---
title: "MCP for B2B Data: How Model Context Protocol Is Changing Agent Development"
description: "MCP for B2B data: see how Model Context Protocol changes enrichment, Claude Code workflows, and real-time agent data calls in production."
canonical: "https://www.explorium.ai/blog/data-for-gtm/mcp-b2b-data/"
last-updated: "2026-05-17"
---

# MCP for B2B Data: How Model Context Protocol Is Changing Agent Development

> MCP for B2B data: see how Model Context Protocol changes enrichment, Claude Code workflows, and real-time agent data calls in production.

- Canonical URL: https://www.explorium.ai/blog/data-for-gtm/mcp-b2b-data/
- Last updated: 2026-05-17

## TL;DR

- **Model Context Protocol standardizes** how AI agents connect to external data sources, replacing fragmented HTTP polling, webhook pipelines, and batch ETL workflows with a single, structured interface.
- **Before MCP, agents** consumed B2B data through slow, stateful integrations that broke under real-time workloads and required constant maintenance by data engineering teams.
- **MCP architecture separates** concerns cleanly: a server exposes tools and resources, a client (your agent) invokes them, and the host environment orchestrates sessions — making agent code far simpler.
- **Synchronous MCP responses** are critical for agents because they allow a running reasoning loop to pause, retrieve live company or contact data, and resume without drifting into stale-context errors.
- Explorium's AgentSource MCP delivers 150M+ company profiles and 800M+ people records across 50+ sources at 100 QPS with synchronous response, deterministic IDs, and 97.8%+ match accuracy.
- Identity resolution through MCP — matching noisy input signals to canonical company or person records — is one of the highest-leverage use cases because it eliminates an entire class of pre-processing pipelines.
- Evaluating an MCP server for production requires assessing latency SLAs, schema stability, authentication model, rate limits, error contract, and whether tool definitions are rich enough for LLM function-calling.

The infrastructure question that used to keep AI teams up at night was not "can the model reason over B2B data?" It was "how do we get live B2B data into a running agent without rebuilding half the data stack?" That problem is finally getting a principled answer in the form of the Model Context Protocol — and the implications for GTM teams building intelligent sales and marketing agents are significant.

MCP for B2B data is not a marketing phrase. It describes a concrete architectural shift in how language model agents discover, invoke, and consume external data tools at runtime. In this article we walk through what MCP actually is, how B2B data access worked before it existed, why the old approaches broke down under agent workloads, and how to build or evaluate an MCP server for production B2B data use cases — including a full code walkthrough of a Claude agent calling a live B2B data tool.

Whether you are a founding engineer standing up your first agentic GTM system, a data platform team deciding how to expose your enrichment APIs to internal agents, or a RevOps leader evaluating infrastructure vendors, this guide will give you the mental model and technical grounding you need to make good decisions.

## What Is the Model Context Protocol and Why Does It Matter?

The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024, that defines how AI applications connect to external tools and data sources. The core idea is deceptively simple: instead of every agent framework inventing its own bespoke integration layer for every data vendor, MCP provides a shared protocol that any server can implement and any client can speak.

Think of MCP as doing for AI agents what REST did for web services — but optimized for the specific interaction patterns that language model reasoning loops require. Where REST was designed around human-initiated request-response cycles, MCP is designed around an autonomous agent that may call dozens of tools in a single reasoning trace, needs to understand what tools are available before calling them, and requires structured outputs it can reliably parse and reason over.

The protocol defines three core primitives. **Tools** are callable functions — the equivalent of API endpoints — that an agent can invoke to perform an action or retrieve data. **Resources** are read-only data objects that an agent can access, similar to file system reads. **Prompts** are reusable instruction templates that a server can expose to help the agent use it correctly. For B2B data use cases, tools are by far the most important primitive: they are how your agent asks "give me the firmographic profile for Salesforce" and gets back structured JSON it can reason over.

What makes MCP architecturally significant is the separation it creates between three roles. The **MCP server** owns the data and exposes it via a defined interface. The **MCP client** (your agent code or agent framework) discovers available tools, constructs calls, and processes responses. The **host environment** — Claude Desktop, a LangChain executor, a custom orchestrator — manages the session lifecycle and passes messages between client and server. This separation means the same MCP server can be consumed by a Claude agent, a LangChain chain, or a fully custom Python executor without any changes to the server itself.

