe search by name, and if that doesn't work, you check a separate sheet for team assignments. It’s slow, it requires context switching, and it's easy to miss data.
With this MCP server, you just talk to your agent. You ask: 'Who are the Senior QA staff in Engineering?' The agent handles all those clicks behind the scenes—running `list_profiles` and filtering by role—and spits out a clean list of names, roles, and contact info. Done.
Using Namely MCP Server: Get specific data with get_profile.
If you need to confirm just one person's details—like a manager's direct phone number or their exact job title—you currently have to navigate to that employee’s profile page. It takes two clicks and assumes the link is correct.
Now, you simply tell your agent: 'Give me David Smith's contact information.' The server runs `get_profile`, pulls the data directly from Namely, and gives you exactly what you asked for. That's it.
Common Questions About Namely MCP
How do I use list_groups to find all departments? +
You ask your agent, 'What are the major groups in the company?' The server runs list_groups, which returns a comprehensive list of every department or office group defined in Namely.
Does get_profile need an employee ID? +
No. While IDs help, you can usually just ask for the profile using the person's full name (e.g., 'Get the profile for Jane Doe'). The agent handles finding the matching record.
What is the difference between list_profiles and get_team? +
list_profiles gives you a master, searchable directory of every employee in the system. get_team, however, focuses on retrieving specific details for a small, defined group or team.
How do I check company announcements using list_announcements? +
Simply ask your agent: 'What were the recent company announcements?' The server runs list_announcements and gives you a summary of titles and dates, so you know what happened without reading every post.
What authentication details does `get_profile` require to run? +
It requires a valid Namely Company Slug and an API Token. You must set these credentials within your AI client's connection settings for the tool call to succeed.
If I use `get_team` but the team name is incorrect, how does the system handle it? +
The agent will return a specific 'Team Not Found' error. This means the provided team name doesn't match any active group or department in your Namely account.
Does `list_profiles` handle large datasets, like thousands of employees? +
Yes, it manages pagination automatically. If the number of profiles exceeds a single batch limit, your agent will make subsequent calls using cursor tokens until every employee record is retrieved.
How can I see what custom data fields are available before trying to query them? +
Use list_fields. This tool lists all custom attributes defined in your Namely account. It shows the exact keys you need to reference when querying specific profile details.
How do I get a Namely API Token? +
You can generate a Personal Access Token in your Namely account under API > Personal Access Tokens. Ensure your user has the appropriate permissions to view the data you want to retrieve.
Can I see salary information through this server? +
This depends on the permissions of the user who generated the API token. If your Namely user can see salary data, the get_profile and list_jobs tools will return that information.
What is the Company Slug? +
The slug is the unique part of your Namely URL. For example, if you log in at 'acme.namely.com', your slug is 'acme'.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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