injection-detector
Server Details
Formally-verified injection/exfiltration detector for AI agents (MCP-02).
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of ambiguity. The tool's purpose is clearly distinct.
With a single tool, naming consistency is inherent. The name 'detect_injection' follows a clear verb_noun pattern.
One tool is appropriate for a focused server that screens untrusted input for injection attacks. The scope is narrow and well-defined.
The tool provides a comprehensive output (verdict, probability, bits-at-risk, patterns, recommended action) covering detection needs fully. No obvious gaps.
Available Tools
1 tooldetect_injectionDetect adversarial injectionAInspect
Screen untrusted input for prompt/tool injection, exfiltration, and obfuscation before an agent consumes it. Returns a verdict (clean|suspicious|attack), probability, bits-at-risk (upper bound on adversarial capture per the Adversarial Landauer bound), matched canon patterns, and a recommended action (allow|sanitize|reject|escalate). Backed by Aristotle-verified theorems T-IB-02/T-IB-06/T-IB-01.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The untrusted text/data to screen. | |
| agentId | No | Optional: for MCP-01 envelope cross-check. | |
| context | No | Optional: the agent's role/system prompt; helps calibrate. | |
| certainty | No | Operating point. Default standard. |
Output Schema
| Name | Required | Description |
|---|---|---|
| signals | No | |
| verdict | Yes | |
| backedBy | No | |
| bitsAtRisk | Yes | |
| probability | Yes | |
| operatingPoint | No | |
| matchedPatterns | No | |
| recommendedAction | Yes | |
| explainabilityToken | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool returns a verdict, probability, bits-at-risk, matched patterns, and recommended action. It mentions underlying theorems, adding credibility. It does not explicitly state side-effect freeness, but 'Screen' implies read-only inspection. This is good transparency, missing only a clear statement of non-destructiveness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each earning its place. First sentence states purpose, second details output. No fluff, front-loaded with the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and the description lists all returned fields, the description is complete. With 4 parameters all documented in schema, and the description providing context on threat types and supported theorems, the agent has sufficient information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with all parameters having descriptions. The description adds little beyond summarizing the output struct. It does not elaborate on parameter usage or constraints beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool screens untrusted input for prompt/tool injection, exfiltration, and obfuscation, with a specific verb ('Screen') and resource ('untrusted input'). It lists the types of attacks checked and the output components, fully defining its purpose without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use the tool: 'before an agent consumes it' when input is untrusted. It implies the tool should be used for safety screening, but does not explicitly state when not to use it or provide alternative tools. However, with no siblings listed, this is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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