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Optimize
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Live 44 Actions
TalentLyft MCP Server
for AI Agents
Connect your AI agent to StackOne's TalentLyft MCP server and give it 44 MCP tools out of the box. Auth, tool execution, and security all managed.
TalentLyft MCP Server
Built by 
StackOne
StackOne 
Coverage
44 Agent Actions
Create, read, update, and delete across TalentLyft — and extend your agent's capabilities with custom actions.
Authentication
Agent Tool Authentication
Per-user OAuth in one call. Your TalentLyft MCP server gets session-scoped tokens with zero credentials stored on your infra.
Agent Auth →
Security
Agent Protection
Every TalentLyft tool response scanned for prompt injection in milliseconds — 88.7% accuracy, all running on CPU.
Prompt Injection Defense →
Performance
Max Agent Context. Min Cost.
Free up to 96% of your agent's context window to enhance reasoning and reduce cost, on every TalentLyft call.
Tools Discovery →What is the TalentLyft MCP Server?
A TalentLyft MCP server lets AI agents read and write TalentLyft data through the Model Context Protocol — Anthropic's open standard for connecting LLMs to external tools. StackOne's TalentLyft MCP server ships with 44 pre-built actions, fully extensible via the Connector Builder — plus managed authentication, prompt injection defense, observability, and agent execution runtime. Connect it from MCP clients like Claude Desktop, Claude Code, Cursor, Goose, and VS Code, or from agent frameworks like OpenAI Agents SDK, LangChain, and Vercel AI SDK.
All TalentLyft MCP Tools
Every action from TalentLyft's API, ready for your agent. Create, read, update, and delete — scoped to exactly what you need.
Candidates
- Create Candidate
Create a new candidate
- List Candidates
Retrieve a list of candidates
- Get Candidate
Retrieve a single candidate by ID
- Update Candidate
Update an existing candidate by ID
Jobs
- Create Job
Create a new job
- List Jobs
Retrieve a list of jobs
- Get Job
Retrieve a single job by ID
- Update Job
Update an existing job by ID
- Delete Job
Delete a job by ID
Employees
- Create Employee
Create a new employee
- List Employees
Retrieve a list of employees
- Get Employee
Retrieve a single employee by ID
- Update Employee
Update an existing employee by ID
- Delete Employee
Delete an employee by ID
Pipelines
- Create Pipeline
Create a new pipeline
- List Pipelines
Retrieve a list of pipelines
- Get Pipeline
Retrieve a single pipeline by ID
- Update Pipeline
Update an existing pipeline by ID
- Delete Pipeline
Archive a pipeline by ID
Rejection Reasons
- Create Rejection Reason
Create a new rejection reason
- List Rejection Reasons
Retrieve a list of rejection reasons
- Get Rejection Reason
Retrieve a single rejection reason by ID
- Update Rejection Reason
Update an existing rejection reason by ID
- Delete Rejection Reason
Delete a rejection reason by ID
Events
- List Events
Retrieve a list of events
- Get Event
Retrieve a single event by ID
Forms
- List Forms
Retrieve a list of forms
- Get Form
Retrieve a single form by ID
Job Requisitions
- List Job Requisitions
Retrieve a list of job requisitions
- Get Job Requisition
Retrieve a single job requisition by ID
Job Tags
- Create Job Tag
Create a new job tag
- List Job Tags
Retrieve a list of job tags
- Get Job Tag
Retrieve a single job tag by ID
- Update Job Tag
Update an existing job tag by ID
- Delete Job Tag
Delete a job tag by ID
Departments
- Create Department
Create a new department
- List Departments
Retrieve a list of departments
Other (7)
- Get Application
Retrieve a specific candidate application
- Get Candidate Activities
Retrieve activity timeline for a candidate
- List Job Tags (Paged)
Retrieve a paginated list of job tags
- List Tags
Retrieve a list of tags
- List Members
Retrieve a list of team members
- Move Candidate To Stage
Move a candidate's application to a different pipeline stage
- Disqualify Candidate Application
Disqualify a candidate's application
TalentLyft AI Agent Use Cases
Connect your AI agent to TalentLyft and help your team scale the recruiting operations they run by hand today.
Job Posting Distribution
Use StackOne to connect your AI agent to your ATS and job boards to automate job posting distribution.
View Reference Check Automation
Use StackOne to connect your AI agent to your ATS, survey tools, and messaging systems to automate reference checks.
View Offer Letter Generation
Use StackOne to connect your AI agent to your ATS, HRIS, and document management tools to automate offer letter generation.
ViewSet Up Your TalentLyft MCP Server in Minutes
One endpoint. Any framework. Your agent is talking to TalentLyft in under 10 lines of code.