For teams building on [GTM data platforms](/resources/gtm-data-platform), this is a meaningful unlock. Instead of maintaining separate integrations for every agent framework your team wants to experiment with, you build the MCP server once and every compliant client can use it. The protocol has already been adopted by major model providers and agent frameworks, which means the ecosystem flywheel is spinning.

MCP PrimitiveAnalogous ConceptB2B Data Use CaseAgent BehaviorToolREST API endpointCompany profile lookup, contact enrichmentAgent calls tool with input, gets structured responseResourceFile / database rowSaved account lists, signal snapshotsAgent reads resource URI, gets data contentPromptDocumentation / system prompt templateGuidance on how to use enrichment toolsAgent loads prompt to improve tool usage qualitySamplingCallback / webhookServer-initiated signals, intent alertsServer asks client LLM to complete a taskThe protocol also defines a standard discovery mechanism. When an agent first connects to an MCP server, it can call `tools/list` to get a machine-readable manifest of every available tool, including its name, description, and JSON Schema input definition. This means a general-purpose agent doesn't need to be pre-programmed with knowledge of your B2B data tools — it can discover them at runtime, read their descriptions, and decide which ones are relevant to the current task. That runtime discovery capability is one of the most important properties for building genuinely autonomous agents.

## How Agents Consumed B2B Data Before MCP

To appreciate what MCP changes, it helps to understand the fragmented landscape it is replacing. Before MCP, there were essentially four patterns for getting B2B data into a running agent, and all of them had serious drawbacks.

**Pattern 1: Batch ETL to a vector store.** The most common early approach was to run nightly or weekly exports from a data vendor, process the records, embed them, and load them into a vector database that the agent could query via semantic search. This worked reasonably well for static knowledge retrieval but was fundamentally unsuited to B2B data, which changes constantly. Company headcounts, funding rounds, technology stacks, and intent signals from six days ago may be worse than useless — acting on stale signals erodes trust in the system and generates noise in CRM.

**Pattern 2: REST API calls embedded in agent code.** A step up from batch ETL, this pattern had the agent call data vendor APIs directly via HTTP inside its tool functions. The problem was maintenance: every API had a different authentication scheme, pagination model, error format, and rate limit strategy. Engineers spent more time writing API wrapper code than building agent logic. When a vendor changed their API, the agent broke silently. And because each integration was bespoke, there was no shared abstraction that other agents or frameworks could reuse.

**Pattern 3: Webhook pipelines with queues.** For near-real-time B2B signals, teams built webhook receivers that ingested events from intent providers, enrichment tools, and CRM platforms into message queues, then had agents consume from those queues. This architecture was complex to operate, introduced significant latency between an event occurring and an agent being able to act on it, and created ordering and deduplication problems that required substantial engineering effort to solve.

**Pattern 4: In-context data dumps.** The simplest approach — just paste the relevant company data into the agent's context window — was also the most brittle. Context windows have limits, pasted data goes stale the moment it is inserted, and there is no mechanism for the agent to ask follow-up questions or retrieve additional data mid-reasoning. This pattern also made it impossible to build agents that could autonomously research accounts they had not seen before.

PatternLatencyData FreshnessMaintenance BurdenAgent AutonomyFramework PortabilityBatch ETL + vector storeSeconds (query)Hours to days oldMedium (pipeline ops)Low (no live lookup)MediumEmbedded REST API calls200–800msLiveHigh (per-vendor code)MediumLow (bespoke)Webhook + queue pipeline5s–5minNear-real-timeVery highLow (reactive only)LowIn-context data dumpNone (pre-loaded)Stale at insert timeLowVery lowHighMCP server50–300msLiveLow (standardized)HighVery highEach of these patterns also had a shared problem: they were designed around human-initiated workflows, not agent-initiated ones. A human decides in advance what data to export, what webhooks to subscribe to, what context to paste in. An autonomous agent needs to be able to decide at runtime — in the middle of a reasoning loop — what data it needs, retrieve it, and continue reasoning. That capability requires a synchronous, discoverable, structured interface. That is what MCP provides.

For teams building [B2B data enrichment](/resources/b2b-data-enrichment) pipelines into their agent stacks, the shift is less about raw capability and more about operational simplicity. MCP does not do things that were impossible before — it makes them dramatically easier to build and maintain.

