lemon-cake-mcp is an MCP server that gives AI agents a USDC wallet to autonomously discover, pay for, and call premium HTTP APIs without human intervention.
Setup guidance (setup): Detects missing credentials and provides step-by-step onboarding instructions, including a ready-to-paste MCP config snippet for obtaining a Buyer JWT and Pay Token.
Discover APIs (list_services): Browse the LemonCake marketplace for approved paid API/MCP services, including their IDs, providers, types, per-call USDC pricing, and endpoints.
Call paid services (call_service): Invoke any upstream API through LemonCake's pay-per-call proxy, automatically charging USDC per successful call. Supports GET/POST/PUT/PATCH/DELETE, custom sub-paths, JSON bodies, and idempotency keys for safe retries without double-charging.
Check account balance (check_balance): View your current USDC balance, KYC/KYA tier, and account info to confirm sufficient funds before making calls.
Japanese tax compliance (check_tax): Validate a Japanese qualified-invoice (γ€γ³γγ€γΉ) registration number against the NTA registry, determine if source-withholding (ζΊζ³εΎ΄ε) applies, and calculate withholding amounts and net payable.
Marketplace usage stats (get_service_stats): Access public usage statistics (call counts, total USDC revenue, last-called timestamps) to help choose the best service.
Demo mode: Run without any credentials to explore demo services (demo_search, demo_echo, demo_fx) with canned responses and a mock $1.00 balance β no signup required.
Allows AI agents to search and retrieve content from Wikipedia via a pay-per-call API proxy, with USDC-based billing and no manual authentication.
Let your AI agent pay for any API β capped, no account.
Give your agent a spend-capped Pay Token and it pays for paid APIs on its own. Discover β pay β pass through. No per-call key, no human in the loop, and it can't exceed your cap. First 3,000 calls free, then 3%. Seller gets 97%. LemonCake never holds your funds.
Want to monetize your own MCP server or API? Start at lemoncake.xyz/app or copy the mcp-monetization-starter.
π° No monthly fee. First 3,000 calls free (lifetime). Then 3% only when your API earns. See pricing β
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (or Cursor / Cline):
{
"mcpServers": {
"lemon": {
"command": "npx",
"args": ["-y", "agent-payment-mcp"]
}
}
}Restart, then ask:
"use lemon to translate 'Hello, agent payments' to Japanese"
Zero env vars. Zero signup. Zero credit card. Demo Mode activates automatically.
Related MCP server: BotWallet MCP Server
Tool | What it does | Upstream |
π | Search Wikipedia (5 results + URLs) | en.wikipedia.org |
π± | Live FX rates (160+ currencies) | open.er-api.com |
π | Translate 80+ languages | MyMemory |
π€ | Current weather for any lat/lon | Open-Meteo |
π | Place name β coordinates | OpenStreetMap Nominatim |
π | Current time + DST for any IANA timezone | worldtimeapi |
π | English definitions / synonyms | dictionaryapi.dev |
π | HTTP echo (request inspector) | httpbin.org |
LemonCake speaks x402 β the HTTP 402 payment protocol for AI agents.
Agent β POST /g/<endpoint>
β
402 + accepts[] { pricePerCall, buyUrl, mintUrl }
β
Agent mints a Pay Token (off-session, capped)
β
Bearer <jwt> β gateway β your APISeller registers any HTTP API at lemoncake.xyz/app and sets a price per call
Buyer prepays via card β Pay Token (JWT) issued automatically
Agent passes Authorization: Bearer <token> β gateway verifies, meters, forwards
Budget exhausted β gateway returns 402 + accepts[] so agents can self-fund
For fully autonomous operation with no human per-session:
Issue a Buyer Key (bk_...) in the Pay Tokens pane at /app
Save a card once at /agent/fund
Agent uses bk_ to mint/top-up Pay Tokens off-session β hard-capped per your limits
Agent β POST /api/lc/agent/tokens (Bearer bk_...)
