A
license-
qualityA
maintenanceEnables AI agents to execute multi-step Standard Operating Procedures step by step, with enforcement of completion at each step, making LLM behavior predictable and auditable.
3
Apache 2.0
AgenticRail Gate - sequence enforcement and verifiable audit receipts for AI agent compliance
Server Details
Deterministic runtime enforcement of step order for AI agents: ALLOW/DENY before a step runs.
- 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.
MCP client
Glama
MCP server
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.
100% free. Your data is private.
Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.5/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools serve completely distinct purposes: one evaluates a step for allow/deny, the other verifies the integrity of the receipt chain. No overlap or ambiguity.
Naming Consistency5/5
Both tools follow a clear verb_noun pattern (evaluate_step, verify_receipt), making the API predictable and easy to navigate.
Tool Count3/5
With only 2 tools, the server feels minimal but not unreasonable given its narrow focus on gating and verification. It sits at the thin edge of the appropriate range.
Completeness4/5
The domain is sequence enforcement and audit verification; evaluate_step covers enforcement and receipt generation, while verify_receipt covers auditing. Minor gaps exist (e.g., no explicit sequence management), but the core workflow is fully supported.
Available Tools
2 toolsevaluate_stepAInspect
Ask the AgenticRail gate to ALLOW or DENY a single step of an agent sequence BEFORE it runs. The gate is deterministic (same state+request → same verdict) and enforces step order, replay protection (nonce), timestamp freshness, and sealing. A denied step must not be executed. Every decision is sealed into an Ed25519-signed, hash-chained receipt. Returns the decision (ALLOW/DENY/HALT), any reason codes, and receipt metadata. Use the demo key by sending no Authorization header, or send Authorization: Bearer . NOTE: an anonymous call has its sequence_id rewritten to 'demo-mcp-'. This is intended, not a leak: it scopes the run to the public demo lane and is how anonymous MCP traffic is identified. Always reuse the sequence_id RETURNED in the response for later steps and for verify_receipt -- the id you sent will not resolve.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| step | Yes | The step being attempted, e.g. 'intake' or 'review_and_sign'. Must appear in step_order (for custom sequences) and must equal `function`. | |
| nonce | No | Optional unique-per-step UUID for replay protection. Generated automatically if omitted. | |
| action | No | Optional human-readable label for the action. | |
| inputs | No | Optional free-form inputs recorded with the decision. | |
| function | No | Defaults to `step`. The rule step === function always holds. | |
| step_order | No | The full ordered list of step names for a CUSTOM sequence. Send it on every call. Omit only if using AgenticRail's built-in MSMD spine. | |
| action_type | Yes | The action class for this step, e.g. CHECK_STATE, SELECT_NEXT_STEP, RECORD_RESULT, PAUSE_CYCLE. Must be permitted for the step. | |
| sequence_id | Yes | Stable identifier for this run of the sequence. Reuse it across every step of the same sequence. An anonymous call is rewritten to 'demo-mcp-<your id>' and an authenticated demo call to 'demo-<your id>'; take the id back from the response and use that one from then on. Give every run a fresh id: sealing is permanent, and on the shared demo lane a fixed id is shared with everyone else. |
Tool Definition Quality
A4.6/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility and delivers: it discloses determinism, enforcement of step order, replay protection, timestamp freshness, sealing, Ed25519-signed hash-chained receipts, and the anonymous sequence_id rewriting behavior. It even clarifies that rewriting is not a leak, which preemptively addresses a likely concern.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively long but well-structured: it leads with the core purpose, then behavior, then authentication, then a crucial note about sequence_id handling. Every sentence adds substantive value, and the length is justified by the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is thorough given the tool's complexity and lack of output schema: it explains the return values (decision, reason codes, receipt metadata), the deterministic behavior, the authentication modes, and the special handling of sequence_id. It leaves little ambiguity about how to invoke and interpret the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds valuable semantic context beyond the schema: it explains the sequence_id rewriting and the importance of using the returned id, clarifies the demo key behavior, and reinforces the invariant between 'step' and 'function'. This goes beyond basic parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to ask the AgenticRail gate for an ALLOW/DENY decision on a single step before execution. It specifies the resource (a step of an agent sequence) and distinguishes from the sibling tool by mentioning verify_receipt in the context of using the returned sequence_id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (before a step runs) and provides critical guidance: a denied step must not be executed, reuse the returned sequence_id for later steps and for verify_receipt. It does not explicitly state when NOT to use it, but the context is clear enough with only one sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_receiptAInspect
Fetch the verification report for an AgenticRail sequence and report whether its receipt chain is intact. Demo- sequences need no key; other sequences need Authorization: Bearer . Returns the verification_status (VERIFIED_INTACT / CHAIN_BROKEN / …) plus the per-receipt signature, chain-hash, and independent-archive checks. This is the same evidence a third party can verify offline against the published Ed25519 keys — no need to trust AgenticRail.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| sequence_id | Yes | The sequence to verify. Use a 'demo-' sequence for keyless verification. |
Tool Definition Quality
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses authentication requirements (Bearer key for non-demo sequences), the return values, and the fact that the evidence is offline-verifiable against published keys. This gives a good picture of the tool's behavior, though it could mention rate limits or idempotency, which are not critical here.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with no wasted words. It front-loads the purpose and includes essential details (authentication, return values) in a well-structured manner.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and no output schema, the description is complete. It explains authentication, the verification process, and the trust model (offline verifiability), leaving no obvious gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with a description for sequence_id. The description adds that demo- sequences allow keyless verification and that other sequences need Authorization. This is a minor addition beyond the schema, meeting the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches a verification report for an AgenticRail sequence and checks receipt chain integrity. It specifies the return values (verification_status, per-receipt signature, chain-hash, archive checks), differentiating it from the sibling tool evaluate_step which likely performs a different action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool: for verifying receipt chains. It mentions the distinction between demo-sequences (no key needed) and other sequences (Authorization required). It does not explicitly state when not to use the tool or suggest alternatives, but the context is 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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- Alicense-qualityAmaintenanceDeterministic policy enforcement for AI agent tool calls. It evaluates every tool call against user-defined rules before execution, with no LLM in the authorization path.3MIT
- AlicenseAqualityAmaintenanceProof-of-behavior enforcement for AI agents. Declare behavioral constraints, enforce at runtime, produce SHA-256 hash-chained audit trails. Supports covenants (permit/forbid/require), real-time verification, and cross-agent trust handshakes.438MIT
- Alicense-qualityBmaintenanceEnables step-debugging, deterministic replay, and signed audit evidence for AI agents, compliant with EU AI Act.MIT