Deterministic preflight checks for credentials, fund transfers, writes, and prompt overrides.
Runs a deterministic, read-only preflight check. It never executes the proposed action.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds behavioral guarantees beyond the annotations: 'deterministic' and 'never executes the proposed action.' This clarifies call safety and repeatability even though readOnlyHint, idempotentHint, and destructiveHint already cover some of the same ground.
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 short, front-loaded sentences with no filler. Every sentence adds behavioral or safety-relevant 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?
Annotations and the 'preflight check' phrase establish safety and rough purpose, but with no output schema the description still does not say what the tool returns. The content parameter is also left to guesswork. Given the tool's simplicity this is adequate but not complete.
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 schema has 0% description coverage, and the description only weakly maps 'the proposed action' to the action parameter. It says nothing about the optional content parameter, its format, or how it affects the risk assessment.
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?
States a specific action ('runs') on a distinct resource ('preflight check' / 'action risk'), and emphasizes that it does not execute the proposed action. It is clearly differentiated from get_guardrail_capabilities by intended purpose, though it does not name the sibling explicitly.
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?
'Preflight check' implies the tool should be used before executing a proposed action, and 'read-only' signals it is safe to call. However, there is no explicit when-to-use guidance or mention of when to prefer the sibling get_guardrail_capabilities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the scope and safety boundary of this free, deterministic preflight checker.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, and the description adds 'deterministic' and 'preflight' context plus a concrete return purpose. It does not contradict the annotations and adds useful behavioral context beyond a bare read flag.
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?
One sentence, no filler, with the key information front-loaded in the first phrase. Every word earns its place.
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?
This is a very low-complexity invocation: no parameters, no output schema, and a robust set of annotations covering side effects. The description's high-level statement of what is returned is sufficient for an agent to call it 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?
The tool takes zero parameters, so there is nothing for the description to explain; the empty schema already covers this fully. Baseline 4 applies because no parameter documentation 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 names a specific verb ('Returns') and a specific resource ('scope and safety boundary' of the preflight checker), so an agent can see this is a read-only capability/metadata tool. It does not explicitly contrast itself with assess_action_risk, so it stops short of full sibling differentiation.
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 phrasing implies the tool is used to inspect the checker's scope and safety boundary before or instead of running risk assessment. There is no explicit when-to-use versus assess_action_risk guidance, so selection must be inferred from the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
assess_action_riskget_guardrail_capabilitiesClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Stop your agent before it runs rm -rf /etc or emails your .env. Deterministic preflight checks.
Risk preflight for AI Agent tool actions and Polymarket settlement and execution checks.
Advisory policy preflight for AI-agent spend requests; never executes payments or accesses wallets.
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
Add one secure layer between your agents and this server.
The two tools have clearly distinct purposes: one assesses action risk, the other describes the guardrail's capabilities. There is no overlap or ambiguity between them.
Both tools use a consistent verb_noun pattern: 'assess_action_risk' and 'get_guardrail_capabilities'. The naming is predictable and follows a clear convention.
With only two tools, the server feels thin even for a narrowly scoped guardrail checker. The count is borderline, but the limited purpose partially justifies it.
The core workflow of checking capabilities and assessing risk is covered. Minor gaps exist, such as no explicit way to list individual guardrail rules, but the surface is not a dead end.
