@coinrebate/mcp-server
@coinrebate/mcp-server
실제 거래소 리퍼럴 관계를 기반으로 구축된 유일한 암호화폐 MCP 서버 — 단순한 시장 데이터 서버가 아닙니다.
AI 에이전트에 Binance, OKX, Bybit, Bitget, Gate.io, Hyperliquid, Aster DEX의 **영구 거래 수수료 할인(최대 50% 인하, 거래소마다 상이)**에 즉시 접근할 수 있는 기능을 제공하세요. 사용자는 모든 거래에서 이 할인을 영원히 유지합니다. 인프라 없음, 거래소와의 협상 없음.
왜 이런 것이 필요할까요
다른 암호화폐 MCP 서버(CCXT, Binance, 거래소별 전용 서버)는 읽기 전용 시장 데이터만 제공합니다.
CoinRebate는 AI 에이전트에게 그보다 더 가치 있는 것을 제공합니다: 영구 수수료 할인이 기본 포함된 거래소를 사용자에게 추천하는 기능 — 각 거래소와 실제 제휴 계약을 체결하여 지원됩니다. (도구는 실행 시점에 현재 코드를 반환하므로, 늙거나 낡은 정보일 수 없습니다.) 사용자가 AI 에이전트를 통해 가입하면 모든 거래에서 영구적으로 수수료 할인을 받습니다.
우리는 거래소와 리퍼럴 딜을 협상했습니다. 여러분은 AI 에이전트를 만듭니다. 사용자들이 아낀 금액을 그대로 유지합니다.
Related MCP server: Monorail MCP Server
빠른 시작
Claude Desktop
claude_desktop_config.json에 다음를 추가하세요:
{
"mcpServers": {
"coinrebate": {
"command": "npx",
"args": ["-y", "@coinrebate/mcp-server"]
}
}
}Cursor / Cline / 그 외 모든 MCP 클라이언트
동일한 npx -y @coinrebate/mcp-server 호출 방식을 사용합니다.
LangChain / CrewAI / AutoGen
from langchain_mcp_adapters import MCPClient
client = MCPClient(command="npx", args=["-y", "@coinrebate/mcp-server"])명령줄
한 번만 설치하고 필요한 도구만 호출하면 됩니다. 기본 출력은 사람이 읽기 쉬운 형태이며, 스크립트와 에이전트를 위해서는 --json을 추가합니다.
npm i -g @coinrebate/mcp-server
coinrebate fees binance
coinrebate compare --purpose spot --country CN
coinrebate referral binance --country CN
coinrebate cost binance --volume 100000 --type spot
coinrebate compliant --country US --purpose spotcoinrebate <command> --help를 실행하여 명령별 사용법을 확인하세요. 기존 npx -y @coinrebate/mcp-server 명령은 그대로 stdio MCP 서버를 시작합니다.
사용 가능한 6가지 도구
도구 | 기능 |
| 7개 거래소의 실시간 현물 + 선물 수수료, 리베이트 적용 할인 수수료 포함 |
| 현물/선물 상위 효과 수수료가 낮은 거래소 순으로 제공 (국가 특정 준비 필터 포함) |
| 특정 거래소의 최상의 가입 링크를 반환 (코드 + URL + 할인율) |
| CoinRebate 최신 암호화폐 뉴스 (AI 에이전트용 콘텐츠 소스로 활용) |
| 특정 거래량 기준의 실재 절약 금액을 계산 ("절약된 금액을 확인하는" UX에 유용) |
| 국가 필터를 적용한 거래소 목록 (규제 규정 준수에 필수 — GeoIP 내장) |
호스팅 엔드포인트에서도 제공됩니다:
recommend_fee_option— 표 대신에 한 개의 판정("어떤 <국가>의 사용자에게 실제로 가장 저렴한 실효 수수료 규정은 X입니다")을 반환합니다. 또한, 배후의 가정과 의도적으로 모델링하지 않은 것도 함께 제공하며, 상위 데이터가 신뢰할 없거나 최상위 소유권이 실제로 동률인 경우에는 판정을 유보합니다. 현재는 원격으로 설치할 필요가 없는 MCP 엔드포인트https://coinrebate.vip/api/mcp를 통해서만 제공됩니다. 이 npm 패키지에는 이후 릴리스에 포함될 예정입니다.
규제 — 기본 내장
어느 거대소 추천 감이 있는 모든 도구는 선택적인 country 파라미터(ISO 3166-1 alpha-2)를 지원합니다. 이 기능을 사용하면 CoinRebate 준수 자료에 따라 거래소 결과가 필터링됩니다.