Agent Frameworks
JSON{
"mcpServers": {
"stackone": {
"command": "npx",
"args": [
"-y",
"mcp-remote@latest",
"https://api.stackone.com/mcp?x-account-id=<account_id>",
"--header",
"Authorization: Basic <YOUR_BASE64_TOKEN>"
]
}
}
}Check More Recruiting MCP Servers
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Platform Resources
MCP Code Mode: Keeping Tool Responses Out of Agent Context
Anthropic's code_execution processes data already in context. Custom MCP code mode keeps raw tool responses in a sandbox. 14K tokens vs 500.
11 min
Comparing BM25, TF-IDF, and Hybrid Search for MCP Tool Discovery
Benchmarking BM25, TF-IDF, and hybrid search for MCP tool discovery across 916 tools. The 80/20 TF-IDF/BM25 hybrid hits 21% Top-1 accuracy in under 1ms.
10 min
Indirect Prompt Injection Defense for MCP Tools: A Technical Guide
MCP tools that read emails, CRM records, and tickets are indirect prompt injection vectors. Here's how we built a two-tier defense that scans tool results in ~11ms.
12 min
TalentLyft MCP Server FAQ
Does StackOne have a TalentLyft MCP server?
Yes. StackOne offers a hosted TalentLyft MCP server with 44 pre-built actions, and every action is tested and QA'd by StackOne. Connect it to Claude, Cursor, and any other MCP client, or to any agent framework through the AI Action SDK. It ships with managed agent authentication, prompt injection defense, and tool discovery with server-side execution that preserve your agent's context window and keep reasoning performance.
TalentLyft MCP server vs direct API integration — what's the difference?
A TalentLyft MCP server and direct API integration serve different use cases. Direct API integration is for software-to-software — backend code calling TalentLyft. A TalentLyft MCP server is for AI agents — MCP clients like Claude and Cursor, plus framework agents built with OpenAI, LangChain, or Vercel AI — discovering and calling TalentLyft at runtime. StackOne provides both.
How does TalentLyft authentication work for AI agents?
TalentLyft authentication for AI agents works through a StackOne Connect Session. Create one via the dashboard or the SDK — you get an auth link and ready-to-paste config for Claude Desktop, Cursor, and other MCP clients. Your user authenticates their own TalentLyft account; StackOne handles token exchange, storage, and refresh. Credentials never reach the LLM, and each user is isolated via
origin_owner_id.Are TalentLyft MCP tools vulnerable to prompt injection?
Yes — TalentLyft MCP tools can be vulnerable to indirect prompt injection. Any tool that reads user-written content — documents, messages, tickets, records, or free-text fields — is a potential vector. StackOne Defender scans every tool response before it enters the agent's context — regex patterns in ~1ms, then a MiniLM classifier in ~4ms. 88.7% accuracy, CPU-only.
What is the context bloat of a TalentLyft agent and how do I avoid it?
Context bloat happens when TalentLyft tool schemas and API responses eat your TalentLyft agent's memory, preventing it from reasoning effectively. A single TalentLyft query can return a massive JSON response, and connecting multiple tools compounds the problem. Tools Discovery and Code Mode reduce context bloat — loading only relevant tools per query and keeping raw responses out of the agent's context.
Can I limit which actions my TalentLyft agent can access?
Yes — you can limit which actions your TalentLyft agent can access directly from the StackOne dashboard. Toggle actions on or off, or restrict them to specific accounts, with no code changes to your agent. Session tokens can be scoped to exact actions so if one leaks, exposure stays contained.
Can I create custom agent actions for my TalentLyft MCP server?
Yes — you can create custom agent actions for your TalentLyft MCP server using Connector Builder. It's an integration agent your coding assistant (Claude Code, Cursor, or Copilot) can invoke to research TalentLyft's API, generate production-ready connector YAML, test against the live API, and validate before you ship.
When should I NOT use a TalentLyft MCP server?
Skip a TalentLyft MCP server if your integration is purely software-to-software — direct TalentLyft API integration is simpler when no AI agent is involved. For deterministic, compliance-critical operations (financial transactions, regulatory reporting), direct API gives you predictable behavior without agent-driven decision-making. MCP shines when AI agents need to dynamically discover and call TalentLyft actions at runtime.
What AI frameworks and AI clients does the StackOne TalentLyft MCP server support?
The StackOne TalentLyft MCP server supports both. MCP clients (paste-and-go apps): Claude Desktop, Claude Code, Cursor, VS Code, Goose. Agent frameworks (code SDKs you build with): OpenAI Agents SDK, Anthropic, Vercel AI, Google ADK, CrewAI, Pydantic AI, LangChain, LangGraph, Azure AI Foundry.
Put your AI agents to work
All the tools you need to build and scale AI agent integrations, with best-in-class connectivity, execution, and security.