Universal MCP Bridge & Proxy
This server exposes MCP tools for pet lookup, text analysis, system health, SQLite inspection/querying, and curl execution.
Find pets by status.
Get a pet by ID.
Analyze text with SHA256 checksum and word stats.
Check system health (disk, memory, uptime).
Inspect the schema of
sample.db.Run read-only SQL queries against
sample.db.Execute
curlcommands through a CLI wrapper.
Exposes an exec_curl tool that executes curl commands in a sandboxed environment (no shell=True, shlex tokenization, command whitelist and dangerous-expression blacklist), letting agents reach HTTP endpoints through the bridge.
Provides a SQLiteAdapter that connects to any .db/.sqlite file and auto-generates MCP tools for exploring the database schema (db_schema) and running queries (db_query) in a safe read-only mode that blocks mutative statements like INSERT, UPDATE, DELETE and DROP.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Universal MCP Bridge & Proxy run a read-only query on my SQLite database at ./app.db: SELECT * FROM users LIMIT 5"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Universal MCP Bridge & Proxy 🌉⚡
The Enterprise-Grade Gateway, Aggregator & Universal Proxy for the Model Context Protocol (MCP). Transform OpenAPI, GraphQL, Documents, Python/Bash scripts, CLIs, Databases (SQLite, PostgreSQL, Redis), and Upstream MCP Servers into secure, federated MCP endpoints on the fly with Multi-Tenant RBAC, Workflow Pipeline Chaining, Cross-Platform Schema Export, Prometheus metrics, and Zero-Config Hot-Reload.
L'adozione del Model Context Protocol (MCP) di Anthropic è lo standard de facto per connettere LLM e agenti intelligenti a tool esterni. Universal MCP Bridge posiziona la tua infrastruttura al centro di questo ecosistema, consentendo di governare, orchestrare ed esporre qualsiasi tool con standard enterprise di sicurezza, osservabilità e flessibilità.
⚡ Installazione 1-Line (Quickstart)
Installa Universal MCP Bridge globalmente su qualsiasi sistema Linux o macOS in pochi secondi:
curl -fsSL https://raw.githubusercontent.com/francaroscillidev-gif/universal-mcp-bridge/main/install.sh | bashOppure con uv:
git clone https://github.com/francaroscillidev-gif/universal-mcp-bridge.git
cd universal-mcp-bridge
uv syncRelated MCP server: yamcp
🚀 Architettura & Funzionalità Top Tier
Adattatori Multi-Sorgente Universali:
OpenAPI / REST: Mappatura automatica di endpoint, path/query params e body JSON.
GraphQL: Introspezione dello schema (
__schema), tipizzazione e argomenti.Documenti & Knowledge Base (
DocumentSearchAdapter): Ricerca fuzzy su cartelle di file (.md,.txt,.csv,.json, ecc.) e lettura a chunk paginati.Federazione MCP-to-MCP (
MCPProxyAdapter): Proxy trasparente per altri server MCP esistenti (es. GitHub MCP, Slack MCP) con wrapping dei nostri controlli di sicurezza.Script (Python & Bash): Ispezione docstring/commenti, argomenti CLI e standard input.
CLI Tools: Ispezione automatica dei sotto-comandi via
--help(git,curl,jq, ecc.).Database Relazionali & In-Memory:
SQLite: Schema inspector e query SQL con blocco read-only.
PostgreSQL: Connection pooling async (
asyncpg) e ispezione DDL.Redis: Navigazione chiavi e recupero tipi complessi (string, hash, list, set).
🔐 Multi-Tenant Role-Based Access Control (RBAC):
Autenticazione e autorizzazione tramite token JWT firmati crittograficamente.
Ruoli granulari (
admin,analyst,agent) con whitelist e blacklist dinamiche su pattern di tool.Generazione chiavi con comando dedicato:
universal-mcp generate-token.
⛓️ Multi-Step Workflow Pipeline Chaining (
WorkflowChainer):Concatena tool MCP eterogenei in una singola pipeline logica (es. estrai da DB -> analizza con script Python -> invia via API REST).
Risoluzione automatica delle variabili con sintassi
{{step_1.output}}ed esecuzione non bloccante.
🌐 Cross-Platform Schema Exporter (
SchemaExporter):Esporta all'istante l'intero catalogo dei tool montati in formato:
OpenAI Function Calling / Assistants API (
--format openai)Anthropic Claude Tool Use (
--format anthropic)OpenAPI 3.1 Specification (
--format openapi)
🧠 LLM Prompt Optimizer (
PromptOptimizer):Arricchisce semanticamente le descrizioni dei tool seguendo le linee guida di Anthropic e OpenAI, riducendo le allucinazioni e migliorando l'accuratezza del function calling.
🧪 Interactive CLI Testing (
test-tool):Esegui e testa qualsiasi tool montato direttamente da riga di comando per debugging rapido senza avviare LLM.
⏳ Background Async Job Queue (
AsyncJobQueue):Esecuzione asincrona per task e script pesanti con tracciamento stato e tool
job_status(job_id).
⚡ Runtime Hot-Mounting & Webhooks Gateway:
Aggiunta di nuovi tool a caldo via REST o Dashboard Web senza riavvio del processo.
Ricezione webhook esterni (
/api/webhooks/{source}) per risvegliare l'agente via prompt notifications.