## MCP Architecture in Depth: Servers, Clients, and Tool Definitions

Let's get concrete about how MCP works at the protocol level. An MCP server is a process that listens for connections from clients and responds to a defined set of method calls over a JSON-RPC 2.0 transport. The transport can be stdio (the server runs as a child process and communicates over standard input/output), HTTP with Server-Sent Events for streaming, or a WebSocket connection. For B2B data use cases where you are connecting to a remote data service, HTTP+SSE is the most common transport.

When a client connects, the first exchange is an initialization handshake where both sides declare their protocol version and capabilities. After that, the client can call `tools/list` to retrieve the server's tool manifest. Here is what a minimal MCP server configuration looks like for a Claude Code or Claude Desktop environment:

```
`{
  "mcpServers": {
    "agentsource": {
      "command": "npx",
      "args": ["-y", "@explorium/agentsource-mcp"],
      "env": {
        "AGENTSOURCE_API_KEY": "your_api_key_here",
        "AGENTSOURCE_BASE_URL": "https://api.explorium.ai/mcp/v1"
      }
    }
  }
}`
```
This configuration tells the host environment to launch the MCP server as a subprocess, passing the API key via environment variable. The host then connects to the server over stdio and manages the session. The agent itself never needs to know about the transport layer — it just calls tools by name with structured input.

The tool definition is the most important artifact in the MCP ecosystem from an agent quality perspective. A well-written tool definition dramatically improves the LLM's ability to call the tool correctly. Here is an example tool definition for a company profile lookup tool:

```
`{
  "name": "get_company_profile",
  "description": "Retrieve a full firmographic and technographic profile for a company. Use this when you need revenue range, employee count, industry, technology stack, funding history, or contact information for a specific organization. Accepts company name, domain, or Explorium company ID as input.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "identifier": {
        "type": "string",
        "description": "Company name (e.g. 'Salesforce'), domain (e.g. 'salesforce.com'), or Explorium company ID"
      },
      "identifier_type": {
        "type": "string",
        "enum": ["name", "domain", "explorium_id"],
        "description": "The type of identifier provided. Defaults to 'domain' if a domain-like string is detected."
      },
      "fields": {
        "type": "array",
        "items": { "type": "string" },
        "description": "Optional list of specific fields to return. If omitted, returns full profile."
      }
    },
    "required": ["identifier"]
  }
}`
```
Notice the description does not just say what the tool does — it tells the agent *when* to use it and gives concrete examples of input formats. This is critical for function-calling quality. LLMs decide which tool to call based on the description, so investing in good descriptions directly translates to fewer tool-calling errors and better agent behavior.

From the client side, calling an MCP tool from Python is straightforward. Here is a full working example of a Python agent calling an MCP B2B data tool using the `mcp` client library:

```
`import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def enrich_company(company_domain: str) -> dict:
    """Call the AgentSource MCP server to enrich a company by domain."""
    server_params = StdioServerParameters(
        command="npx",
        args=["-y", "@explorium/agentsource-mcp"],
        env={"AGENTSOURCE_API_KEY": "your_api_key_here"}
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the connection
            await session.initialize()

            # List available tools (optional — for discovery)
            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")

            # Call the company profile tool
            result = await session.call_tool(
                "get_company_profile",
                arguments={
                    "identifier": company_domain,
                    "identifier_type": "domain"
                }
            )

            # The result content is a list of content blocks
            for block in result.content:
                if block.type == "text":
                    import json
                    return json.loads(block.text)

    return {}

# Example usage
if __name__ == "__main__":
    profile = asyncio.run(enrich_company("stripe.com"))
    print(f"Company: {profile.get('name')}")
    print(f"Employees: {profile.get('employee_count')}")
    print(f"Revenue: {profile.get('revenue_range')}")
    print(f"Tech stack: {profile.get('technologies', [])[:5]}")`
```
This pattern — initialize session, list tools, call tool, parse response — is the same regardless of which MCP server you are connecting to. That consistency is the entire point of the protocol. Your agent code does not change when you switch data providers; only the server configuration changes.

For teams [building AI lead generation](/resources/ai-lead-generation) workflows, this standardization means you can swap enrichment providers, add additional data sources, or layer in intent signals without restructuring your agent code. The MCP server becomes a stable abstraction boundary between your agent logic and the data supply chain beneath it.

## Why Synchronous Response Is Non-Negotiable for Agent Workloads

One of the most important and least-discussed properties of a production-ready MCP server for B2B data is synchronous response. This deserves a dedicated explanation because the failure mode it prevents is subtle but catastrophic for agent quality.

