What can you do with this server?

ProDisco is a progressive-disclosure MCP server framework that indexes TypeScript library APIs for discovery and executes code in a sandboxed environment, returning console output to agents. Core Capabilities: * Dynamic API Discovery - Search and browse API documentation (methods, types, functions, cached scripts) extracted from TypeScript libraries via .d.ts files or ESM JavaScript fallback * Sandboxed Code Execution - Execute TypeScript code in an isolated environment with restricted access to only pre-configured npm packages and environment variables * Multiple Execution Modes - Supports blocking, real-time streaming, async (background), and cached script execution, with status checking and cancellation * Flexible Library Configuration - Configure which npm packages to index and allow via YAML/JSON files, with optional auto-installation into .cache/deps * Versatile Deployment - Supports stdio (default) and HTTP transport with session management, SSE streaming, and containerized sandbox deployment (e.g., Kubernetes) using TCP/gRPC communication * Security Options - TLS and mutual TLS (mTLS) support for production deployments * Advanced Analytics - Statistical analysis, machine learning, linear algebra, and signal processing via libraries like simple-statistics, ml-regression, mathjs, and fft-js * Zero Maintenance - Automatically extracts methods from library type definitions, staying current with dependency upgrades Key Tools: * prodisco.searchTools - Search extracted API documentation by method name, type, library, or category * prodisco.runSandbox - Execute TypeScript code with captured console output Example Use Cases: * Query Kubernetes clusters via @kubernetes/client-node * Analyze Prometheus metrics with PromQL execution * Query Loki logs using LogQL * Detect anomalies, memory leaks, and capacity issues * Perform trend forecasting and correlation analysis on operational data

Which integrations are available for this server?

Provides tools for managing and inspecting Kubernetes clusters, including listing nodes, pods, and other resources across namespaces, viewing logs, and executing operations through TypeScript modules that agents discover and use progressively.

How do I use ProDisco?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ProDisco list all pods in the default namespace with their status" 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.

ProDisco (Progressive Disclosure MCP Server)

ProDisco is a progressive-disclosure MCP server framework: you provide a list of TypeScript libraries, ProDisco indexes their APIs for discovery, and a sandbox executes code that uses only those libraries. It follows Anthropic's Progressive Disclosure pattern: the MCP server exposes search tools which surface library APIs, agents discover them to write code, execute it in a sandbox, and only the final console output returns to the agent.

Kubernetes/observability is just one example configuration (see examples/). You can equally build an MCP server around AWS/GCP SDKs, Postgres clients, internal TypeScript SDKs, etc.

Note: ProDisco prefers indexing APIs from TypeScript declaration files (.d.ts). If a library ships no .d.ts, ProDisco can fall back to indexing ESM JavaScript exports (best-effort; types default to any). CommonJS-only JavaScript packages without typings are not supported.

Demo use-cases (optional):

Kubernetes access via @kubernetes/client-node
Prometheus metrics via @prodisco/prometheus-client
Loki logs via @prodisco/loki-client
Analytics via simple-statistics

Watch the demo

Why Progressive Disclosure?

Anthropic's latest guidance explains why MCP servers should progressively reveal capabilities instead of dumping every tool definition into the model context. When agents explore a filesystem of TypeScript modules, they only load what they need and process data inside the execution environment, then return a concise result to the chat. This keeps token usage low, improves latency, and avoids copying large intermediate payloads through the model (source).

ProDisco goes a step further: instead of exposing custom TypeScript modules, it provides a structured parameter search tool that dynamically extracts methods from upstream libraries using TypeScript AST parsing. This means:

Zero maintenance - Methods are extracted directly from library .d.ts files
Always current - Upgrading a dependency automatically exposes new methods
Type-safe - Full parameter types and return types included

Quick Start

Add to Claude Code

npm install npm run build # Example configs live in ./examples (see examples/README.md) # Then add the MCP server claude mcp add --transport stdio prodisco -- node dist/server.js --config examples/prodisco.postgres.yaml --install-missing

Remove if needed:

claude mcp remove prodisco

Environment Variables

Variable	Required	Description
`PRODISCO_CONFIG_PATH`	No	Path to the libraries config file (same as `--config`)
`KUBECONFIG`	No	(If using `@kubernetes/client-node`) Path to kubeconfig (defaults to `~/.kube/config`)
`PROMETHEUS_URL`	No	(If using `@prodisco/prometheus-client`) Prometheus server URL
`LOKI_URL`	No	(If using `@prodisco/loki-client`) Loki server URL

Important: Export environment variables before running claude mcp add. The --env flag may not reliably pass variables to the MCP server process.

