5 Agentic AI Workflows That Need B2B Data (And How MCP Makes Them Work)

Agentic AI describes systems that can call tools and take actions, rather than only generating text. Those agents are constrained by the data and operations they can access. An AI agent without connected data sources has limited context for operational work. One useful input for business agents is structured B2B data: who works where, which contact fields are available, what companies are hiring, how large they are. MCP (Model Context Protocol) provides a standard interface that compatible agents can use to connect to this data. APIs and other tool interfaces are also valid integration paths. Here are five workflows that illustrate that connection.

Why agents need B2B data (and how MCP can connect it)

AI agents can reason, plan, and take action. But reasoning without data is just speculation. For any business workflow involving people or companies — sales, recruiting, marketing, research, fundraising — the agent needs access to four categories of information:

• Who works at which company — names, titles, seniority, departments
• How to contact them — available work email and direct phone fields
• Company firmographics — documented fields such as size, industry, and location
• Hiring signals — open roles that can provide a hiring signal for review

Without this data, an agent may draft copy but cannot run a structured people search or request contact fields. It can outline a market-research plan but lacks the connected operations to execute it.

MCP gives compatible agents a standard way to access this data. Descovo’s call_operation tool executes operation IDs such as peopleSearch, companySearch, jobPostingSearch, and syncQuickContactReveal.

For a deeper explanation of how MCP works, see What Is MCP (Model Context Protocol) and Why It Matters for B2B Data.

1. Sales prospecting: from ICP to outbound list

The problem

Building prospect lists manually can involve logging into multiple tools, setting filters across different UIs, exporting CSVs, deduplicating records, and importing into yet another system. Those handoffs add review work before a list is ready to use.

How the agent solves it

The agent takes an ICP description in natural language, translates it to structured filters, searches 700M+ professional profiles, filters by documented fields such as role, industry, and location, requests available contact fields for the matches you approve, and structures the results in the same conversation.

The agent translates that prompt into a structured MCP call:

{
  "tool": "call_operation",
  "arguments": {
    "operationId": "peopleSearch",
    "params": {
      "body": {
        "searchParams": {
          "jobTitleV3": {
            "include": [{
              "type": "functional",
              "seniority": ["vp"],
              "keywords": ["marketing"]
            }]
          },
          "industry": { "anyOf": ["Software Development"] },
          "location": { "include": ["United States"] }
        },
        "pageSize": 30
      }
    }
  }
}

For an approved profile, the standard reveal uses:

{
  "tool": "call_operation",
  "arguments": {
    "operationId": "syncQuickContactReveal",
    "params": {
      "body": {
        "linkedinUrl": "https://www.linkedin.com/in/example-profile",
        "enrichmentType": {
          "getWorkEmails": true,
          "getPersonalEmails": false,
          "getPhoneNumbers": true
        },
        "validateEmails": true
      }
    }
  }
}

The operation can return matching profiles up to the chosen limit. If you pick 15 profiles, 15 reveal attempts cost 15 credits, including any not-found results. When available, work email and direct phone are returned together under each attempt’s credit.

For the full deep dive on this workflow, see How to Build an AI Sales Prospecting Agent with MCP and B2B Data.

2. Recruiting: source passive candidates with fewer manual handoffs

The problem

A recruiting pipeline can span profile search, review, contact-data lookup, and ATS entry. In a manual version it looks like this: search → profile review → export to spreadsheet → email finder tool → ATS import. Each step is manual and each handoff can lose context.

How the agent solves it

The agent searches by title keywords, seniority, location, and company. It returns candidate profiles with the fields available in the search response. When you find someone worth reaching out to, the agent can request available contact fields on demand. Each reveal attempt costs 1 credit, including not-found results. When available, work email and direct phone are returned together under that credit.

{
  "tool": "call_operation",
  "arguments": {
    "operationId": "peopleSearch",
    "params": {
      "body": {
        "searchParams": {
          "jobTitleV3": {
            "include": [{
              "type": "functional",
              "seniority": ["senior"],
              "keywords": ["data engineering"]
            }]
          },
          "industry": { "anyOf": ["Hospitals and Health Care"] },
          "location": { "include": ["London"] }
        },
        "pageSize": 25
      }
    }
  }
}

The agent can return matching candidate profiles in the same conversation and request contact fields for the shortlist. Returned fields depend on availability, and each reveal request follows the same attempt-based billing rule.

For the full recruiting workflow, see AI Recruiting Agent: How to Find and Reach Candidates.

3. Market research: map competitive landscapes on demand

The problem

Understanding a competitive landscape means researching companies, leadership teams, and hiring patterns, then keeping the evidence and assumptions organized as the market changes.

How the agent solves it

The agent uses call_operation with operationId: companySearch to find matching companies in a sector, then operationId: peopleSearch to identify leadership teams at each one. Cross-reference with operationId: jobPostingSearch to collect open-role data as a possible hiring signal. Job postings can suggest areas to investigate, but they do not prove a company’s strategy, budget, or future plans. The agent can assemble a reviewable landscape for further validation.

