AML/compliance address screening for AI agent payments — free tools, no API key.
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 4.3/5 across 2 of 2 tools scored.
The two tools are unmistakably distinct: one returns a sample of flagged addresses, the other screens a single address. No overlap in purpose or output, so an agent cannot confuse them.
Both tools use snake_case and a two-word pattern, but 'recent_flagged' is an adjective-noun rather than verb-noun like 'screen_address'. Minor deviation, yet consistent style and clear intent.
With only two tools, the surface is thin but aligns with a narrow AML-specific purpose. It avoids overreach, though it feels minimal—borderline acceptable for the niche.
The core needs—viewing known flagged addresses and screening a counterparty—are covered. Gaps like bulk screening or detailed historical lookups exist, but they are non-critical for the primary use case.
recent_flaggedAReturn a representative sample of addresses on OceanAlt's reviewed risk list — OFAC-sanctioned, known mixers, and community-reported scam/phishing — each with its source category and reason. Takes no arguments. Free, no API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint; the description adds practical behavioral context beyond the annotation: it returns a 'representative sample' rather than an exhaustive list, includes source category and reason per address, and emphasizes the free/no-key nature. This is meaningful added transparency for agent decision-making.
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 compact and front-loaded: the first sentence states purpose and output contents, and subsequent short clauses cover constraints and access. 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 that there are no input params, no output schema, and annotations already cover safety, the description provides enough context: what is returned, what categories are included, and that it is a sample. It also clarifies access requirements, making the tool self-contained for an agent.
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 zero parameters and the schema already communicates this fully; the description reinforces it with 'Takes no arguments.' Since there are no parameter details to clarify, the baseline for a zero-parameter tool applies.
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?
Description clearly states a specific action ('Return') on a specific resource (OceanAlt's reviewed risk list), enumerating the categories of addresses and the per-item contents. It stands apart from the sibling screen_address tool because it provides a sample list rather than screening a single address.
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 gives clear usage context: no arguments are required and no API key is needed, making this an easy entry point. It does not explicitly name alternatives or say when not to use it, but for a zero-parameter read tool the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screen_addressARun an AML compliance screen on a single blockchain address BEFORE paying or receiving from it — the answer to "is this counterparty safe?". Checks OFAC sanctions, known mixers, community scam/phishing lists, stablecoin issuer freezes, and on-chain heuristics. Returns a verdict (clear | caution | risky), a 0–100 risk score, a blocked flag, and the matching signals — receipts, not a black-box score. Supports EVM (0x…), Tron (T…), Solana (base58). Free, no API key.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Address to screen. EVM: 0x + 40 hex. Tron: T + 33 base58. Solana: base58 public key. | |
| network | No | EVM chain (optional; auto-defaults to ethereum; ignored for Tron/Solana). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint, openWorldHint, and idempotentHint already in annotations, the description adds useful behavioral context above the annotations: it lists the screening sources, the outputs are explained as receipts rather than a black-box, and it discloses supported but distinct address families. It thoroughly states what the tool does not hide or normalize, and no contradictions with the annotations exist.
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 efficient and front-loaded: it opens with 'Run an AML compliance screen' then quickly covers raison d'être, data sources, purpose, output format, and free accessibility. Every sentence carries distinct information, though it is slightly richer than strictly necessary, so a near-top mark feels appropriate.
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 two-parameter tool with no output schema, the description gives a robust map of outputs (verdict, risk score, blocked flag, matching signals) and catches supported chains (EVM, Tron, Solana). It lacks such as timeout bounds, error handling for invalid addresses, or a note about non-determinism, but given the tool's surface area, the essentials are completely present.
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 input schema has 100% coverage for both parameters, so the baseline for parameter noise is already filled. The description adds supporting mention of EVM/Tron/Solana address patterns, confirming that address semantics are respected across chains, but it mostly restates what the schema already offers, so it adds marginal but non-essential semantic value.
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 opens by clearly stating the tool's function: run an AML compliance screen on a single blockchain address before payment, and frames it as an answer to a concrete safety question. It adds scoping ('single address'), which contrasts with the sibling tool recent_flagged, and lists exact inputs/outputs, all with a clear action and target resource.
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 gives strong guidance on when to use the tool ('BEFORE paying or receiving from it') and clarifies the kind of decision support it provides. It does not explicitly name the sibling tool as an alternative for multi-address/list-based checks, so it lacks a strict exclusions-by-sibling reference, but the context is unambiguous.
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.
No comments yet. Be the first to start the discussion!