CardGuard is a high-performance MCP-compliant service designed for real-time transaction fraud detection and IP threat intelligence. It provides microsecond-level scoring and multi-dimensional analysis (IP geolocation, BIN validation, and risk pattern recognition) for autonomous AI agents
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Average 3.8/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: evaluating transaction risk, IP intelligence, monitoring payment attempts, and subscription verification. No two tools overlap in function.
Tool names use different verbs ('evaluate', 'lookup', 'sense', 'verify') without a consistent pattern. While all are snake_case, the verb choice is varied and not predictable.
With 4 tools covering fraud evaluation, IP lookup, attempt monitoring, and subscription verification, the count fits the server's focused scope well.
Core operations for fraud risk assessment are covered, but there is a minor gap in detailed reporting for specific attempts. Subscription verification is included, but integration between the two domains could be deeper.
evaluate_transaction_riskAInspectEvaluate transaction fraud risk across multiple dimensions.
Combines IP geolocation intelligence, BIN validation, Luhn algorithm,
and amount patterns to output an allow/review/block decision and scoring rationale.
Args:
ip: Transaction initiator IP address.
email: Buyer email address.
card_bin: Bank card BIN (first 6-8 digits).
card_last4: Bank card last 4 digits.
amount_cents: Transaction amount in USD cents.
Returns:
Risk evaluation result containing decision, risk_score, reasons, geo_country, and geo_org.
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes | ||
| Yes | |||
| card_bin | Yes | ||
| card_last4 | Yes | ||
| amount_cents | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations exist, the description fully discloses that the tool combines multiple data sources and returns a decision, risk score, reasons, and geo details. It transparently lists the output fields, though it does not discuss potential side effects, rate limits, or dependency on external services.
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 well-organized with a summary paragraph, an Args list, and a Returns note. It is slightly verbose but every sentence provides value. Could be more concise by omitting redundant phrasing, but overall structure supports readability.
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 complexity (5 required params, no output schema, no annotations), the description covers the tool's purpose, algorithms, input parameters, and output fields. It lacks examples, threshold explanations, or error handling details, but provides sufficient information for an AI agent to use the tool 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?
Schema description coverage is 0%, so the description must add meaning to parameters. It does so thoroughly: each parameter (ip, email, card_bin, card_last4, amount_cents) receives a brief but clear definition (e.g., 'Transaction initiator IP address', 'Bank card BIN (first 6-8 digits)'), significantly enhancing usability beyond the bare 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 evaluates transaction fraud risk across multiple dimensions using specific algorithms (IP geolocation, BIN validation, Luhn, amount patterns) and outputs a decision with scoring rationale. It is distinct from sibling tools that focus on narrower aspects like IP intelligence or specific fraud attempts.
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 implies a comprehensive fraud evaluation use case but provides no explicit guidance on when to use this tool versus alternatives (e.g., for simple IP checks or specific fraud types). No when-not-to-use or comparative context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_ip_intelligenceAInspectIP threat filtering and geolocation lookup.
Identifies Tor exit nodes, anonymous VPNs, residential proxies, data center IPs,
satellite providers, and high-risk infrastructure, returning a complete GeoProfile.
Args:
ip: IP address to lookup (IPv4/IPv6).
Returns:
Intelligence profile containing country_iso, org, asn, and various threat flags.
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses output fields (country_iso, org, asn, threat flags) and threat categories, but does not mention error handling, data freshness, or any side effects. Since no annotations exist, description carries full burden and could be more comprehensive.
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 clear paragraphs with bullet-like list of threat sources and a structured Args/Returns section. Every sentence adds value, no redundancy.
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 simple lookup tool with one parameter and no output schema, the description adequately covers purpose, input, and output. Could be improved with error examples or more detail on threat flags, but generally 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 parameter 'ip' in schema only specifies type 'string', but description adds 'IPv4/IPv6', clarifying acceptable formats and providing value beyond the 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?
Clearly states it performs IP threat filtering and geolocation lookup, listing specific threat types (Tor, VPNs, proxies) and specifying output as a GeoProfile. Distinct from siblings which evaluate transaction risk or agent 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?
No explicit guidance on when to use this tool vs alternatives. It is implied that it is for IP intelligence, but with siblings like evaluate_transaction_risk, it would benefit from stating prerequisites or order of operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sense_402_attemptsAInspectQuery the CardGuard Node Sensing Chain — inspect recent 402 rejection attempts and threat profile metrics.
Args:
limit: Max number of recent attempts to retrieve (default 20).
Returns:
Summary statistics of 402 payment attempts and recent rejection log entries.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It does not mention read-only vs destructive nature, authentication requirements, rate limits, or side effects. The description is insufficient for safe invocation.
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 concise with a clear Arg/Returns structure. Every sentence adds value, no redundancy. It front-loads the main purpose effectively.
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?
With no output schema, the description should detail the return format. It mentions 'summary statistics' and 'log entries' but lacks specifics on structure or fields, leaving ambiguity for the 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 description explicitly documents the limit parameter (type, default, purpose) in the Args section. Despite schema description coverage being 0%, the description compensates fully for the single parameter, adding meaning beyond the 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 inspects recent 402 rejection attempts and threat profile metrics using the CardGuard Node Sensing Chain. It uses a specific verb ('inspect') and resource, distinguishing it from sibling tools like evaluate_transaction_risk and lookup_ip_intelligence.
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?
No guidance is provided on when to use this tool versus siblings or alternatives. It lacks context on prerequisites, conditions, or exclusions, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_agent_for_subscriptionAInspectPre-verify agent Web3 wallet authenticity or whitelist status to unlock SaaS checkout URL ($199/month).
Args:
wallet_address: EVM wallet hex address (0x...) of the agent or orchestrator.
agent_id: Optional agent identifier (e.g. smithery-ai, coaxon-kernel).
Returns:
Verification status and the Polar checkout subscription URL if verified.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | No | ||
| wallet_address | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool 'pre-verify' and returns verification status and checkout URL if verified, but does not disclose important behaviors such as whether the operation is read-only, potential side effects, rate limits, or error handling. This lack of detail leaves the agent uncertain about side effects.
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 relatively concise: two sentences plus an Args and Returns section. The main purpose is front-loaded. However, the Args section somewhat duplicates the schema, but still adds clarity. No unnecessary 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 lack of annotations, output schema, and only two simple parameters, the description covers the essential: purpose, parameters, and return value. However, it omits error conditions, authentication requirements, and more detail on the verification workflow, which would improve completeness for a tool that unlocks a payment flow.
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?
With 0% schema description coverage, the description compensates by explaining both parameters: wallet_address (EVM hex address) and agent_id (optional with examples). This adds meaning beyond the bare schema, though it could include more validation rules or format hints.
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's purpose: pre-verify agent Web3 wallet authenticity or whitelist status to unlock a SaaS checkout URL. It uses a specific verb ('verify') and resource ('agent for subscription'), and is clearly distinct from sibling tools like evaluate_transaction_risk or lookup_ip_intelligence.
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 implies when to use (when needing to check agent eligibility for subscription), but does not explicitly state when not to use or mention alternatives. While siblings are different enough, no explicit guidance on exclusions or prerequisites is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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