MCP Tools: Navigating Potential and Security Risks

The Model Context Protocol (MCP) ecosystem has exploded. In just months, developers have published hundreds of servers and thousands of tools. It’s a remarkable show of creativity: everything from lightweight utilities that return the time of day to heavyweight servers that can administer databases, manage cloud infrastructure, or even control a computer.

This is a developeing ecosystem with promise. MCP will make tools universally accessible to AI systems, which means agents can do more than chat — they can act. With this freedom and creativity comes security concerns. As with any new protocol that touches sensitive systems, the way tools are designed matters. The descriptions, parameters, and scope of these tools reveal both how much potential MCP has and what risks must be managed as the ecosystem matures.

What We Found in the Data

For this analysis, we focused on a snapshot of MCP ecosystem as of September 17, 2025. OUr dataset included 136 servers and 644 active tools. Our broader MCP data set is larger — over 360 servers and 1,300 tools have been listed — but many of those are now deprecated or superseded. These findings are drawn from 644 currently tools in active developement. These represent what developers are experimenting with and publishing today.

It’s important to stress: this analysis is of code artifacts, not live deployments. These servers illustrate potential capabilities and risks if run unsecured. They are not evidence of vulnerabilities being exploited in the wild.

Where Potential Meets Risk

The MCP tools we analyzed span everything from productivity boosters to system-level administration. Each category demonstrates both immense potential and areas where careful design and deployment matter. Here are four illustrative cases:

Remote System Control
- The potential: Imagine an AI assistant that can install software updates, troubleshoot an application by launching it, or automate repetitive GUI tasks like filling forms or clicking through menus. Tools like Click-Tool, Type-Tool, and Powershell-Tool make this kind of end-to-end automation possible. For IT admins, this is the holy grail of hands-free support.
- The risk: Those same tools grant an AI near-total control of a desktop. If not sandboxed, an agent could accidentally (or maliciously) delete files, change system settings, or expose sensitive data. In effect, you’ve given the keys to the kingdom.
- The takeaway: Remote-control tools are incredibly powerful — but should always run in isolated environments, with explicit approval steps for high-impact actions.
Database Administration
- The potential: Supabase MCP servers expose functions like execute_sql, manage_roles, and backup_database. This means an AI agent could not only query data but also manage schema changes, handle migrations, or generate backups. For developers, this could automate tedious tasks and reduce operational overhead.
- The risk: An unconstrained AI might drop a table instead of selecting from it, leak sensitive customer records, or modify access roles incorrectly. If a “helpful” agent has full DBA powers, every request becomes a potential integrity or confidentiality breach.
- The takeaway: Database tools are best positioned when scoped to specific, non-destructive queries, with destructive operations gated or restricted to human approval.
Secrets and Key Management
- The potential: Some tools allow fetching or managing service keys, making it easier for AI systems to connect downstream services. In theory, this enables agents to self-provision resources, rotate keys automatically, or configure integrations on the fly.
- The risk: Returning raw keys to an AI is like handing them a credit card — you don’t know where they’ll store it, how they’ll use it, or whether they’ll surface it in conversation. Once exposed, keys can’t be “un-leaked.”
- The takeaway: Secret-management tools should focus on using keys internally (e.g., injecting them into a secure call) rather than exposing them directly to the model layer.
Combinatorial Risk
- The potential: MCP is designed for orchestration — stitching together multiple tools to accomplish complex tasks. An AI could query a mind map, fetch a Docker release, and run a Lighthouse audit, all in one workflow. This composability is what makes MCP powerful.
- The risk: Safe tools can become unsafe when chained. For example: a mind map query produces a filename → a database admin tool executes SQL against it → a secrets manager exposes credentials needed for that database. No single tool is “dangerous” in isolation, but the chain creates escalation paths no one intended.
- The takeaway: Orchestration needs policy. Safe defaults, allowlists, and guardrails help ensure combinations don’t accidentally expose privileged workflows.
Overflexibility and Ambiguity
- The potential: Some servers expose generic “catch-all” tools where the parameter is simply args: object. This makes them highly flexible, letting developers pass in anything they want. For rapid prototyping, that’s appealing.
- The risk: For AI, this vagueness is a trap. Without clear typing or examples, models struggle to format requests correctly. Worse, attackers or careless inputs can exploit the open schema to push unintended commands or payloads.
- The takeaway: Flexibility shouldn’t come at the cost of clarity. Strong schemas, enums, and examples make tools both safer and easier for agents to use.

