Finance Toolkit
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
Descripción del creador (original en inglés)
Spectra Finance MCP server: yield discovery, PT/YT, looping, Morpho, 10 chains
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"spectra": {
"command": "npx",
"args": [
"-y",
"spectra-mcp-server"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add spectra -- npx -y spectra-mcp-serverPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"spectra": {
"command": "npx",
"args": [
"-y",
"spectra-mcp-server"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"spectra": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"spectra-mcp-server"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Finanzgoblin, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (1.0.0) llegó al registro hace 9 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Finance Toolkit, SolidInvoice, Korea Tax Calculator
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.
Solo se muestra el principio; lee el resto en el repo