As enterprises adopt Model Context Protocol (MCP) to connect AI agents and tools with internal systems, one of the biggest risks they face is untrusted or unsafe tool calls. Without safeguards, a malicious prompt, injected instruction, or poorly validated request could trigger dangerous behavior—such as exposing sensitive data, running unauthorized actions, or even spreading malware.
That’s where MCP call filtering comes in.
What Is MCP Call Filtering?
MCP call filtering is the process of inspecting and validating tool calls and their results before they are executed. Instead of trusting every request from an AI client, filters let IT teams apply custom business logic, security checks, or compliance rules at the gateway level.
At its core, filtering acts like a checkpoint between an AI tool and the system it’s trying to access:
- Interception – Every MCP tool call is intercepted by the gateway.
- Inspection – The request details are sent to a webhook for review.
- Decision – Based on custom logic, the webhook either accepts (200) or rejects (non-200) the call.
This provides enterprises with a way to ensure that only safe, compliant, and policy-approved tool calls are executed.
Why Filtering Matters: The Risk of Prompt Injection
AI-driven systems are particularly vulnerable to prompt injection attacks. An attacker (or even a well-meaning user) can manipulate input in a way that causes the model to generate unsafe tool requests. For example:
- A user pastes malicious instructions into a chat that trick the AI into calling internal MCP servers with unauthorized queries.
- A model is asked to retrieve information but is redirected to exfiltrate sensitive data.
- Excessively long or malformed inputs trigger unexpected tool behaviors.
Without a filter in place, these tool calls can pass through unchecked—creating serious security and compliance risks.
How Obot MCP Gateway Implements Call Filtering
Obot MCP Gateway includes a robust filtering framework that lets administrators define exactly how tool calls are inspected and controlled:
- Custom Webhook Filters – Admins configure a webhook endpoint to receive every MCP call request. This service can run custom logic (validation, logging, content checks, compliance rules) before allowing or rejecting a call.
- Selective Targeting – Filters can apply broadly, or only to specific MCP servers, tool names, or function calls.
- Payload Security – Filters can be configured with a shared secret, ensuring that all payloads are cryptographically signed and validated before processing. This prevents tampering and guarantees authenticity.
- Granular Enforcement – Calls that pass inspection return
HTTP 200and proceed as normal; anything suspicious returns a non-200 response and is blocked.
Example use cases include:
- Blocking queries that contain unsafe keywords (e.g., “download malware” or “bypass authentication”).
- Rejecting tool calls with unusually long inputs (potential injection attempts).
- Allowing only specific MCP functions for regulated workflows.
- Logging all outbound requests for compliance review.
Example: Filtering Unsafe Search Queries
A simple example is filtering calls to a search MCP. An Obot filter can reject queries containing dangerous terms (e.g., “how to make a bomb”) or those exceeding a safe length. This ensures that even if a model is tricked by prompt injection, the gateway enforces a last line of defense.
Why Obot’s Approach Matters
Most enterprises want to enable AI adoption without introducing new risks. By embedding MCP call filtering directly into the Obot MCP Gateway, organizations gain:
- Security – Protection against prompt injection, unsafe queries, and malicious calls.
- Governance – Centralized control over what tools can and cannot do.
- Auditability – Full logs of what requests were made, and which were blocked.
- Flexibility – Custom filters tailored to the organization’s policies and compliance needs.
Instead of relying on every MCP server to handle filtering individually (which is error-prone and inconsistent), Obot centralizes control at the gateway—giving IT confidence that all tool interactions are monitored and enforced.
Conclusion
MCP call filtering is critical for securing AI-driven systems in the enterprise. As MCP adoption accelerates, so do the risks of unsafe or unauthorized tool usage.
With Obot MCP Gateway, enterprises gain a powerful, open-source framework for filtering tool calls, defending against prompt injection, and ensuring that MCPs can be adopted securely and at scale.
