Agents vs. Tools Is Over: MCP Elicitations Changed the Game

Developers still talk about agents and tools as if they’re different species: one “runs the show,” the other “does one job when called.” But in practice, those lines are blurring… and with MCP elicitations, they’ve practically disappeared. Why should you care? Because the way we label a capability shapes how we design, package, and reuse it. A complex model wrapped as a “tool” may look simple, while an “agent” can be invoked as just another callable step. This post unpacks a few common claims and dives into what the agent–tool divide really means in the era of MCP elicitation.

MCP Tools vs Agents: Integration with External Systems

Claim #1: “Integration belongs to tools.”

What this implies: Only tools should talk to external systems (Slack, webhooks, APIs).

What actually holds (then and now): Agents integrate directly just fine. If an agent needs to post to Slack or hit an internal API, it doesn’t need to route that through an inferred tool call. In coding frameworks like LangChain, CrewAI, and AutoGen, you can drop imperative code right into the agent’s flow—e.g., a direct Slack send, a webhook call, or a database write. Sure, if you prefer reuse or governance, you can wrap the same action as a tool. But that’s a choice, not a boundary.

Complexity Isn’t Exclusive to Agents

Claim #2: “Agents are smarter and more complex than tools.”

What this implies: Agents plan, branch, and retry; tools are simple, single-step routines.

What actually holds: A tool can be extremely complex while sounding simple, because the sophistication lives behind a single, clean interface. Examples: 

• Grafana “threshold & forecast” tool (sounds simple): “Watch this error-rate graph and tell me if we’ll breach 2% in the next hour.”
  
  Behind the interface: query time-series data, denoise/smooth, detect regime shifts, fit a short-horizon forecast, account for deployment windows, compute breach probability, and output a rollback-time estimate—one callable tool, substantial machinery.
  
  
• Contract risk tool (sounds simple): “Score this MSA for data-handling and indemnity risk.”
  
  Behind the interface: clause detection, entity extraction, policy alignment, precedent lookup, redline suggestions, confidence scoring.
  
  
• Customer health “churn signal” tool (sounds simple): “Flag at-risk accounts this week.”
  
  Behind the interface: join telemetry with billing and support data, engineer features, run a calibrated model, generate explanations, and open follow-ups.
  
  

Each of these examples reads like a single-purpose tool, yet hides the kind of reasoning and branching people often associate with “agents.”

Invocation and Capability: How MCP Redefines Roles

Claim #3: “Invocation defines capability.”

What this implies: Because agents are run directly and tools are selected by the agent, they must be fundamentally different.

What actually holds: Invocation defines role, not capability.

• Agent: typically invoked directly by a person or scheduler (“Run the research agent”).
  
  
• Tool: typically invoked by inference—the agent selects it when your request implies it will help.

You can see the interchangeability clearly in two products:

• n8n. You can build an agentic workflow in n8n and expose it as a callable tool so other clients (or other n8n flows) invoke it. Conversely, a “tool” step in n8n can call other agents or workflows. The construction is the same; only the role changes.
  
  
• Zapier (Zaps / “ZAP” steps). A Zap can start an Agent as a step, and Zapier’s capabilities can be exposed as tools that other assistants invoke. A Zap that looks like a “tool” from the outside can, in turn, call other agents. Same capability; different hat. No intrinsic difference between “agent” and “tool.”

MCP Elicitation: Bringing Interactivity to Tools

Claim #4: “Interactivity belongs to agents.”

What this meant (historically): Only agents could talk to the user, collect missing details, then invoke tools when the time was right.

What’s changed now: With MCP elicitation, tools can do that for themselves. A tool can pause mid-operation to ask the user (via the client) for specific, typed fields—dates, destinations, options—and continue once those are provided. The request is structured; the client renders a small form and validates entries; the tool proceeds without switching to a separate “agent” flow.

Example:

You say, “Create a Q3 marketing snapshot and send the chart to the team.”

The Campaign Snapshot tool starts and asks only for what’s missing: date_range, channels, kpi, destination. Your client shows a short form; you confirm; the tool fetches data, posts to Slack, and returns a structured result. No agent loop required—yet the experience is interactive and tailored.

A little more on elicitation: interactivity is now in-band with the tool call. Instead of over-specifying every parameter up front, the tool gathers exactly what it needs, exactly when it needs it, with types and validation handled by the client. That’s why the old “agents interact; tools don’t” line no longer holds. Tools can talk back, collect inputs, and even branch internally.

Rethinking the Labels: Agent, Tool, Subagent

Think roles in a larger use case, not technical categories. The labels set expectations for how something participates in a flow; they don’t limit what it can do.

• Agent — the root of the interaction: the entity a user addresses directly.
  
  Keep in mind: the same capability might be packaged as a tool in another context, invoked from above rather than addressed directly.
  
  
• Tool — a callable capability placed explicitly in a flow to be invoked by an agent above (or selected by a model in context).
  
  Keep in mind: a tool can be relabeled as an agent in another use case; it can also encapsulate a very complex system (its own flows, reasoning, and orchestration) presented as a single purpose.
  
  
• Subagent — a tool that, once invoked, coordinates additional work (calls other tools, branches, retries). From the outside it’s still a tool; inside, it behaves agent-like.
  
  

Where Gentoro Fits in the Agent–Tool Continuum

Gentoro was built for this shift. In our platform, an MCP Tool can be as lightweight as a single API call or as sophisticated as a multi-step orchestration with its own reasoning. With upcoming support for elicitation, those Tools don’t just run. They can pause, collect structured inputs, validate them, and continue, all inside one seamless flow.

That means developers don’t have to agonize over whether to label something an agent or a tool. In Gentoro, you design the capability once, then:

• Run it as a standalone agent users can talk to directly,
• Package it as a tool other agents call, or
• Embed it as a subagent coordinating downstream work.

The roles can shift, but the definition doesn’t change. With Gentoro, you gain the flexibility to ship faster, reuse everywhere, and future-proof your MCP workflows—no matter how the ecosystem evolves.

Final Thoughts: Building Flexible AI Workflows With Agents and Tools

The old line between agents and tools was always more about convenience than capability. With MCP elicitations, that line has vanished. For developers, this means you don’t have to decide whether something “deserves” to be an agent or a tool. You can design the capability once, then package and reuse it however your workflow needs. In Gentoro, that flexibility is built in: one definition can run as a standalone agent, a callable tool, or even a subagent coordinating others. The role may change, but the capability remains yours to deploy anywhere.

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