Governance firewall for autonomous AI agents. Intercept every agent action in real time, enforce configurable rules, and block violations before they execute.
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.
Average 3.9/5 across 3 of 3 tools scored.
Each tool serves a distinct purpose: checking rules, fetching logs, and sending events. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case: check_guardrails, get_feed, log_intercept.
Three tools is well-scoped for the server's focused domain of guardrail enforcement and audit logging.
The surface covers the full workflow: check rules before acting, log the action, and retrieve the feed later. No obvious gaps for this domain.
check_guardrailsAInspectList the currently active guardrail rules for the Gateplex project tied to the API key, so an agent can self-check a planned action before executing it.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. 'List' implies a read-only operation, but the description does not explicitly state side-effect-free behavior, return format, or any limitations. The mention of 'currently active' indicates state-dependent results, which is useful context.
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?
The description is a single sentence that is front-loaded with the action and resource, then gives the rationale. Every clause earns its place with no fluff.
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?
For a simple read-only list tool with no parameters, the description provides the essential information: what it lists, for which project, and why. It lacks explicit return structure details, but the nature of 'guardrail rules' gives some implicit expectation, and no output schema is provided.
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?
The tool has no parameters (0 declared), so the baseline is 4. The description adds meaningful context by noting the API key ties the request to a specific project, which explains how the tool knows which guardrails to list.
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 action (List), the resource (currently active guardrail rules), and the context (Gateplex project tied to the API key). It also explains the intended use for self-checking, which distinguishes it from sibling tools like get_feed or log_intercept.
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?
It specifies a clear context: use before executing a planned action to self-check guardrails. However, it does not explicitly mention when not to use it or compare to alternatives, though the sibling tools are clearly different in function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_feedAInspectRetrieve the latest intercepts (default 10) for the Gateplex project tied to the API key.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the default limit and API-key scoping, but does not explicitly mention side effects (though 'retrieve' implies read-only), return format, or error handling. Some behavioral context exists but it is not comprehensive.
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?
The description is one sentence, front-loaded with the primary action, and contains no unnecessary words. It is appropriately concise.
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?
For a simple read tool with one optional parameter and no output schema, the description covers the core purpose and default value. Missing return format and error handling, but the tool's simplicity makes this acceptable.
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?
The single parameter 'limit' has 0% schema description coverage, so the description must compensate. The phrase 'default 10' connects the parameter to the number of intercepts, but it does not explicitly explain the parameter's purpose beyond what the schema already provides (type, min, max, default).
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 uses a specific verb 'retrieve' and resource 'latest intercepts', and identifies the scope (Gateplex project tied to API key). It clearly distinguishes from sibling tools 'log_intercept' (writing) and 'check_guardrails' (checking).
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?
Clear context is provided: this tool retrieves data for the project tied to the API key. It does not explicitly state exclusions or alternatives, but the verb 'retrieve' naturally differentiates it from sibling tools like 'log_intercept'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
log_interceptAInspectSend an intercept event to the Gateplex API. Use this every time an AI agent performs an action that should be logged, governed, or audited.
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | Raw input/prompt | |
| model | No | Model identifier, e.g. 'gpt-4o' | |
| output | No | Raw output | |
| flagged | No | ||
| agent_id | No | UUID of the agent generating this intercept | |
| metadata | No | ||
| event_type | Yes | Event type, e.g. 'llm_call', 'tool_call', 'http_request' | |
| latency_ms | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'send an intercept event' but does not mention side effects, persistence, authentication, error behavior, or consequences of calling this tool. This is a significant transparency gap for a remote API call.
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 concise sentences, front-loaded with the verb and resource. Every word earns its place; no redundancy.
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?
For an 8-parameter tool with nested objects and no output schema/annotations, the description is too minimal. It does not explain return values, errors, or usage nuances. The description is incomplete for an agent trying to invoke this tool correctly in varied situations.
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 moderate (63%). The description adds no extra meaning to parameters; it does not clarify `flagged`, `latency_ms`, or `metadata`. The schema descriptions for many parameters are adequate, so baseline 3 applies, but the description does not compensate for undocumented parameters.
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 states a specific action ('Send an intercept event') and destination ('to the Gateplex API'), clearly distinguishing it from sibling tools (check_guardrails, get_feed). It conveys the resource and operation unambiguously.
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?
Provides explicit usage context: 'Use this every time an AI agent performs an action that should be logged, governed, or audited.' This gives clear when-to-use guidance, though it does not mention when-not-to-use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$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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