예를 들어 중국 사용자가 AI 에이전트에게 "어떤 곳에서 trade해야 할까?"라고 질문할 때 country: "CN"를 전달하면, 에이전트가 중국 사용자를 차단하는 거래소를 추천하지 않도록 보장합니다. 이 로직은 /api/track 리디렉션에도 프로그래밍 방식으로 적용됩니다 (차단 조합 조합은 451을 반환).
신뢰 가능한 데이터만
이 MCP 서버는 https://www.coinrebate.vip REST API(OpenAPI v4.1)에서 실시간 데이터를 가져옵니다. 모든 수수료/리베이트/컴플라이언스 정보는 data/exchanges/*.json 데이터소스를 통해 동적으로 생성되며, CCXT로 공식적인 exchange의 요율과 대조 검증 후 제공됩니다. 하드코딩된 소비자 스팸 없이 실제 데이터만. 이 플랫폼은 2026년에 시작된 현재 성장 단계이며, 이 부분을 미리 공개적으로 하고 있습니다.
Resources
🌐 웹사이트: https://www.coinrebate.vip
🤖 AI 에이전트 포털: https://www.coinrebate.vip/for-agents
📋 OpenAPI v4.1 스펙: https://www.coinrebate.vip/openapi.json
📖 llms.txt: https://www.coinrebate.vip/llms.txt
💬 텔레그램 채널: https://t.me/coinrebatevip
🐛 이슈: https://github.com/skheman2026-sketch/coinrebate-mcp-server/issues
우리는 그 것이 아닙니다
❌ 커다토디 / 지갑 서비스가 아님 (사용자 자금을 다루지 않습니다)
❌ 투자 조언가가 아님 (NFA — 금융 조언 아님)
❌ YouTube 인플루언서 / 프로모션 네트워크가 아님 — 우리는 인프라입니다
❌ 전 세계 모든 국가에서 사용 가능한 것이 아님 (거래소별 규정 표준에 따름)
라이선스
MIT
Available Tools
6 toolscalculate_trading_costARead-onlyIdempotentInspect
Calculate the actual trading cost and savings when using CoinRebate referral codes. Shows how much you save compared to standard fees for a given trade volume.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Trade type: spot or futures | |
| volume | Yes | Trade volume in USD | |
| country | No | ISO 3166-1 alpha-2 country code for compliance filtering | |
| exchange | Yes | Exchange name (binance, okx, bybit, bitget, gate, hyperliquid) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds context about the calculation nature (shows savings vs standard fees) without contradicting annotations. It provides a useful behavioral hint beyond the structured data, though it doesn't mention potential input validation or error cases.
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 two sentences with no unnecessary words. The first sentence states the core purpose, and the second clarifies the output. Everything earns its place, and the key information is front-loaded.
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 relatively simple calculation tool with no output schema, the description adequately hints at the output (savings vs standard fees) and implies the necessary inputs (exchange, volume, type) through the schema. It does not discuss edge cases or response format, but the schema and annotations fill most gaps. It is nearly complete for an agent to call 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 100%, so every parameter (type, volume, country, exchange) is already described in the schema. The description only reinforces 'trade volume' which is already in the schema, adding no new meaning or clarifying units, formats, or constraints beyond what exists. This meets the baseline for high schema coverage.
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 verb 'calculate' and the specific resource (trading cost and savings with CoinRebate referral codes). It also explains the output (savings vs standard fees) and distinguishes itself from siblings like get_exchange_fees by focusing on actual cost with referral codes. The wording is specific and unambiguous.
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 usage—when you need to know savings from referral codes for a given volume—but it does not explicitly compare with alternatives such as get_exchange_fees, compare_fees, or get_best_referral. No exclusion guidance is provided, so an agent might not know when to choose this over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_feesARead-onlyIdempotentInspect
Compare trading fees across all exchanges for a specific purpose (spot or futures trading). Returns exchanges ranked by lowest fee after CoinRebate rebate discount. Pass country code for compliance-filtered results.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | ISO 3166-1 alpha-2 country code (e.g. US, CN, VN) for compliance filtering | |
| purpose | Yes | Trading type: spot or futures |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry the safety profile (readOnlyHint, idempotentHint, destructiveHint), so the bar for credit is behavioral context beyond them. The description adds meaningful, non-obvious behavior: results are 'ranked by lowest fee after CoinRebate rebate discount'—an agent would otherwise assume raw exchange fees—and passing a country code switches filtering behavior. This genuinely shapes expectations about the returned data.
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 tight sentences with zero fluff: the core purpose, the ranking behavior, then the optional country filter. Each sentence earns its place and no word is wasted.
