PR pros are using AI more than ever. 66% of PR professionals now use it regularly, whether that is drafting a press release, summarizing a report, or preparing talking points for a briefing.
The speed AI offers saves valuable time spent on routine tasks, but adoption has stalled at the surface layer. AI assistants still don’t reliably pull coverage data, run advanced analysis, or deliver real-time market updates.
AI answer engines source information online and base their results on what’s available in search. That may lead to inaccurate results and misinformation. Although AI-generated responses are increasingly accurate as models evolve, hallucinations and accuracy errors persist.
That is the gap MCP closes.
And it isn’t limited to PR. MCP connectors can be used across different use cases and industries, anywhere an organization needs its MCP-compatible AI assistants to integrate with its own systems, whether that is a sales rep pulling CRM records, a finance lead querying live dashboards, or a support manager reviewing ticket history.
What is MCP?
MCP stands for Model Context Protocol. It’s an open standard, originally introduced by Anthropic, that defines a common way for AI models (like LLMs) to connect to external tools, data sources, and services.
Think of it like a universal adapter: instead of building a custom, one-off integration every time you want an AI model to talk to a database, a search API, a file system, or a business tool, MCP gives you a standardized protocol so any MCP-compatible model can talk to any MCP-compatible server without bespoke glue code.
A few key concepts:
- MCP Server: exposes specific capabilities — like tools (functions the model can call), resources (data it can read), or prompts (reusable templates). Agility PR’s MCP Connector acts as a server exposing a “search media mentions” tool.
- MCP Client/Host: the AI application (e.g., Claude, or another LLM-powered app) that connects to one or more servers and lets the model use their capabilities during a conversation.
- Tools: a tool is a defined function with a name, description, and input/output schema that the model can invoke, such as “search_media_mentions(query, date_range)” returning metadata and snippets.
Why it matters: MCP defines exactly what data flows between the model and the MCP server (the tool call and its response). In the case of the Agility MCP server, Agility controls what’s returned — which is why you can confidently say the connector returns accurate, structured and trusted Agility media coverage.
Why does MCP exist?
MCP lets AI models connect to external data and applications without custom-building an integration for every model and use case. Before it, each AI assistant needed its own bespoke connection to each application.
As both AI assistants and business applications multiply across PR and comms teams, building and maintaining all those integrations individually becomes impossibly complex and resource-intensive. The result: the data teams need lives in one place, and the AI assistant they use every day lives in another.
A common standard like MCP solves that. By connecting media intelligence directly to AI assistants, it gives PR and comms teams verified, accurate coverage data right where they already work.
MCP vs API: What’s the difference?
The core difference between APIs and MCPs comes down to purpose. Both connect external data to internal applications, but they differ in scope and use case.
Let’s compare the two and how they fare for PR:
| API | MCP | |
| What is it? | A set of rules that lets one piece of software talk to another. It defines what requests you can make, what data you can send, and what response you’ll get back. | MCP is a standardized way for AI assistants to connect to external data and applications, so developers don’t have to build a custom integration for every model and every tool. |
| What does it connect to? | Dashboards, business intelligence tools (PowerBI, Tableau), internal applications | Claude or custom MCP-compatible agents. |
| How is it set up? | Requires custom development for integration with every endpoint | You can link a MCP connector with any compatible AI assistant. Requires no custom development |
| What is the output? | Structured data delivery into the specified dashboard/tool. The format and delivery remain the same on every run | A MCP server exposes tools, each with defined inputs and outputs, that an AI assistant can call to complete a task |
| Best suited to | Predefined use cases that repeat reliably at scale. Board and client dashboards, campaign performance, custom alerts, recurring reporting on a fixed cadence | Giving an AI assistant live, structured access to a specific system’s data or actions, the things a static training set can’t provide |
In short, an API delivers data into the tools your team already reports in, while a MCP connector lets an AI assistant retrieve that same data and reason over it in conversation.
How does a MCP connector benefit PR and communications teams?
PR and communications teams need to ensure their information comes from verified sources to maintain integrity. This raises the question: what if your AI chatbot could source information directly from the media data that supports your PR strategy? That is the problem a MCP connector solves.
Instead of switching between databases, monitoring dashboards, and AI assistants, MCP streamlines the process of gathering accurate and structured media data.
Here’s how that benefits PR and comms teams:
Answers are grounded in verified data
Inaccurate sourcing of information is what limits AI assistant use for mundane tasks. By removing open web results for verified media intelligence, the MCP connector returns accurate, verifiable results that allow informed decision-making.
Centralized access to media data
Users can request current coverage, sentiment, and share of voice directly from the assistant they already use. Getting the data no longer depends on knowing your way around multiple platforms and applications.
Increased accessibility for every user
Not every PR platform has agentic functionality to conduct searches in plain language. Building searches and conducting Boolean queries requires technical knowledge to execute properly. A MCP connector changes that by allowing communications professionals and various stakeholders in the organization to pull trusted data through natural-language prompts.
Allows multi-step reasoning for complex outputs
A comms deliverable is rarely a one-step process. With a MCP connector, users can pull current coverage, tailor it to the messaging guidelines already added to the assistant and draft an executive-ready briefing or internal memo, without switching tools.
What to consider before connecting your AI assistant with a MCP connector
If you are evaluating a MCP connector for your team, a few questions are worth asking first:
- Which platform holds the data you actually need to reach, and does it offer a connector?
- Does the connector work with the assistant your team has already adopted?
- Who on the team is authorized to connect, and what does the connector allow them to see?
- What is the one high-frequency task you would point this at first?
Agility’s MCP Connector: Bringing verified media intelligence into your AI assistant
The Agility MCP Connector extends Agility’s Media Intelligence API to Claude and other MCP-compatible assistants.
Once connected, communications teams can dissect media coverage however they want, using natural language prompts. Using Agility’s MCP Connector, PR teams can:
- Generate executive takeaways
- Analyze sentiment and trends
- Compare brands against competitors
- Create visuals covering share of voice, coverage over time, sentiment breakdown, and more
All outputs are grounded in Agility’s verified, structured media data and delivered in the AI assistant you’re working with.
The MCP Connector requires no custom development. For users with Agility’s Media Intelligence API access, all they require is to authorize the connection with a compatible client.
Want to see what your coverage looks like from inside your AI assistant? Book a demo.