Tip: If you're using a kind cluster for local testing, you can port-forward to Prometheus:
kubectl port-forward -n monitoring svc/prometheus-server 9090:80
Then set PROMETHEUS_URL="http://localhost:9090"

Development Setup

For local development:

git clone https://github.com/harche/ProDisco.git cd ProDisco npm install npm run build claude mcp add --transport stdio prodisco -- node dist/server.js claude mcp remove prodisco # remove when you're done

Startup Options:

Flag	Description
`--clear-cache`	Clear the scripts cache before starting
`--config <path>`	Path to YAML/JSON config listing libraries to index/allow
`--install-missing`	Auto-install missing libraries into `.cache/deps` (opt-in)
`--transport <mode>`	Transport mode: `stdio` (default) or `http`
`--host <host>`	HTTP host to bind to (default: `127.0.0.1`)
`--port <port>`	HTTP port (default: `3000`, implies `--transport http`)

node dist/server.js --clear-cache

Dynamic Libraries Configuration

ProDisco can be started with a config file that determines which npm packages are:

Indexed by prodisco.searchTools
Allowed in the sandbox via require() (kept in lockstep with indexing)

Example prodisco.config.yaml:

libraries: - name: "@kubernetes/client-node" description: "Kubernetes API client" - name: "@prodisco/prometheus-client" description: "Prometheus queries + metric discovery" - name: "@prodisco/loki-client" description: "Loki LogQL querying" - name: "simple-statistics" description: "Statistics helpers"

Start with a config file:

node dist/server.js --config prodisco.config.yaml

Optional: auto-install missing packages into .cache/deps:

node dist/server.js --config prodisco.config.yaml --install-missing

Environment variables:

Variable	Description
`PRODISCO_CONFIG_PATH`	Path to YAML/JSON config listing libraries
`PRODISCO_INSTALL_MISSING`	If set to `1`/`true`, auto-install missing libraries into `.cache/deps`

Build Docker Images From Config

If you want images that already contain the configured libraries (for deploying MCP and sandbox separately), you can build them directly from the same config file:

npm run docker:build:config -- --config prodisco.config.yaml

This builds:

prodisco/mcp-server:<configSha8> using the root Dockerfile
prodisco/sandbox-server:<configSha8> using packages/sandbox-server/Dockerfile

You can override image names/tags:

npm run docker:build:config -- --config prodisco.config.yaml --tag dev --mcp-image myorg/prodisco-mcp --sandbox-image myorg/prodisco-sandbox

HTTP Transport

ProDisco supports HTTP transport for network-based MCP connections, enabling remote access and containerized deployments.

Start in HTTP mode:

# HTTP mode on default port (3000) node dist/server.js --transport http # HTTP mode on custom port node dist/server.js --port 8080 # HTTP mode on all interfaces (for network access) node dist/server.js --host 0.0.0.0 --port 3000

Environment Variables:

Variable	Default	Description
`MCP_TRANSPORT`	`stdio`	Transport mode (`stdio` or `http`)
`MCP_HOST`	`127.0.0.1`	HTTP host to bind to
`MCP_PORT`	`3000`	HTTP port to listen on

HTTP Endpoints:

Endpoint	Method	Description
`/health`	GET	Health check, returns `{"status":"ok"}`
`/mcp`	POST	MCP JSON-RPC endpoint (Streamable HTTP)

Example: Connect with curl

# Health check curl http://localhost:3000/health # Initialize MCP session curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -H "Accept: application/json, text/event-stream" \ -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' # Use session ID from response header for subsequent requests curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -H "mcp-session-id: <session-id-from-init>" \ -d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}'

The HTTP transport uses the MCP SDK's StreamableHTTPServerTransport, which supports session management via mcp-session-id headers and Server-Sent Events (SSE) for streaming responses.