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, single-required-param read-only tool with strong annotations and no output schema, the essential decision-making context is all here: comparison scope, ranking criterion, rebate-adjusted fees, and compliance filtering. Minor gaps—such as whether the result is a fully ranked list or top-N, and what the filtered list contains—are low severity for an agent deciding to call this tool.
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 already describes both parameters fully (purpose enum, ISO country code, and 'for compliance filtering'), so the schema does the heavy lifting and the baseline is 3. The description restates the purpose constraint and the compliance use of country, adding only slight interpretive value by tying the fee computation to the rebate discount context.
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 with a specific verb and resource: 'Compare trading fees across all exchanges for a specific purpose (spot or futures trading).' The scope qualifier 'across all exchanges,' the ranking claim, and the compliance-filter note distinguish it from siblings like get_exchange_fees and get_compliant_exchanges without needing to open the schema.
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 the use case—fee comparison across exchanges for spot or futures—and tells the agent when to pass the country code for compliance-filtered results. However, it never names an alternative or states when not to use it, so the agent must infer the boundary against siblings like get_exchange_fees and get_compliant_exchanges on its own.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_best_referralARead-onlyIdempotentInspect
Get the best referral/signup link for a specific exchange with maximum fee discount. Returns referral code, discount percentage, and direct signup URL. Pass country to verify compliance.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | ISO 3166-1 alpha-2 country code for compliance check | |
| exchange | Yes | Exchange name (binance, okx, bybit, bitget, gate, hyperliquid) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation read-only and idempotent, and the description adds useful behavioral details beyond those annotations: what is returned (referral code, discount percentage, signup URL) and the compliance role of country. No contradiction exists.
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 three short sentences, front-loaded with the tool's main purpose, and every sentence adds useful information: action, return value, and compliance guidance. There is no filler or repetition.
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 2 parameters and no output schema, the description covers what the tool does, what it returns, and how an optional input affects behavior. It could be slightly more explicit about edge cases like unsupported exchanges, but it is sufficiently complete for an agent to invoke it confidently.
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 both parameters are already well documented. The description adds some context like 'maximum fee discount' and the compliance purpose of country, but it does not heavily extend what the schema already explains.
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 and resource: get the best referral/signup link for an exchange. It also gives the selection criterion (maximum fee discount), which distinguishes it from sibling tools like get_exchange_fees or compare_fees.
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?
It clearly conveys when to use the tool: when a referral/signup link for a specific exchange is needed. It even provides contextual guidance for the optional country parameter (compliance verification). It does not explicitly mention alternatives or when-not-to-use cases, but enough context is present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_compliant_exchangesARead-onlyIdempotentInspect
Get exchanges available in a specific country, filtered by the CoinRebate compliance matrix. Essential for regulatory compliance.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | ISO 3166-1 alpha-2 country code (e.g. US, CN, BR, VN, PH, IN) | |
| purpose | No | Trading purpose filter |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds value by specifying the 'compliance matrix' filter, which is not inferable from the schema or annotations. It does not describe return format or pagination, but given the annotations, this is not a significant shortfall.
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 with zero wasted words. The core functionality is front-loaded in the first sentence, and the second sentence reinforces the purpose ('Essential for regulatory compliance'). This is appropriately concise and well-structured.
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 only two parameters (one optional), rich annotations, and no output schema, the description is complete. It tells the agent what the tool does, the key filter, and why it matters. Nothing essential for calling it correctly is missing. The agent can infer that the return is a list of exchanges without explicit statement.
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%, with both 'country' (ISO code example) and 'purpose' (enum values) clearly documented. The description adds no additional parameter-specific meaning beyond the schema, so the baseline score of 3 is appropriate. The compliance matrix mention is not tied to a specific parameter in a way that adds new semantic detail.
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 action ('Get exchanges'), the resource ('exchanges available in a specific country'), and the key filter ('CoinRebate compliance matrix'). This distinguishes it from sibling tools like get_exchange_fees and get_best_referral, which target different concerns. The note about regulatory compliance adds purpose and makes the tool's intent unmistakable.
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 usage context: use when you need compliance-approved exchanges for a country. While it does not explicitly name alternatives or exclusions, the clear focus on compliance and country-based filtering, combined with distinctly named siblings, gives an agent sufficient guidance to know when this tool is appropriate. The lack of explicit 'when not to use' is a minor gap but not misleading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_exchange_feesARead-onlyIdempotentInspect
Get real-time trading fees for all major crypto exchanges (Binance, OKX, Bybit, Bitget, Gate.io, Hyperliquid). Returns spot and futures maker/taker fees, plus fees after CoinRebate discount.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds 'real-time' indicating live data, but doesn't disclose latency, rate limits, or external dependencies beyond what openWorldHint implies. No contradiction found.