β off-session card charge β Pay Token issued
β Bearer <jwt> β gateway β pass throughCaps enforced server-side: per-mint / daily / monthly. Cannot overspend.
Monetize any HTTP API or MCP server in minutes:
Sign in at lemoncake.xyz/app
Add API β paste your URL, set price per call
Share the buy link β buyers prepay with a card, Pay Token issued automatically
You keep 97%. LemonCake takes 3% once at checkout. Never holds funds (Stripe Connect Direct Charge).
// Your tool code is unchanged β LemonCake sits in front as a gateway
class MyTool extends MCPTool {
// existing logic β no SDK required
}
// Route traffic through: https://lemoncake.xyz/g/<shortId>Japan FSA Fintech Support Desk (2026-06) confirmed: no electronic payment means management registration required. LemonCake never holds funds (Stripe Connect Direct Charge, custody-free).
Jurisdiction | Basis |
π―π΅ Japan | FSA confirmed β registration not required |
πΊπΈ USA | FinCEN 2019 Β§4.5 β non-custodial software β MSB |
πͺπΊ EU | MiCA β non-CASP |
π¬π§ UK | FCA β Tech Service Provider |
πΈπ¬ Singapore | MAS β DPT non-applicable |
π¨π¦ Canada | FINTRAC β non-custodial exemption |
π¨π Switzerland | FINMA β non-financial intermediary |
Full posture: lemoncake.xyz/security
Package | Use |
Main MCP β x402 gateway + agent payment rail (this one) | |
SDK to monetize your own MCP server | |
Buy tokenized US stocks on Solana | |
Alpaca paper / live trading with hard USD cap | |
Dinari dShares |
Server-side hard caps β per-mint / daily / monthly limits enforced on the server. Cannot be exceeded.
Pay Token = JWT β signed HS256, verified on every gateway call. Not a blockchain asset.
No private keys in the MCP server β Buyer Key (bk_) has hashed secret, PM reference only (no raw card data).
Stripe Connect Direct Charge β funds go seller-direct. LemonCake never holds customer funds.
RLS on all DB tables, upstream_auth never returned in API responses.
Try demo |
|
Seller dashboard | |
Agent card setup | |
Docs | |
Source | |
MCP Registry | |
License | MIT |
Invoke an upstream API service through LemonCake's pay-per-call proxy. Each successful call automatically debits USDC from your wallet via the permit you signed. LemonCake never holds your USDC β the transfer is direct wallet β provider on Base.
PRECONDITIONS:
β’ LEMON_CAKE_PERMIT env var must be set for real services. Get one in ~30 seconds at
https://lemoncake.xyz/start/v2 (sign in with Google, sign 1 EIP-712 permit, copy the blob).
If missing, the tool returns a structured PERMIT_MISSING error with how-to-fix steps.
β’ DEMO MODE: any serviceId starting with demo_ works WITHOUT a permit and hits real
free upstreams (Wikipedia / httpbin / Open-Meteo / Nominatim / MyMemory / dictionaryapi /
worldtimeapi / open.er-api). 8 demos cover search / echo / fx / translate / weather /
geocode / time / dictionary β useful for Glama Inspector or new-user trial. They are
marked with mode: "demo" and incur no charge.
β’ serviceId must come from list_services.
BEHAVIOR: β’ Returns the upstream response body verbatim (JSON or text), plus the X-Charge-Id and X-Amount-Usdc headers reported by the proxy. β’ HTTP 402 Payment Required is returned as a normal result (NOT thrown) so the agent can autonomously stop spending when the daily $25 permit cap is exhausted. β’ Pass the same idempotencyKey to retry safely without double-charging. β’ This tool spends real money and contacts an external service β it is non-idempotent by default and has external side effects.
x402-COMPATIBLE INTERFACE:
β’ Successful calls include an x402Receipt field with { scheme, chain, asset, amount,
recipient, paymentIntentId, settledAt }. Same shape as on-chain x402 receipts so the
agent's payment-handling logic is portable.
