search_talents tool lets your AI assistant search the Kalent talent database using a simple natural language prompt. Describe who you are looking for in plain text — the system extracts the right search filters automatically. It returns up to 5 matching profiles with full career details.
How it works
- You describe the talent you need in everyday language.
- The MCP server converts your prompt into structured search filters using AI.
- The search engine finds matching profiles and returns the results.
- The AI presents a formatted table and can answer follow-up questions using the full profile data.
Input
The tool accepts a singleprompt parameter — a natural language description of the talent you are looking for.
| Parameter | Type | Required | Description |
|---|---|---|---|
prompt | string | Yes | Natural language description of the talent you need. |
relatedSearchTransactionIds | string[] | No | Array of searchTransactionId values from previous calls for pagination (see Pagination). |
Prompt examples
- “I need a senior React developer in Paris with 5+ years of experience”
- “Find me product managers who speak French and have worked at a startup”
- “Software engineers in London with AWS certifications and fintech experience”
- “UX designers with 10+ years of experience who have worked at a YC startup”
Response
Each search returns two content blocks:1. Structured JSON
Complete profile data for each matching talent, including:searchTransactionId— unique identifier for this search (used for pagination)estimationCount— the total estimated number of talents matching the filters in the database, not just the ones returned in this batchcredits— credits consumed by this search and remaining balance- Full name, location, headline
- Current job title and organization
- Work experiences (company, title, dates, description)
- Education (school, degree, major, dates)
- Skills
- Languages and proficiency levels
- Professional certifications
- LinkedIn profile URL
searchTransactionId is used internally for pagination (see below). The estimationCount helps convey how large the overall talent pool is for the given criteria.
2. Markdown Summary Table
A formatted table displayed directly in the conversation:Errors
If the search engine times out, the tool returnsisError: true with a message asking you to try again and a ref: line containing the request debugTrackingCode for support.
Credits
Each search consumes API search credits. The number of credits consumed corresponds to the number of profiles returned (up to 5 per search). The response includes acredits object showing how many credits were used and how many remain.
If you run out of credits, the tool will return an error prompting you to purchase more at https://app.kalent.ai/settings/billing.
Pagination
When you ask for more results, the AI automatically handles pagination by passing previoussearchTransactionId values via the relatedSearchTransactionIds parameter. Talents already returned in those earlier searches are excluded from the new results, so you always see fresh profiles.
You: Find me React developers in Paris. [5 results displayed — estimationCount: 120] You: Show me more. [5 new, different results displayed — no duplicates from the first batch]The AI collects
searchTransactionId values from each response and passes them in subsequent requests so you never see the same talent twice. The estimationCount tells you how many total matches exist, helping you decide whether to paginate further.
Non-deterministic Results
Example Conversations
Finding engineers with specific company experience
You: Find software engineers who have worked at OpenAI for at least 3 years. Assistant: I’ll search for software engineers with OpenAI experience and at least 3 years in the role. [Results displayed as a table with matching profiles] I found several software engineers who match your criteria. Would you like me to look into any of these profiles in more detail, or would you like to refine the search?
Location-based search with skills
You: I need to find talent in Paris (within 20km radius) with expertise in SEO and marketing strategies. Assistant: Let me search for marketing professionals near Paris with SEO expertise. [Results displayed] Here are professionals in the Paris area with SEO and marketing strategy skills. Several of them also have experience in content marketing and digital analytics.
Combining experience, role, and company type
You: Find talent with 10+ years of experience as a UX designer who has worked for a YC startup. Assistant: I’ll search for senior UX designers with Y Combinator startup experience. [Results displayed] I found UX designers with 10+ years of experience and ties to Y Combinator startups. Want me to narrow down by location or specific skills?
Tips for Better Results
Be specific about requirements
Mention specific job titles, companies, locations, and skills. The more
precise you are, the better the results.
Use follow-up prompts
Refine your search iteratively. Ask to narrow down by adding criteria, or
broaden by relaxing constraints.
Combine multiple criteria
You can combine as many criteria as needed in a single prompt: job title +
location + skills + experience + company type.
Watch your credits
Each search consumes credits based on the number of profiles returned. Check
your remaining balance at any time in the billing settings.