A language model agent reasons in a loop. It receives a task, generates a plan, calls tools to gather information, observes the results, updates its plan, calls more tools, and eventually produces an output or takes an action. The critical word is "loop" — the agent's reasoning trace is a continuous, ordered sequence of thoughts and observations. Each observation shapes the next thought.

When a tool call is asynchronous — meaning the agent fires a request and gets back a job ID rather than a result — the agent faces a choice: wait and poll, or continue reasoning with incomplete information. Both options are bad. Polling introduces retry logic, backoff strategies, timeout handling, and state management complexity that dramatically increases the surface area for bugs. Continuing with incomplete information means the agent may reason itself into a wrong conclusion before the data it needed even arrives.

More fundamentally, asynchronous tool responses break the mental model that makes agents predictable. The power of the reasoning loop is that each step builds on the last. Inject latency and uncertainty into that loop and you get agents that hallucinate to fill the gap, produce inconsistent outputs across runs, or simply time out mid-task.

Synchronous MCP tools solve this cleanly. The agent calls the tool, the call blocks until the server returns a result, and the agent continues reasoning with real data. There is no polling, no job ID, no state to manage. The agent's reasoning trace remains a clean sequence of calls and observations.

Response ModelAgent ComplexityFailure SurfaceReasoning CoherenceSuitable ForSynchronous (blocking)LowMinimalHighAll agent workloadsAsync with pollingHighLarge (timeouts, retries)MediumLong-running batch jobsAsync with callbackVery highVery largeLowEvent-driven pipelines onlyStreaming (SSE)MediumMediumHigh (if buffered)Long content generationFor B2B data specifically — company lookups, contact enrichment, intent signal retrieval, firmographic filtering — the data volumes per request are small enough (a few kilobytes at most) and the server-side computation is fast enough that synchronous response at 100 QPS or better is entirely achievable. There is no good reason to build an async B2B data MCP server unless you are doing something like triggering a batch export of tens of thousands of records.

This is why Explorium designed AgentSource MCP around synchronous response from the ground up. When your agent asks for a company profile, it gets a complete, structured response within the same tool call — no polling, no job IDs, no state management on the client side. That design choice is not incidental; it is what makes the server usable in production agent workloads rather than just demos.

For the broader context of [architecting autonomous GTM data infrastructure](/resources/architecting-autonomous-gtm-data-infrastructure), synchronous data access is a first-class design constraint. Every component in the stack that introduces asynchrony adds latency and complexity that compounds across a multi-step reasoning trace.

## Identity Resolution via MCP: Eliminating the Pre-Processing Pipeline

One of the highest-leverage applications of MCP for B2B data is identity resolution — the process of taking a noisy, incomplete, or ambiguous reference to a company or person and resolving it to a canonical, stable identifier in a master data graph. This is an unglamorous but critical capability that most B2B agent stacks handle poorly.

The problem is pervasive. Your agent might receive a company name from a web scrape ("salesforce inc"), a domain from a form fill ("www.salesforce.com"), a LinkedIn URL from a prospect research workflow, or a partial company name from a customer's email signature. Before the agent can retrieve enrichment data, apply scoring models, or update CRM records, it needs to resolve all of these inputs to the same canonical company — reliably, at speed, and without a human in the loop.

Before MCP, identity resolution was typically handled as a pre-processing step: a separate pipeline that cleaned and resolved identifiers before feeding them to the agent. This added latency, required its own infrastructure, and meant the agent could not resolve identities on-demand for companies it encountered mid-reasoning.

With an MCP server that supports identity resolution as a first-class tool, the agent can resolve identifiers inline during its reasoning loop. It calls `resolve_company_identity` with the noisy input, gets back a canonical ID and confidence score, and then uses that ID for all subsequent data lookups. The canonical ID is stable across sessions and data sources, which means the agent can build up a coherent view of an account across multiple tool calls and multiple conversations.

Deterministic IDs are a related but distinct property. A deterministic company ID means that given the same input — say, the domain "stripe.com" — the server always returns the same canonical identifier, regardless of when the call is made or which server instance handles it. This is essential for building agents that need to join data across calls, update CRM records, or maintain state about accounts across sessions. Without deterministic IDs, you end up with fragmented account representations that are impossible to reconcile.