β’ If upstream returns an x402 challenge (WWW-Authenticate: x402, X-402-* headers, or
body.x402), it's parsed into x402Challenge for the agent to reason about.
β’ If upstream returns 202 + Retry-After + X-Payment-Status: pending, the result is
{ status: "PAYMENT_PENDING", paymentIntentId, retryAfterMs, retryContract }.
Re-call with the same idempotencyKey to resume β no double-charge.
Returns: { status, chargeId, amountUsdc, response, x402Receipt?, x402Challenge?, hint? }
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | JSON request body (only used for POST/PUT/PATCH). | |
| path | No | Sub-path on the service (e.g. "/search", "/v1/completions"). Defaults to "/". | / |
| method | No | HTTP method to use against the service. Defaults to GET. | GET |
| serviceId | Yes | ID of the service to call (obtain from list_services). | |
| idempotencyKey | No | Optional idempotency key (UUID recommended). Identical keys within the proxy's retention window return the cached result without re-charging. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description elaborates on behavioral traits beyond annotations: it details payment mechanics, HTTP 402 handling, idempotency via key, x402 interface, and demo mode. It confirms non-idempotent, external side effects, and spending moneyβfully consistent with annotations.
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?
Description is well-structured into sections (Preconditions, Behavior, x402-COMPATIBLE INTERFACE, Returns) and front-loads the core purpose. It is somewhat lengthy due to the tool's complexity, but each sentence adds value. Minor redundancy could be trimmed.
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 no output schema, the description fully explains the return format and error states (PERMIT_MISSING, HTTP 402, payment pending). It covers all aspects: preconditions, behavior, demo mode, x402 handling, and retry logic. No gaps.
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 100%, so the schema already documents each parameter well. The description adds context like idempotencyKey retry logic and serviceId sourcing, but does not significantly enhance per-parameter understanding beyond what the schema provides.
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: 'Invoke an upstream API service through LemonCake's pay-per-call proxy.' This specific verb+resource, combined with detailed behavioral context, distinguishes it from sibling tools like check_balance, list_services, and setup.
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?
Description provides explicit preconditions (LEMON_CAKE_PERMIT env var, demo mode, serviceId from list_services) and behavior (spends money, external side effects). It indirectly guides when to use this tool vs alternatives by stating serviceId must come from list_services. Could be slightly more explicit about when not to use it, but is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the current on-chain USDC balance of the wallet that owns the configured permit.
PRECONDITIONS: β’ DEMO MODE (no LEMON_CAKE_PERMIT): returns a canned $1.00 demo balance so trial users see something useful before signing. β’ LIVE: queries Base USDC.balanceOf(owner) directly + reports remaining daily permit cap. LemonCake's backend doesn't store the balance β it's read from the chain.
Use BEFORE call_service to confirm sufficient funds and remaining daily cap.
Returns: { balanceUsdc, dailyCap, remainingToday, ownerAddress, [mode], [note] }
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent), the description discloses demo mode behavior (canned $1.00) and live mode (queries chain, reports daily cap). It also notes that the backend does not store balance.
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?
Well-structured with clear sections (preconditions, usage, returns). Front-loaded with main purpose. No redundant sentences.
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?
Complete for a zero-param read-only tool. Describes all relevant behaviors, return fields, and usage context. No gaps.
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?
No parameters exist; schema coverage is 100%. The description adds value by detailing the return fields (balanceUsdc, dailyCap, etc.), compensating for the lack of output 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 specifies the exact verb 'check' and resource 'on-chain USDC balance of the wallet that owns the configured permit.' It clearly distinguishes from sibling tools like call_service or check_tax.
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 recommends using this tool BEFORE call_service to confirm funds and daily cap. Also outlines preconditions for demo vs. live mode, providing clear context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a Japanese tax compliance check on a single transaction. No authentication required.
Performs three checks in one call:
Validates the qualified-invoice registration number (T-number) against the NTA registry.