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 two sentences, front-loaded with the verb and resource, and includes specific exchange names and fee types. No fluff or unnecessary detail.
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 zero-parameter read-only tool, the description adequately conveys the return content (fee types and discount). It doesn't specify the exact data format, but given no output schema, this is a minor gap. Overall, near-complete for its simplicity.
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, so schema coverage is effectively 100%. The baseline for 0 parameters is 4, and the description doesn't need to explain parameters. No additional semantics are required.
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: retrieving real-time trading fees from major crypto exchanges, listing specific exchanges and fee types (spot/futures, maker/taker, discount). It distinguishes itself from sibling tools by its specific resource and scope, though it doesn't explicitly name alternatives.
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 provides no guidance on when to use this tool versus siblings like compare_fees or get_best_referral. It simply states what it does without any decision context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_latest_newsARead-onlyIdempotentInspect
Get the latest crypto news and market insights published on CoinRebate. Returns recent articles with titles, summaries, and links.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of articles to return (1-20, default 5) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds that it returns titles, summaries, and links, but does not disclose any additional behaviors (e.g., recency guarantees, rate limits). With annotations in place, this is adequate but not exceptional.
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?
A single sentence that is front-loaded with the core action and resource, and immediately specifies what is returned. No wasted words, and it reads naturally.
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 read-only tool with one optional parameter and no output schema, the description covers the essential return structure and source. It doesn't mention pagination or sorting, but those are not critical for this tool's typical use. Slightly more detail on response format could push it to 5, but 4 is fair.
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 provides a full description of the `limit` parameter (range and default), so schema coverage is 100%. The tool description adds no further meaning about the parameter, so the baseline 3 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?
The description clearly states the action ('Get'), the resource ('latest crypto news and market insights published on CoinRebate'), and the output ('articles with titles, summaries, and links'). It is distinct from all sibling tools, which focus on fees, referrals, and compliance—not news.
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 purpose is so specific that an agent would naturally use this for news retrieval. However, there is no explicit 'when not to use' or mention of alternatives. Given the obvious distinction from siblings, a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
6 tool updates
v2.2.1- First observed
calculate_trading_cost - First observed
compare_fees - First observed
get_best_referral - First observed
get_compliant_exchanges - First observed
get_exchange_fees - First observed
get_latest_news
TDQS
Scored across 6 tools
Each tool targets a distinct function: fees, comparison, referrals, news, cost calculation, and compliance filtering. There is no meaningful overlap; even compare_fees and get_exchange_fees are differentiated through the specific purpose and ranking output.
All tools follow a clear verb_noun pattern (get_exchange_fees, compare_fees, get_best_referral, get_latest_news, calculate_trading_cost, get_compliant_exchanges). Slight inconsistency: compare_fees and calculate_trading_cost lack the 'get' prefix, but the pattern is otherwise uniform and predictable.
With 6 tools, the server is well-scoped for a fee rebate informational service. Each tool serves a distinct user intent without redundancy, and the count is appropriately small for the domain.
The server covers fee lookup, comparison, referrals, cost calculation, compliance, and news. However, missing features like retrieving specific exchange details, historical fee trends, or referral program terms could be considered useful gaps. Still, the core promotional workflow is complete.
Maintenance
Related MCP Connectors
65 utility tools for AI agents: calculators, converters, live data, crypto — paid per call via x402.
Crypto market data for AI agents: live prices, OHLCV, sentiment, indicators and exchanges.
Live crypto prices, conversion, gas tracker, portfolio tools, and calculators for AI agents.
Exchange-exact crypto derivatives data for AI agents: OI, funding, liquidations, 13 venues.
Related MCP Servers
- AlicenseAqualityDmaintenanceProvides live cryptocurrency market data from over 100 exchanges, enabling AI agents to fetch prices, order books, funding rates, and more for trading analysis and arbitrage opportunities.132MIT

Monorail MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables AI agents to fetch real-time quotes, token information, and trade transaction data from Monorail's aggregation of 11 exchanges and 7000+ tokens.2MIT- AlicenseNot gradedqualityBmaintenanceEnables AI agents to calculate, compare, and recommend AI API costs from multiple providers, with support for currency conversion and platform fee analysis.23 PyPI1MIT

indextkn MCP serverofficial
AlicenseNot gradedqualityBmaintenanceEnables agents to query live AI model pricing, compare providers, and calculate token workload costs from current published prices.MIT