Scan agent payment endpoints (x402, AP2, L402, MPP, 402) for a trust grade A-F before paying.
Verify a live 402 payment offer at the moment of spend. Pass the EXACT offer you just received (url, payTo, amount, asset, network) and get an allow / warn / block verdict with machine-readable reasons: wallet mismatch vs the verified payout wallet, price swaps vs the last verified quote, danger flags, blocklisted wallets, and typosquats. Call this AFTER receiving a 402 offer and BEFORE authorizing the payment.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The endpoint URL you are about to pay. | |
| asset | Yes | Asset contract address or symbol from the offer. | |
| payTo | Yes | The payout address presented in the live 402 offer. | |
| amount | Yes | The offered amount in atomic units, as a string. | |
| scheme | No | Optional payment scheme (e.g. exact). | |
| network | Yes | Network / chain identifier from the offer (e.g. base, solana). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes return verdict types (allow/warn/block) and reasons (mismatch, swaps, flags, blocklists, typosquats). No annotation provided, so description carries full burden; it does well but could mention if it mutates state (likely not).
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, front-loaded with purpose, no redundancy. Every word adds value.
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?
Despite no output schema, description explains return value format. Covers when to use, what to pass, and expected output for a 6-parameter tool with full schema coverage.
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% but description adds vital context: 'Pass the EXACT offer you just received', ensuring precise use. Also clarifies 'scheme' as optional beyond schema.
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 verifies a live 402 payment offer, with specific verb 'verify' and resource 'offer'. It distinguishes from sibling 'attest_scan' by focusing on spend-time verification.
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?
Explicitly says 'Call this AFTER receiving a 402 offer and BEFORE authorizing the payment', providing clear temporal context. Lacks explicit mention of when not to use or alternatives, but sibling context is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan an agent payment endpoint (x402 / HTTP 402) and return a letter grade A to F with a safety verdict before paying.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The endpoint URL to scan. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the return (grade and verdict) but does not disclose side effects, idempotency, or safety implications beyond the scan result. Moderate transparency.
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?
Single sentence that efficiently conveys purpose, resource, output, and usage timing. No wasted words.
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 tool's simplicity (1 param, no output schema), the description adequately covers purpose, input, and output. Minor gap: could mention whether the scan is safe or verbose, but overall sufficient.
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 a clear parameter description. The tool description does not add additional meaning or constraints beyond the schema, so 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 specifies the action (scan), the resource (agent payment endpoint with x402/HTTP 402 context), and the output (letter grade A-F plus safety verdict). It clearly distinguishes the tool's purpose.
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 states 'before paying' which implies when to use. However, it does not provide exclusions or alternatives, though no sibling tools exist. The context is clear.
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.
attest_guardattest_scanClaiming 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.
No comments yet. Be the first to start the discussion!
Verify x402 payment endpoints before an AI agent pays: scam scan, on-chain checks, trust scores.
Pre-payment merchant trust checks for x402 agents on Base and Solana.
Verified directory for machine payments (x402 & MPP): check services and wallets before agents pay.
The honest trust index for x402: check who you're paying before your agent pays.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: one verifies a specific 402 offer at spend time, the other scans an endpoint for a safety grade before paying. No overlap.
Both tools follow the same 'attest_' prefix with a verb (guard, scan), creating a predictable pattern.
With only 2 tools, the set is slightly small but appropriate for a focused payment verification utility; each tool serves a distinct and necessary step.
The two tools cover the core workflow of scanning an endpoint and verifying an offer. Minor gaps (e.g., historical checks) exist but the surface is adequate for the stated purpose.