Determines whether source-withholding (ζΊζ³εΎ΄ε) applies based on the service description.
If withholding applies, computes the withholding amount and net payable.
Intended for Japanese corporations that pay AI / API services and need to file withholding correctly under the qualified-invoice (γ€γ³γγ€γΉεΆεΊ¦) regime.
Returns: { invoice: { valid, name, ... }, withholding: { required, rate, amount, net } } Errors: invalid registrationNumber returns invoice.valid = false (not an exception).
| Name | Required | Description | Default |
|---|---|---|---|
| grossAmountJpy | Yes | Gross transaction amount in JPY, tax inclusive. | |
| registrationNumber | Yes | Qualified-invoice registration number issued by the Japanese NTA (e.g. "T1234567890123"). | |
| serviceDescription | Yes | Plain-text description of what was purchased. Used to classify whether source-withholding applies. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant context beyond annotations: it states no authentication required, details the three checks performed, and explains error handling (invalid registrationNumber returns invoice.valid = false, not an exception). This aligns with the readOnlyHint and idempotentHint.
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-structured with a bullet list of checks, clear sections, and no unnecessary words. It is concise yet informative.
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 having no output schema, the description provides a complete picture: it explains the three checks, the return object structure, error handling, and intended usage. For a complex tax compliance tool, this is highly 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?
Schema coverage is 100%, so baseline is 3. However, the description adds detailed meaning: it explains how each parameter is used in the three checks and describes the return object structure (invoice and withholding fields), which goes 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's purpose: 'Run a Japanese tax compliance check on a single transaction.' It lists three specific checks and differentiates from sibling tools, as no other tax check tool exists among the siblings.
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 explicitly states the intended use case: 'Japanese corporations that pay AI / API services' and notes that no authentication is required. It provides context for when to use it, though it could be more explicit about when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return public usage statistics for every approved service on the marketplace. No authentication required.
Use this AFTER list_services and BEFORE call_service to pick a service based on real-world traction (call counts, USDC revenue, last-used timestamp).
Returns: an array of { serviceId, callCount, totalRevenueUsdc, lastCalledAt }.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, idempotentHint=true. The description adds that no authentication is required and specifies the output format, adding value beyond annotations.
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?
Three sentences: purpose, usage guidance, return format. Front-loaded with key information, 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?
For a tool with no parameters and no output schema, the description fully explains functionality, usage sequence, and return structure. Adequate for the complexity.
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?
No parameters, so baseline is 4. The description does not need to elaborate on parameters.
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 returns public usage statistics for every approved service. It uses specific verb 'Return' and resource 'usage statistics for every approved service', and distinguishes from siblings by providing ordering context.
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 states when to use this tool: 'Use this AFTER list_services and BEFORE call_service to pick a service based on real-world traction.' This provides clear context and exclusion of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List approved API services available on the LemonCake marketplace. No authentication required.
Use this BEFORE call_service to discover serviceId values and per-call USDC pricing.
When LEMON_CAKE_PERMIT is missing, 8 demo services are prepended: demo_search β Wikipedia opensearch demo_echo β httpbin.org/anything demo_fx β live FX rates (open.er-api) demo_translate β 80+ languages (MyMemory) demo_weather β current weather any lat/lon (Open-Meteo) demo_geocode β place name β lat/lon (OpenStreetMap Nominatim) demo_time β IANA timezone time + DST (worldtimeapi) demo_dictionary β English definitions / synonyms (dictionaryapi.dev) All real upstreams, no auth, free. Live users (permit set) see real marketplace entries; demo_* IDs remain callable directly.
Each item: { id, name, provider, type ('API' | 'MCP'), pricePerCall, [usage], [mode] }.
Errors: HTTP-level errors are returned as Error: API <status>: <body>.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of services to return (default 50, max 100). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: no authentication required, demo services prepended when permit missing, error format, and output structure. No contradictions with annotations.