🛡️ Sicurezza & Resilienza Enterprise:
Circuit Breaker Pattern: Isolamento automatico di dipendenze a monte instabili.
Security Sandbox: Esecuzione asincrona senza shell=True, tokenizzazione
shlex, blacklist anti-injection e timeout.Secret & PII Redactor: Mascheramento automatico di token Bearer, API key e password.
Human-in-the-Loop (HITL): Approvazione interattiva su terminale per comandi critici.
Rate Limiting & Response Cache (TTL): Protezione da loop agentici e costi API.
Osservabilità & Web Dashboard:
Live Web Dashboard: UI moderna su porta 8000 con monitoraggio in tempo reale.
Prometheus
/metrics: Integrazione standard Prometheus/Grafana per latenze e volumi.Healthcheck
/healthz: Pronto per Kubernetes liveness/readiness probes.
🛠️ Comandi CLI Completi
1. Test Rapido di un Tool da Terminale
uv run universal-mcp test-tool --config examples/universal-mcp.yaml --name run_text_analyzer --args '{"args": "Hello World"}'2. Avviare la Live Web Dashboard & Gateway
uv run universal-mcp dashboard --config examples/universal-mcp.yaml --port 8000Web UI:
http://localhost:8000Metriche Prometheus:
http://localhost:8000/metricsHealthcheck:
http://localhost:8000/healthzWebhook Ingestion:
POST http://localhost:8000/api/webhooks/{source}
3. Esportazione Schema per altri Framework (OpenAI / Claude / OpenAPI)
# Esporta per OpenAI Assistants o GPT-4o
uv run universal-mcp export-schema --config examples/universal-mcp.yaml --format openai
# Esporta per Claude Tool Use
uv run universal-mcp export-schema --config examples/universal-mcp.yaml --format anthropic
# Esporta come specifica OpenAPI 3.1
uv run universal-mcp export-schema --config examples/universal-mcp.yaml --format openapi --output spec.json4. Generazione Token JWT Multi-Tenant (RBAC)
uv run universal-mcp generate-token --subject my-agent-1 --role analyst5. Avvio del Server MCP (Standard I/O o SSE)
# Modalità standard I/O (Claude Desktop, Cursor, Antigravity) con Prompt Optimization
uv run universal-mcp serve --config examples/universal-mcp.yaml --optimize --transport stdio
# Modalità con Human-in-the-Loop attivo
uv run universal-mcp serve --config examples/universal-mcp.yaml --hitl
# Modalità SSE per microservizi di rete
uv run universal-mcp serve --config examples/universal-mcp.yaml --transport sse --port 80806. Esportare la configurazione 1-Click per Claude Desktop / Cursor
uv run universal-mcp export-client-config --config examples/universal-mcp.yaml📖 Guide all'Integrazione
🐳 Esecuzione con Docker
docker compose up --build🧪 Esecuzione Suite di Test
uv run pytest📬 Contact, Support & Commercial Inquiries
If you have questions, feedback, enterprise integration requests, or custom agent governance needs, feel free to reach out directly:
Lead Developer & Maintainer: Franca Roscilli
Email: francaroscillidev@gmail.com
GitHub: @francaroscillidev-gif
Bug Reports & Feature Requests: Open an issue on GitHub
📜 Licenza
Distribuito sotto licenza Apache 2.0. Consulta il file LICENSE per maggiori dettagli.
Available Tools
7 toolsdb_sample_queryC
Run an SQL query on database 'sample.db' (READ-ONLY)
Inputs: kwargs (required).
db_sample_schemaC
Inspect tables and column schemas in database 'sample.db'
Inputs: kwargs (required).
exec_curlC
Executes 'curl'. Help summary: Usage: curl [options...] -d, --data HTTP POST data -f, --fail Fail fast with no output on HTTP errors -h, --help Get help for commands -o, --output Write to file instead of stdout -O, --remote-name Write output to...
Inputs: kwargs (required).
findPetsByStatusD
Finds Pets by status
Inputs: kwargs (required).
getPetByIdC
Find pet by ID
Inputs: kwargs (required).
run_sys_healthC
Quick system health check reporter (disk, memory, uptime)
Inputs: kwargs (required).
run_text_analyzerC
Calculate SHA256 checksum and word stats of a given text.
Inputs: kwargs (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 7 tools
Each tool targets a clearly different operation: pet lookup by status vs. by ID, text analysis, system health, DB schema inspection, DB query, and curl execution. The only mild overlap is findPetsByStatus vs getPetById, but list-vs-get semantics distinguish them. The bigger problem is thematic incoherence, not tool-to-tool confusion.
The set mixes camelCase (findPetsByStatus, getPetById) with snake_case (run_text_analyzer, db_sample_schema, exec_curl) and uses inconsistent verb styles (find/get/run/exec). Names are individually readable but the conventions change from tool to tool.
Seven tools is a reasonable count in isolation and none feels redundant. However, they are an unrelated grab-bag rather than a scoped surface, so the count is fine but the composition is arbitrary.
There is no coherent domain to cover: pet tools lack create/update/delete, the DB exposes only read-only sample/query with no schema mutation, and text/sys/curl are one-off utilities. As a 'universal bridge' the surface is a random sampling with major lifecycle gaps.
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