IntentFence Policy Gate
Server Details
Spend, scope, data, and approval policy gate for autonomous AI actions.
- 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
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Tool access control
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Managed credentials
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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 4/5 across 1 of 1 tools scored.
Only one tool exists, so there is no risk of confusion with other tools.
With a single tool, naming is trivially consistent.
One tool is appropriate for a simple policy gate, but expanding to include policy management tools could improve the server's utility.
The single tool covers the core functionality of evaluating constraints before agent calls; no obvious gaps for the stated purpose.
Available Tools
1 toolintentfence_preflightIntentFence PreflightARead-onlyIdempotentInspect
Evaluate spend, scope, data, and approval constraints before an agent tool call. Returns safe_to_proceed, needs_review, or denied.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| proofs | No | ||
| subject | Yes | Agent or principal identifier. | |
| constraints | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the key behavioral trait of returning a status result (safe_to_proceed, needs_review, denied), which is not captured in the annotations. No contradictions.
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 efficiently conveys the tool's purpose, scope, and output. Every word adds value, and the structure is front-loaded with the key action.
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?
The description explains the return values, which is important since there is no output schema. However, it does not elaborate on how constraints are evaluated or how proofs are used, leaving gaps for an agent to understand full behavior.
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 description coverage is only 25% (only 'subject' has a description). The tool description does not explain the meaning or usage of parameters like 'action', 'proofs', or 'constraints', leaving the agent to infer from the schema alone. For a tool with nested objects, more guidance is needed.
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 verb 'evaluate' and the resources (spend, scope, data, approval constraints), and specifies the three possible return values (safe_to_proceed, needs_review, denied). It fully defines the tool's 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 phrase 'before an agent tool call' provides clear context for when to use the tool. While no exclusions or alternatives are given, the absence of sibling tools makes this guidance sufficient.
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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