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-structured with a clear purpose statement, usage direction, and detailed list of demo services. It is slightly lengthy but each sentence adds value, and bullet-like formatting aids 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?
For a list tool with no output schema, the description includes output structure (fields), error handling, and relationship to sibling tools. It covers the demo behavior comprehensively, making it complete for an AI 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 only parameter 'limit' is fully described in the schema (type, default, min, max, description). The description does not add additional meaning beyond what the schema provides, so baseline score of 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 clearly states 'List approved API services available on the LemonCake marketplace' with a specific verb and resource. It distinguishes from sibling tools like call_service by explicitly mentioning its role as a prerequisite for discovering serviceId values.
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 explicitly advises to 'Use this BEFORE call_service to discover serviceId values and per-call USDC pricing' and explains the demo service behavior when LEMON_CAKE_PERMIT is missing. It does not explicitly say when not to use it, but 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.
Show the LemonCake MCP first-run setup guide. No authentication required. Call this tool FIRST to learn what is missing and how to obtain a permit.
If LEMON_CAKE_PERMIT is not set, the server is in DEMO MODE: list_services returns 8 free demo services (search / echo / fx / translate / weather / geocode / time / dictionary) powered by real upstreams (Wikipedia, httpbin, Open-Meteo, Nominatim, etc.). call_service and check_balance respond with mock data so you can verify integration before signing.
Returns the current credential status (permit presence), demo-mode flag, and step-by-step instructions for getting a permit at /start/v2, including a sample MCP client config snippet ready to paste.
Returns: { version, apiUrl, mode, credentials, availableTools, setupSteps, permitUrl, docs } Errors: none β this tool always succeeds.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Built upon annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds key behaviors: no authentication required, always succeeds, returns specific fields, and explains demo mode behavior. No contradictions.
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-structured with a clear purpose, context on demo mode, and a list of return fields. Every sentence adds value, and it is front-loaded with the main purpose.
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?
Without an output schema, the description includes the complete return object fields. It covers error behavior (none), authentication needs, and the demo mode vs. full mode scenario, making it fully self-contained for a setup guide.
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?
No parameters exist, so baseline is 4. The description does not add parameter info, but that's irrelevant here. Schema coverage is 100%.
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 starts with a clear verb+resource: 'Show the LemonCake MCP first-run setup guide.' It distinguishes itself from sibling tools by stating 'Call this tool FIRST,' making its unique purpose obvious.
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 advises when to use the tool: 'Call this tool FIRST to learn what is missing and how to obtain a permit.' It explains the context of demo mode and provides guidance on what to expect based on credential status.
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.
v0.5.9call_servicecheck_balancecheck_taxget_service_statslist_servicessetupv0.5.4call_servicecheck_balancecheck_taxget_service_statslist_servicessetupv0.5.2call_servicecheck_balancecheck_taxget_service_statslist_servicessetupv0.5.1call_servicecheck_balancecheck_taxget_service_statslist_servicessetupv0.1.0call_servicecheck_balancecheck_taxget_service_statslist_servicessetupEach tool targets a distinct concern: setup guidance, service discovery, usage statistics, balance checking, tax compliance, and the core pay-per-call execution. There is no functional overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (setup, list_services, get_service_stats, check_balance, check_tax, call_service), making the set predictable and easy to navigate.
With exactly 6 tools, the server is well-scoped for its purposeβcovering onboarding, discovery, financial checks, and the core paid API callβwithout being bloated or sparse.
The tool surface covers the full lifecycle: discover services, check stats and balance, verify tax, and execute calls. One minor gap is the lack of a transaction history tool, but core workflows are complete.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Agent bookkeeping, sanctions, KYB, VAT and e-invoice checks - pay per call in USDC
30 pay-per-call APIs for AI agents: compliance, trade, safety, web, data. USDC on Base via x402.
Payment infrastructure for AI agents: spending rules, approval flows, single-use virtual cards.
Wallet and payments for AI agents: auto-pay x402 APIs in USDC on XDC, within on-chain limits.
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