One leadership-search call can use this documented shape:

{
  "tool": "call_operation",
  "arguments": {
    "operationId": "paginatedCombinedSearch",
    "params": {
      "body": {
        "companyConfig": {
          "searchParams": {
            "employeeCountV2": {
              "lowerBoundExclusive": 49,
              "upperBoundInclusive": 500
            }
          },
          "pageSize": 25
        },
        "profileConfig": {
          "searchParams": {
            "jobTitleV3": {
              "anyOf": [{
                "type": "functional",
                "seniority": ["c-suite", "vp"],
                "keywords": ["technology", "engineering"]
              }]
            }
          },
          "pageSize": 25
        }
      }
    }
  }
}

The output can be structured as a research map: company name, headcount, leadership profiles, and hiring signals. Treat it as evidence for review rather than a guarantee that every record is complete or permanently current.

4. Fundraising research: find the right investors and decision-makers

The problem

Founders raising capital need to identify the right VCs, find the right partner at each firm, and get a warm intro or cold email. This research can involve cross-referencing investment history, partner profiles, and network context. Keeping those inputs together makes the shortlist easier to review.

How the agent solves it

The agent searches for people at VC firms by title — Partner, Principal, Managing Director — and uses documented company and industry fields to build a candidate-firm shortlist. It can also use call_operation with operationId: companySearch to find companies for separate strategic-fit and investment-thesis research. The returned firmographics do not establish investment focus or intent.

{
  "tool": "call_operation",
  "arguments": {
    "operationId": "peopleSearch",
    "params": {
      "body": {
        "searchParams": {
          "jobTitleV3": {
            "include": [{
              "type": "functional",
              "seniority": ["c-suite", "director"],
              "keywords": ["venture capital"]
            }]
          },
          "industry": { "anyOf": ["Venture Capital and Private Equity"] },
          "location": { "include": ["United States"] }
        },
        "pageSize": 25
      }
    }
  }
}

The agent can return a candidate list of relevant investors. For profiles you select, a reveal attempt may return available contact fields. You can then use the same conversation to organize research notes or draft outreach for your existing delivery tool.

Founders can also flip this workflow: use call_operation with operationId: companySearch to find companies in adjacent verticals that might be strategic acquirers or partners, then operationId: peopleSearch to find the Corp Dev or M&A leads at those companies.

5. Account-based marketing: build target account profiles automatically

The problem

ABM campaigns require building detailed profiles of target accounts: who are the decision-makers, what is the org structure, how large is the company, and what technology do they use. Building those profiles across multiple systems creates repetitive search and handoff work.

How the agent solves it

The agent takes a list of target accounts and uses call_operation with operationId: companySearch for firmographics, then operationId: peopleSearch to build a shortlist of possible stakeholders at each — VP of Marketing, Head of Ops, CTO, CFO — and can make reveal attempts for selected profiles.

{
  "tool": "call_operation",
  "arguments": {
    "operationId": "peopleSearch",
    "params": {
      "body": {
        "searchParams": {
          "jobTitleV3": {
            "include": [{
              "type": "functional",
              "seniority": ["c-suite", "vp"],
              "keywords": ["marketing", "technology", "finance"]
            }]
          }
        },
        "pageSize": 25
      }
    }
  }
}

If the shortlist contains 30 people, 30 reveal attempts cost 30 credits, including not-found results. Returned work email and direct phone fields depend on availability. The agent can structure the responses by account or format them for review before a CRM import.

The common thread: structured data through one dispatcher

The pattern across all five workflows is the same: the agent needs connected access to people and company data. MCP provides the interface. The same indexed people and company data can support different questions, while each workflow decides which results to review and which profiles to reveal.

What Descovo doesn’t do

Honesty about scope matters more than marketing. Here is what Descovo is not:

• No intent data.We don’t track who is “in market” for your product.
• No email sequences. We don’t send emails. We return available contact fields.
• No CRM sync.We don’t write directly to Salesforce or HubSpot (yet).
• No ad targeting.We don’t build audiences for ad platforms.

Descovo is the data layer — who works where and which contact fields may be available. The workflows above require pairing Descovo with other tools for the action layer: your CRM, your outreach platform, your marketing automation. That’s by design. Do one thing well, and let the agent orchestrate the rest.

Why data connections matter to an agent workflow

A model can organize a task and decide which tool to call, but the result still depends on the connected operation and the data it returns. That makes the integration contract part of the product, not background plumbing.

For B2B workflows, useful connections include structured people, company, job, and reveal operations. The agent can use their responses as inputs, while the user remains responsible for review and for deciding what to do next.

MCP is one mechanism. It provides a standard way for compatible agents to connect to data sources. A hosted MCP server can reduce bespoke integration work for compatible clients and make the operation names and request fields discoverable to the model.

The practical evaluation question is therefore concrete: can the agent call the operations you need, understand the billing unit, and return data in a form your workflow can review?

Key takeaways

• Agentic AI needs data to act. Without connected access to B2B data, agents can reason but can’t execute business workflows.
• Five workflows, one data layer. Sales prospecting, recruiting, market research, fundraising, and ABM can all use the same categories of data: people, companies, jobs, and available contact fields.
• MCP provides a standard interface. One server connection exposes call_operation, which can execute operation IDs such as peopleSearch, companySearch, jobPostingSearch, and syncQuickContactReveal.
• 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. The Starter plan includes 5,000 credits/month at $99 standard, with a $49 launch price through Aug 20, 2026. Free includes 500 credits/month.
• Reveal modes make the tradeoff explicit. Lite uses a lower-yield lookup. Standard balances speed and coverage. Turbo prioritizes speed, while Exhaustive trades latency for the broadest available provider coverage.
• Descovo is the data layer, not the action layer. Who works where and which contact fields may be available. Pair it with your CRM, outreach tool, or marketing automation for the full workflow.