Available Tools

ProDisco exposes two tools:

prodisco.searchTools

Search and browse extracted API documentation for your startup-configured TypeScript libraries (from .d.ts). Use it to discover the correct method/type/function signatures before calling prodisco.runSandbox.

Document Types:

Type	Description
`method`	Class methods / instance APIs extracted from configured libraries
`type`	TypeScript types (interfaces/classes/enums/type aliases)
`function`	Standalone exported functions
`script`	Cached sandbox scripts
`all`	Search everything above (default)

Examples:

// Search broadly by name (methods/types/functions/scripts) // Replace the placeholders with terms relevant to your configured libraries. { methodName: "<search-term>" } // Filter by document type (methods/types/functions/scripts) { methodName: "<search-term>", documentType: "method" } // Find Loki query methods { documentType: "method", library: "@prodisco/loki-client", category: "query" } // Find Prometheus methods { methodName: "executeRange", library: "@prodisco/prometheus-client" } // Find analytics functions { documentType: "function", library: "simple-statistics" } // Search cached scripts { documentType: "script", methodName: "deployment" } // Get TypeScript type definitions (classes/interfaces/enums/type aliases) { methodName: "<type-or-class-name>", documentType: "type" } // Exclude certain categories/libraries { methodName: "query", exclude: { categories: ["delete"], libraries: ["some-library"] } }

For comprehensive documentation, see docs/search-tools.md.

prodisco.runSandbox

Execute TypeScript code in a sandboxed environment using the same configured library allowlist as prodisco.searchTools.

Execution Modes:

Mode	Purpose	Key Parameters
`execute` (default)	Blocking execution	`code` or `cached`, `timeout`
`stream`	Real-time output streaming	`code` or `cached`, `timeout`
`async`	Background execution	`code` or `cached`, `timeout`
`status`	Check async execution	`executionId`, `wait`, `outputOffset`
`cancel`	Cancel running execution	`executionId`
`list`	List active executions	`states`, `limit`

Sandbox Environment:

console - Captured output (log, error, warn, info)
require() - Restricted to configured npm packages (and their subpaths)
process.env - Environment variables

Examples:

// Execute code (default mode) { code: ` const k8s = require("@kubernetes/client-node"); const kc = new k8s.KubeConfig(); kc.loadFromDefault(); const api = kc.makeApiClient(k8s.CoreV1Api); const pods = await api.listNamespacedPod("default"); console.log(\`Found \${pods.body.items.length} pods\`); ` } // Run a cached script { cached: "script-2025-01-01T12-00-00-abc123.ts" } // Stream mode - real-time output { mode: "stream", code: "for(let i=0; i<5; i++) console.log(i)" } // Async mode - start long-running task { mode: "async", code: "longRunningTask()" } // Check async execution status { mode: "status", executionId: "abc-123", wait: true } // Cancel a running execution { mode: "cancel", executionId: "abc-123" } // Query Prometheus metrics { code: ` const { PrometheusClient, MetricSearchEngine } = require('@prodisco/prometheus-client'); const client = new PrometheusClient({ endpoint: process.env.PROMETHEUS_URL }); // Discover metrics semantically const search = new MetricSearchEngine(client); const metrics = await search.search("memory usage"); console.log('Found metrics:', metrics.map(m => m.name)); // Execute PromQL query const end = new Date(); const start = new Date(end.getTime() - 60 * 60 * 1000); const result = await client.executeRange('node_memory_MemAvailable_bytes', { start, end, step: '1m' }); console.log(\`Got \${result.data.length} time series\`); ` } // Query Loki logs { code: ` const { LokiClient } = require('@prodisco/loki-client'); const client = new LokiClient({ baseUrl: process.env.LOKI_URL }); const result = await client.queryRange('{namespace="default"}', { since: '1h', limit: 100 }); result.logs.forEach(log => console.log(\`[\${log.timestamp.toISOString()}] \${log.line}\`)); ` }

For architecture details, see docs/grpc-sandbox-architecture.md.