Explorium's AgentSource MCP uses deterministic Explorium company IDs and person IDs across its entire data graph. When your agent resolves "stripe.com" to an Explorium company ID, that ID maps to the same record whether you call the enrichment tool, the signal tool, or the contact lookup tool. This consistency is what makes it possible to build agents that accumulate and reason over multi-source account intelligence rather than just looking up isolated facts.

For teams working with [B2B buying signals](/resources/b2b-buying-signals), identity resolution is the prerequisite that makes signal attribution possible. A buying signal — a company researching your category on G2, a LinkedIn post from a prospect discussing a relevant pain point, a spike in job postings for roles that signal a technology initiative — is only actionable if you can reliably map it to an account in your CRM. MCP with built-in identity resolution collapses the pipeline that used to make this mapping fragile and slow.

## REST API vs. MCP for Agent Use Cases: A Practical Comparison

Given that most B2B data vendors already expose REST APIs, a reasonable question is: why bother with MCP at all? The answer is that REST APIs and MCP tools solve subtly different problems, and the difference becomes significant when you are building agents rather than traditional applications.

A REST API is designed to be called by code written by a human developer. The developer reads the documentation, understands the authentication scheme, writes wrapper code, handles pagination and errors, and calls the endpoint from application logic. The API assumes a human in the integration loop, at least at build time.

An MCP tool is designed to be called by a language model agent at runtime, without a human having pre-written the calling code. The agent discovers the tool, reads its description, constructs the input, calls the tool, and parses the output — all autonomously. This requires the interface to be self-describing in a way that REST APIs typically are not.

DimensionREST APIMCP ToolWinner for AgentsDiscoveryRequires documentation, human-written SDKRuntime via tools/list with JSON SchemaMCPAuthenticationPer-vendor (API key, OAuth, JWT)Handled at server level, transparent to agentMCPError handlingHTTP status codes + vendor-specific error bodiesStandardized MCP error types agent can reason overMCPInput validationServer-side only, often opaqueJSON Schema exposed to agent before callMCPPaginationMust be handled in client codeServer handles, returns complete result setMCPFramework portabilityLow — bespoke per integrationHigh — any MCP client worksMCPHuman debuggabilityHigh — curl, Postman, browserMedium — requires MCP client or inspectorRESTEcosystem maturityVery highRapidly growing (2024–2025)REST (for now)There are still cases where a REST API is the right choice. If you are building a traditional application where a human developer writes all the integration code, REST is simpler, better documented, and supported by more tooling. If you need to stream large result sets, REST with HTTP streaming may be more appropriate than MCP. And if your agent framework does not yet support MCP (though most major ones now do), you may need to wrap REST APIs in a compatibility layer.

But for building autonomous agents that need to discover and call B2B data tools at runtime — which is the direction the entire field is moving — MCP provides a substantially better interface than REST. The self-description, the standardized error contract, the authentication abstraction, and the framework portability all compound into a significantly simpler agent development experience.

For teams investing in [agentic sales infrastructure with MCP servers](/resources/agentic-sales-infrastructure-mcp-servers), the practical implication is clear: build your internal data services as MCP servers from the start, and prefer MCP-native external data providers when they exist. The marginal investment in MCP compatibility at the infrastructure layer pays for itself many times over in reduced agent integration complexity.

> **Want B2B data via MCP server?** Explorium's AgentSource MCP delivers 150M+ company profiles at 100 QPS with synchronous response — drop it directly into your Claude or LangChain agent. [Explore AgentSource →](https://www.explorium.ai)

## Building with Explorium AgentSource MCP

Explorium's AgentSource MCP is built specifically for the agent use case, not retrofitted from a human-facing API. The design decisions that distinguish it from a generic B2B data API reflect a deep understanding of what autonomous agents actually need from a data tool.

The data foundation is substantial: 150 million company profiles, 800 million person records, sourced and cross-validated across 50+ independent data providers. That breadth matters for agent workloads because agents explore accounts the system has never seen before — they cannot rely on a pre-loaded universe of target accounts. When your agent encounters a mid-market logistics company in Eastern Europe while researching a prospect's supply chain partners, it needs to be able to look that company up and get a complete, reliable profile. Shallow databases fail these edge cases. AgentSource does not.

The 18 signal categories cover the full spectrum of buying intent and account activity: technology adoption signals, hiring velocity signals, funding events, executive change signals, web traffic patterns, review site activity, social engagement signals, regulatory filings, patent activity, and more. Each signal category is exposed as a distinct MCP tool with its own schema, so agents can be selective about which signals they retrieve based on the task at hand rather than pulling everything and filtering client-side.