An AI-UX Perspective

This isn’t just a security conversation — it’s an AI-UX one. In this paradigm the way tools are described, parameterized, and scoped directly affects both how usable they are for AI agents and how safe they are in practice.

Patterns we observed:

Clarity is safety: Tools with typed parameters and natural examples (e.g. query_mindmap) are easier for AI to call and less likely tobe misused.
Overflexibility is risky: Tools that just expose a generic “args: object” put the burden on the model to guess, making errors more likely and increasing the risk of unintended actions.
Purpose helps orchestration: Tools that include why they should be used (not just what they do) help models choose them at the right time.
Small verbs > big bundles: Narrow tools reduce ambiguity and risk. Big, multipurpose servers can feel efficient, but they multiply the chance of misfires.

In other words: better AI-UX is better security. Design choices that make tools easier for models to use also make them safer to deploy.

##From Developer Experiments to Mainstream Risks

It’s worth recognizing that the developers building MCP tools today often do understand the risks. Many of these servers are experimental, published as proofs-of-concept, or intended for controlled environments and knowledgable deployment. A seasoned engineer knows not to deploy a tool that exposes raw SQL or system control without careful safeguards.

But ecosystems evolve. As MCP gains adoption — and it’s happening fast — these same tools, or ones inspired by them, will be deployed by a broader audience. Everyday developers, startups racing against deadlines, or IT teams simply following “drop-in” tutorials may not have the same instincts for risk. (See countless XKCD comics on fragile dependencies and Bobby Tables.) That’s where the danger lies: when powerful tools cross into mainstream use without guardrails.

The quality of today’s examples matters more than we think. If tool descriptions, schemas, and defaults are precise and safe, they become models for future developers. If they are vague, overbroad, or insecure, those patterns will propagate too. In this sense, the security posture of the ecosystem is cumulative: each well-coded tool strengthens the baseline. With that in mind what can we do?

Principles for Safer MCP Deployment

Rather than fear-mongering, we see this as an opportunity. The ecosystem is young, and patterns are still forming. We can learn from today’s best develoeprs to build tomorrow’s safer standards.

Some initial guiding principles:

Least privilege by default: Only expose the specific verbs an agent needs.
Sandbox the powerful stuff: Tools that run SQL or OS commands should live in isolated environments. Potentially use deterministic code to limit exposure of critical commands.
Policy enforcement layers: MCP gateways can enforce allowlists, redactions, and approval flows.
Audit trails everywhere: Every tool call should be logged with who/what/when.
Design for AI first: Schemas, typing, and examples aren’t just developer ergonomics; they’re safety features for AI use and to avoid accidentil missuse.

The Bigger Picture

What this analysis shows is not that MCP tools are “insecure.” Quite the opposite: it shows that developers are exploring the full creative spectrum of what MCP can enable. That’s a sign of a healthy ecosystem. But with this explosion of creativity come the obligation to make these tools usable and safe — not just for humans, but for the AIs that will increasingly call them.

The opportunity now is to channel this experimentation into shared practices: standard ways of typing parameters, flagging risky tools, and encouraging clarity in descriptions. If we get this right, MCP can deliver on its promise: a world where AI systems can reason, act, and collaborate across a rich landscape of tools — without opening the door to unnecessary risk.

Conclusion

The active tools we reviewed are the early sketches of an AI future. They show what’s possible — and they highlight the pitfalls we’ll need to navigate. By treating AI-UX and security as two sides of the same coin, we can ensure MCP doesn’t just succeed, but thrives as the safe, universal plumbing of AI action.