Finance Toolkit
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
作者提供的说明(英文原文)
Spectra Finance MCP server: yield discovery, PT/YT, looping, Morpho, 10 chains
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"spectra": {
"command": "npx",
"args": [
"-y",
"spectra-mcp-server"
]
}
}
}在终端中运行这条命令
claude mcp add spectra -- npx -y spectra-mcp-server粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"spectra": {
"command": "npx",
"args": [
"-y",
"spectra-mcp-server"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"spectra": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"spectra-mcp-server"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 Finanzgoblin 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.0.0)于9个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Finance Toolkit, SolidInvoice, Korea Tax Calculator
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
Create and send invoices and quotes, manage clients, and track payments in SolidInvoice.
Korean tax calculation and tax-audit judgment MCP: 96 tools with statutory citations.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Multi-protocol yield intelligence for AI agents — covering Spectra Finance, Pendle, and Morpho via the Model Context Protocol.
55 tools · 3 protocols · 10+ chains · read-only · open emergence architecture · calibration intelligence · on-chain Curve quoting · Pendle logit AMM modeling · ERC-4626 health checks · yield curve term structure · historical eth_getLogs · full Pendle protocol parity (15 tools) · cross-protocol position map · cross-protocol Spectra↔Pendle comparison · veSPECTRA + vePENDLE governance · Pendle historical time-series · Morpho supply-side visibility with supplier cascade modeling · Morpho user positions & historical rates · Merkl campaign APR + expiry integration · entry path awareness · prescriptive observation boundaries · curator risk monitoring · withdrawal stress testing · rollover planning · multi-vault portfolio aggregation · pool expiry monitoring · zero web3 library dependencies
Any AI agent (Claude, GPT, open-source) that supports MCP can query Spectra, Pendle, and Morpho in a unified interface to:
get_dy() for exact output, falling back to math estimateseth_getLogs when API data has aged out — with dynamic RPC URL support for any chainmv_get_protocol_context (PT/YT identity, Router batching, deposit paths, glossary, fees & costs, workflow routing, externalPositions registry)The agent doesn't need to understand PT/YT mechanics -- it just calls spectra_scan_opportunities or pendle_scan_opportunities with its capital size and gets ranked, actionable data. For cross-protocol comparison, mv_scan_curator_opportunities ranks both protocols together. If it needs to understand *why* something works that way, it calls mv_get_protocol_context.
The server is designed so that AI agents can discover novel strategies without being taught specific strategies. Instead of hard-coding strategy identification logic, the system teaches protocol mechanics at three layers — and lets the agent compose building blocks into its own analysis.
Layer 1: Protocol Context (mv_get_protocol_context tool + resources)
→ Teaches the "physics" of the protocol: PT/YT identity, Router batching, minting → Available as a callable tool (on-demand) and as MCP resources → Static knowledge — what CAN happen, not what IS happening
Layer 2: Tool Descriptions (every tool's description string) → Teaches domain-specific mechanics relevant to that tool's data → Cross-reference nudges: "use spectra_get_portfolio to check actual holdings" → Uses "could be" language, not "is" — preserves ambiguity where it exists → Calls out hidden mechanics that could mislead (e.g., AMM_ADD_LIQUIDITY can mint YT)
Layer 3: Structured Output Hints (computed at runtime in tool output) → Position Shape analysis in portfolio: balance ratios (e.g., "YT/PT 4:1") → Portfolio Signals: concentration, maturity alerts, strategy shape across positions → Volume Signals: volume/liquidity ratio, buy/sell skew, trend detection → Morpho Market Hints: capacity warnings, utilization alerts, spread analysis, reward incentives → Morpho Supply-Side Analysis: supplier identification (vault/EOA/looper), concentration metrics, supply gap warnings — surfaces where lending liquidity comes from and whether it's sufficient → Competing Interpretation Branches (A/B/C) in activity analysis per-address: multiple explanations for the same observable pattern, presented with equal weight. The agent must bring external evidence to collapse branches — the tension IS the info. → Statistical confidence boundaries: small-N cycle repetitions (≤5) flagged as insufficient for extrapolation, preventing false pattern-matching → Flow Accounting with competing hypotheses: YT-only, PT-only, fully exited positions each get multiple explanations that predict different future behavior → Address isolation mode: cycle detection, flow accounting, contract/EOA detection, pool impact warnings, gas estimates, pool context → Capital-aware warnings: short maturity, low liquidity, negative effective APY → Yield Dimensions in scan output: fixed, variable, LP, looping side-by-side → Strategy Tension: competing PT looping vs YT accumulation on same pool → On-chain quote source indicators: "(on-chain Curve get_dy)" vs "(estimated)" → Yield composition: IBT APR breakdown (organic base vs external incentives) in scans and quotes → Incentive sustainability: flags when >50% of IBT APR or LP APY comes from incentives, shows "organic only" APY so agents can assess yield durability without incentive assumptions → Pool reserves: IBT/PT amounts with ratio for AMM imbalance analysis → Merkl campaign APR: external incentive programs (e.g., KAT rewards) shown per-pool/market with double-counting avoidance (skips campaigns whose reward tokens are already displayed) → Merkl campaign eligibility: action type (POOL/HOLD) always shown, with explicit "[LP only — not for YT/PT holders]" flag on POOL campaigns to prevent misattribution → Protocol YT fees: Spectra 3% and Pendle 5% shown in every pool/market detail output so agents apply them to yield calculations without needing external lookups → Points multipliers: external programs (Drops, InfiniFi, Firelight) with amounts → "Could be" / "at current rates" language: preserves ambiguity in ranked output → Makes key signals SALIENT without prescribing interpretation
Layer 4: Observation Coverage (quantifies blind spots in tool output) → Value coverage: what % of an address's position is explained by observable activity. Low coverage means most behavior is invisible (direct mints, transfers, cross-chain). → Temporal coverage: active days vs dark periods with no observable events. Longest gap duration surfaced so the agent can reason about invisible activity. → Data source coverage: which of 5 available sources were consulted vs not. Explicitly lists what is invisible to the current analysis. → Activity diversity: single-type activity flagged as highest-ambiguity pattern. → Boundary marker: "Position sizing should assume this analysis is incomplete, not comprehensive." — a structural admission of domain-of-validity limits. → Coverage metrics bound ALL interpretations above them. High-confidence strategy assessment + low observation coverage = false confidence. The coverage section forces the agent to size its conviction to what it actually observed.
只显示了开头部分;其余内容请在仓库中阅读