The 97.8%+ match accuracy figure matters most for identity resolution. When your agent resolves a company by domain or name, it needs to be confident the returned profile is the right company — not a subsidiary, a similarly-named competitor, or a stale record from an acquired entity. High match accuracy is not just a quality metric; it is a safety property. An agent that occasionally resolves "Oracle" to a different company will make attribution errors that propagate through its entire reasoning trace.

The 100 QPS synchronous throughput is sized for production agent workloads. A research agent processing a list of 500 accounts can complete enrichment in under 10 seconds at that throughput. A real-time qualification agent can enrich inbound leads as they arrive without queuing. A multi-step prospecting agent can call back-to-back enrichment, signal, and contact tools in a single reasoning loop without hitting rate limits.

AgentSource MCP also exposes deterministic Explorium IDs as first-class identifiers. These IDs are stable across time and data source, which means your agent can build up account intelligence across multiple sessions, join data from multiple tool calls, and write structured data back to CRM with reliable account linkage. For teams building agents that need to maintain persistent account state — tracking which accounts have been researched, scored, or contacted — deterministic IDs are the foundation that makes this possible.

Integration is designed to be fast. If you are running Claude Code or Claude Desktop, you add the MCP server configuration to your settings file and restart the application. If you are building a custom agent with LangChain, LlamaIndex, or a direct API integration, you use the Python or Node.js MCP client library to connect to the server. Either way, you go from zero to a working B2B data tool in your agent in under 15 minutes.

The server exposes tools across the full account intelligence lifecycle: company profile retrieval, person profile retrieval, company search and filtering, contact discovery, signal retrieval by category, identity resolution, account list enrichment, and technology stack lookup. Each tool is designed to be composable — the output of a company search tool can be fed directly as input to the signal retrieval tool, enabling multi-step account research chains with clean data handoffs.

## Evaluating MCP Servers for Production B2B Data Use Cases

As the MCP ecosystem matures, more vendors will offer MCP servers for B2B data. Not all of them will be production-ready. Here is a framework for evaluating an MCP server before committing to it as infrastructure for an autonomous agent.

**Latency SLA.** What is the p50, p95, and p99 latency for tool calls? For synchronous agent workloads, p95 under 500ms is a reasonable target. Anything above 1 second at p95 will noticeably degrade agent performance on multi-step tasks. Ask for benchmark data, not just marketing claims.

**Schema stability.** How often does the tool schema change? Breaking schema changes — removing fields, renaming parameters, changing types — will silently break your agent's tool-calling behavior. A production MCP server should version its tools and provide advance notice of breaking changes.

**Authentication model.** Is API key authentication supported, or does the server require OAuth flows that are difficult to automate in an agent context? Does the server support server-side authentication (key stored in server config) rather than requiring the agent to manage credentials? The latter is significantly safer.

**Rate limits and burst capacity.** What are the rate limits per API key, and what happens when they are exceeded? Does the server return a well-formed MCP error that the agent can reason over, or does it return an HTTP 429 that the client library may not handle gracefully? What is the burst ceiling for momentary spikes?

**Error contract.** A production MCP server should define a clear error taxonomy: not-found errors (the company doesn't exist in the database), validation errors (the input was malformed), authentication errors, and rate limit errors should all be distinguishable. Agents that can distinguish error types can recover gracefully; agents that receive opaque errors cannot.

**Tool description quality.** Read the tool descriptions as if you were an LLM deciding whether to call them. Are the descriptions precise enough that a model could correctly decide when to use each tool? Are the input parameter descriptions clear enough that the model would construct valid inputs? Poor tool descriptions are the most common cause of agent tool-calling errors, and they are entirely within the server vendor's control.

**Data freshness guarantees.** How frequently is the underlying data refreshed? For some B2B data categories (company firmographics), weekly refresh may be sufficient. For others (intent signals, funding events, executive changes), staleness of more than 24 hours makes the data much less actionable. Understand the refresh cadence for each data type exposed by the server.

**Identity resolution quality.** Test the server's identity resolution with ambiguous inputs: common company names, subsidiaries, recently acquired companies, companies with multiple domains. The resolution quality you see in testing will be representative of what you get in production, where agents regularly encounter edge cases.