Web3 Signals

io.github.manavagaPublished by GitHub account manavagav0.4.1Updated Oct 25, 2026

AI crypto buy/sell signals for 20 assets: 6 data dimensions fused into 0-100 scores. x402 + MCP.

The publisher's own server: SIGNALS Loyalty Agent Platform

VerifiedSSEWeb executableFinanceAI & ML

Overview

AI-generated overview

A remote MCP endpoint serving scored crypto buy/sell signals for 20 assets, fusing five data agents into 0-100 composite signals with rationales.

What you can do
Query composite crypto signals for 20 assets, with directional labels, momentum tracking and LLM-generated rationales built from five scored data dimensions (whale flows, technicals, derivatives, narrative, market microstructure). Ask for top buy candidates or a specific asset's current signal. Review self-graded prediction accuracy at 24h and 48h horizons through the reputation/performance data. Paid signal and performance endpoints are reached through x402 micropayments rather than API keys.
When to use it
Useful when an assistant needs quick, pre-computed crypto market signals or a ranked shortlist of assets, and when you want a hosted endpoint with no signup or key management. Not a substitute for your own research or execution tooling.
Requirements
A remote SSE endpoint; no local install, API key or account is declared. Signal and performance endpoints require an x402 payment header settling USDC on Base mainnet per call; other endpoints are free. Network access to the hosted service is needed.
Before you install
Paid calls spend real money: each signal or performance request settles a small USDC micropayment on Base mainnet, so configure spending limits. Signals are model-generated predictions, not financial advice, and accuracy is self-graded. Queries and payment details go to a third-party hosted service. Running the project locally instead requires its own credentials such as REDDIT_CLIENT_ID and ANTHROPIC_API_KEY.

Installation

In SourceWeft

  1. Open Web3 Signals in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via SSE. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.manavaga%2Fweb3-signals/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "web3-signals": {
      "type": "sse",
      "url": "https://web3-signals-api-production.up.railway.app/mcp/sse"
    }
  }
}

README

Web3 Signals MCP

[smithery badge]

Multi-agent crypto signal intelligence. 20 assets, 5 data dimensions, scored 0–100, refreshed every 15 min.

Live API — https://web3-signals-api-production.up.railway.app Dashboard — https://web3-signals-api-production.up.railway.app/dashboard MCP endpoint — https://web3-signals-api-production.up.railway.app/mcp/stream (Smithery listing)

What it does

Five independent data agents (whale flows, technicals, derivatives, narrative, market microstructure) each score every asset 0–100. A fusion engine combines them into a single composite signal with a directional label, momentum tracking, and an LLM-generated rationale. The system grades its own predictions at 24h and 48h horizons against actual price moves — no self-reported accuracy.

Why it's interesting

  • Per-asset weight learning via IC analysis. Each asset gets its own dimension weights, fitted from Spearman/Pearson/Kendall correlations between past dimension scores and forward returns. Different assets respond to different signals.
  • Walk-forward backtesting with FDR correction. Benjamini–Hochberg adjustment on indicator significance to avoid false discoveries when testing dozens of features.
  • Platt-scaled probability calibration. Raw scores → calibrated probabilities so "75" means a real 75% directional likelihood, not just a higher number than 70.
  • x402 HTTP micropayments. Paid endpoints settle $0.001 USDC on Base mainnet per call via Coinbase's CDP facilitator. Payment IS authentication — no API keys, no signup, no OAuth.
  • MCP-native. Exposes itself to Claude Desktop, Cursor, and any MCP-compatible client over SSE. AI agents can query it with natural language.
  • Adaptive regime gating. Abstain zone widens/narrows with the Fear & Greed index; bullish-bias contrarian boost is dampened in confirmed BTC downtrends.

Quick start

Hit the API directly

bash
curl https://web3-signals-api-production.up.railway.app/signal/BTC

(/signal* and /performance/reputation require an x402 payment header; everything else is free.)

Use over MCP (Claude Desktop / Cursor / Windsurf)

json
{  "mcpServers": {    "web3-signals": {      "url": "https://web3-signals-api-production.up.railway.app/mcp/sse"    }  }}

Then prompt: "What's the BTC signal right now?" or "Show me top 3 buys."

Run locally

bash
git clone https://github.com/manavaga/web3-signals-mcp.gitcd web3-signals-mcpcp .env.example .env             # fill in REDDIT_CLIENT_ID, ANTHROPIC_API_KEY, etc.pip install -r requirements.txtpython -m api                    # API on :8000python -m orchestrator.runner --once   # one fusion cycle

Project layout

api/                FastAPI server, dashboard, x402 middlewaremcp_server/         MCP tool definitions (stdio + SSE)signal_fusion/      Weighted fusion, Platt calibration, meta-learnerwhale_agent/        On-chain flow tracking (Etherscan + exchange wallets)technical_agent/    RSI, MACD, MA, Bollinger (Binance)derivatives_agent/  Funding rate, OI, long/short rationarrative_agent/    Reddit, news, CoinGecko trending, LLM sentimentmarket_agent/       Price, volume, Fear & Greedshared/             Storage (Postgres / SQLite), base agent, profile loaderorchestrator/       15-minute agent scheduler + accuracy evaluatortools/              Backtesting, IC fitting, walk-forward, weight optimizer

Stack

Python 3.13 · FastAPI · PostgreSQL · pandas / numpy / scikit-learn · Anthropic Claude (LLM rationales) · Coinbase CDP x402 facilitator · Railway (deploy)

Performance evaluation

Snapshots are saved on every fusion cycle. At 24h and 48h each directional call is graded against the actual price move (CoinGecko + Binance). Neutral signals are skipped (only directional calls count). Accuracy is AVG(gradient_score) × 100 where gradient ∈ [0, 1] depending on whether the move was in the predicted direction and how large it was. See /performance/reputation for the live numbers.

Development notes

This codebase was built in pair-programming with Anthropic's Claude. Most commits have a Co-Authored-By: Claude trailer — kept intentionally to document the workflow. Architectural decisions, model choices (IC-based weighting, FDR correction, Platt scaling), and the production-readiness criteria (no-deploy-without-backtest hard rule, walk-forward embargoing) were human-driven; Claude was used for implementation, refactoring, and code review.

License

MIT

Source: README.md at commit 32fbf81

Tools

0
SourceWeft has not read this server's tools yet.

Version history

1
  1. v0.4.1LatestSep 16, 2026

Score

85% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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