How to Find B2B Work Emails and Phone Numbers with AI Agents

Finding business contact fields can involve several tools, manual exports, and repeated review. AI agents connected to B2B data via MCP (Model Context Protocol) offer another workflow: describe who you need, let the agent search and filter, then request the available contact fields for the profiles you select.

This guide walks through common approaches and a hosted MCP setup step by step.

The problem with finding B2B contact data in 2026

A manual contact-data workflow often starts by opening a database, setting filters, exporting a list, and reviewing which fields are available.

The problems with this approach are well-known:

• Changing records. People change jobs, companies rebrand, and domains change. Treat any returned contact record as a point-in-time result rather than a permanent fact.
• Different billing units. Providers may charge by seat, search, record, field, or enrichment request. Compare the unit itself before comparing headline prices.
• Plan fit. Some teams want named seats and a full application; others want usage-based access for automated workflows. Neither model is automatically right for every team.
• The export-import dance.Export from database → clean in spreadsheet → import to CRM/outreach tool. Each step adds another handoff and another place to review formatting or missing fields.

None of these problems are unsolvable. The useful question is which interface and billing model remove unnecessary handoffs for your workflow.

Traditional approaches (and where they fall short)

Apollo.io

Apollo offers an application, browser extension, API, and hosted MCP access. Evaluate its current plan entitlements, field-level credit rules, and workflow features against the way your team actually works.

ZoomInfo

ZoomInfo offers enterprise sales-intelligence products as well as GTM AI tools with MCP access. Its product lines and purchasing models differ, so use the current vendor documentation and quote for the exact product you are evaluating.

Lusha

Lusha provides browser-based and application workflows for contact data. Check the current plan documentation for field availability, credit deductions, and integrations rather than assuming every field has the same cost.

Manual LinkedIn research

Manual research can work for one-off tasks, but contact fields may require a separate data source and each result still needs review before use.

The practical choice is no longer simply dashboard versus agent. Several providers support multiple interfaces. Compare the exact operations, billing unit, and fields available through the interface you plan to use.

The AI agent approach: find contacts without leaving your conversation

Here is the core idea: instead of you going to a data provider, your AI agent goes to the data provider on your behalf.

This works through MCP (Model Context Protocol), an open standard that lets AI agents call external tools. When your agent is connected to a B2B data MCP server, it can:

• Search millions of people by title, company, seniority, industry, and location
• Search companies by name, domain, headcount, industry, and tech stack
• Request available work email and direct phone fields on demand
• Do all of this inline, within the same conversation where you are planning outreach or researching accounts

The key difference is the handoff: the agent can use search results and reveal responses in the same workflow without requiring a CSV export between steps.

Step by step: connect Descovo MCP and request contact fields

Here is how to set this up with Descovo’s hosted MCP endpoint in a compatible client.

Step 1: Get your API key

Sign up at descovo.com. No credit card required. The Free plan includes 500 credits/month. Search costs 0 credits. Each reveal attempt costs 1 credit, including not-found results. When available, work email and direct phone are returned together under that credit.

Step 2: Add the MCP server to your AI client

In Claude Code, run this command in your terminal and replace the placeholder with your Descovo API key:

claude mcp add --transport http \
  --header "x-api-key: sk_live_YOUR_KEY_HERE" \
  descovo https://mcp.descovo.com/mcp

That’s it. No npm install, no Docker, no local process. Descovo runs as a hosted MCP server using the Streamable HTTP transport. Check the developer quickstart for Cursor and other clients that support static custom headers.

Claude.ai and Claude Desktop custom remote connectors require OAuth, which Descovo does not yet expose. Use Claude Code, Cursor, or another MCP client that accepts a static x-api-key header.

Step 3: Search for people

Now just talk to your agent. Try something like:

The agent uses call_operation with operationId: peopleSearch and supported filters. Search costs 0 credits, so you can narrow the list before choosing reveal attempts.

Step 4: Request contact fields

When you see someone you want to reach, ask the agent to request the available contact fields you need:

The standard reveal uses call_operation with operationId: syncQuickContactReveal. Its request goes in params.body and includes the selected profile’s LinkedIn URL, the requested email and phone fields, and email validation preference. Lite uses a lower-yield lookup. Standard balances speed and coverage. Turbo prioritizes speed, while Exhaustive trades latency for the broadest available provider coverage. Each reveal attempt costs 1 credit, including not-found results. When available, work email and direct phone are returned together under that credit.

Step 5: Use the data immediately

The Descovo search-to-reveal portion can stay inside a compatible agent conversation. You can then review the returned fields, research the company further, or pass approved results to a separate list, CRM, or outreach workflow.

Choose the reveal mode for the job

Descovo exposes multiple reveal modes so you can choose a latency and coverage tradeoff instead of assuming every request behaves the same way.

Lite uses a lower-yield lookup. Standard balances speed and coverage. Turbo prioritizes speed, while Exhaustive trades latency for the broadest available provider coverage.

No mode guarantees that a field will be found. A not-found result still represents a completed reveal attempt and costs one credit.

Descovo pricing in this workflow

Keep the billing unit explicit when estimating a workflow. Descovo counts reveal attempts, not successful fields.

On Descovo’s Starter plan, 5,000 credits/month support up to that many reveal attempts. Each reveal attempt costs 1 credit, including not-found results. When available, work email and direct phone are returned together under that credit.

For another provider, calculate the same workflow from its current plan documentation. Do not assume that a seat, email field, phone field, or enrichment request maps one-to-one across vendors.

What to look for in a B2B email and phone finder in 2026

If you are evaluating tools right now, consider these questions:

1. Reveal behavior. Ask whether a mode may return cached fields or query providers at request time, and what happens when no field is found.
2. Transparent credit model. Can you easily calculate what 10,000 reveals will cost? If you need a sales call to get pricing, that is a red flag for transparency.
3. Agent compatibility. If you are using AI agents for prospecting, check whether the provider offers a compatible hosted MCP server. MCP standardizes tool discovery and calls; an API can also be exposed to an agent through a separate tool integration.
4. Field entitlements. Confirm which work email and direct phone fields can be returned under one billing unit, and remember that availability can differ by contact.
5. No seat minimums. Usage-based pricing means your costs scale with actual usage, not headcount.

Separate search from reveal

Providers package access in different ways, including application seats, usage credits, API requests, and field-level entitlements.

MCP can separate the search step from the reveal step. In Descovo, search costs 0 credits and reveal attempts consume credits. That makes it possible to narrow a list before deciding which contact attempts are worth spending on.

Different teams will prefer different combinations of application access, seats, usage, and automation. The important part is to compare those units explicitly.

Key takeaways

• B2B data providers differ in interface, plan entitlements, field availability, and billing unit; compare current documentation for the workflow you plan to run.
• AI agents connected to B2B data via MCP can search, filter, and request available contact fields inline — no dashboard needed.
• Lite uses a lower-yield lookup. Standard balances speed and coverage. Turbo prioritizes speed, while Exhaustive trades latency for the broadest available provider coverage.
• Each reveal attempt costs 1 credit, including not-found results. When available, work email and direct phone are returned together under that credit.
• Add the hosted MCP server URL and API-key header to a compatible client; search then costs 0 credits.