Advanced Analytics

ProDisco goes beyond simple resource fetching - it provides statistical analysis, machine learning, and signal processing capabilities for deep cluster observability.

Available Libraries:

Library	Purpose
`simple-statistics`	Mean, median, std dev, z-scores, percentiles, linear regression, correlation
`ml-regression`	Polynomial, exponential, and power regression for trend forecasting
`mathjs`	Matrix operations, linear algebra, symbolic math
`fft-js`	Fast Fourier Transform for detecting periodic patterns

Example Prompts:

Use Case	Prompt
Log Analysis	"Query Loki for error logs from the nginx app in the last hour. Show me the most common error patterns."
Cluster Health	"Analyze CPU and memory usage across all pods. Calculate mean, median, standard deviation, and identify outliers using z-scores. Show pods above the 95th percentile."
Memory Leaks	"Check for memory leaks. Fetch memory usage over 2 hours and use linear regression to identify pods with increasing memory."
Anomaly Detection	"Analyze network traffic and detect anomalies. Find receive/transmit rates more than 2 standard deviations from normal."
Correlation	"Find correlations between CPU and memory usage. Tell me if high CPU correlates with high memory."
Periodic Patterns	"Use FFT analysis on node CPU to detect periodic patterns. Are there dominant frequencies suggesting scheduled jobs?"
Capacity Planning	"Analyze resource trends and use polynomial regression to forecast when we might hit resource limits."

For detailed examples with code and output, see docs/analytics.md.

Advanced Deployment

Container Isolation

For stronger isolation, run the sandbox server in a Kubernetes cluster and connect via TCP.

1. Deploy the sandbox server:

# Build and load the image (for kind clusters) docker build -f packages/sandbox-server/Dockerfile -t prodisco/sandbox-server:latest . kind load docker-image prodisco/sandbox-server:latest # Deploy kubectl apply -f packages/sandbox-server/k8s/deployment.yaml # Port-forward to access locally kubectl -n prodisco port-forward service/sandbox-server 50051:50051

2. Configure the MCP server to use TCP:

export KUBECONFIG="${HOME}/.kube/config" export SANDBOX_USE_TCP=true export SANDBOX_TCP_HOST=localhost export SANDBOX_TCP_PORT=50051 claude mcp add --transport stdio prodisco -- node dist/server.js --config examples/prodisco.kubernetes.yaml

Transport Environment Variables:

Variable	Default	Description
`SANDBOX_USE_TCP`	`false`	Use TCP instead of local subprocess
`SANDBOX_TCP_HOST`	`localhost`	Sandbox server host
`SANDBOX_TCP_PORT`	`50051`	Sandbox server port

Transport Security (TLS/mTLS)

For production deployments, the sandbox server supports TLS and mutual TLS (mTLS):

Mode	Description
`insecure`	No encryption (default, for local development)
`tls`	Server-side TLS (client verifies server identity)
`mtls`	Mutual TLS (both client and server authenticate)

Configuration:

# Server-side TLS export SANDBOX_TRANSPORT_MODE=tls export SANDBOX_TLS_CERT_PATH=/path/to/server.crt export SANDBOX_TLS_KEY_PATH=/path/to/server.key # Client-side (MCP server) export SANDBOX_TRANSPORT_MODE=tls export SANDBOX_TLS_CA_PATH=/path/to/ca.crt

For Kubernetes deployments, use cert-manager to automate certificate management. See the k8s/cert-manager directory for ready-to-use manifests.

For full architecture and security details, see docs/grpc-sandbox-architecture.md.

Testing

Integration Tests

End-to-end testing with KIND cluster + Claude Agent SDK:

npm run test:integration

For detailed testing instructions, see docs/integration-testing.md.

Additional Documentation

Document	Description
docs/analytics.md	Advanced analytics guide - anomaly detection, forecasting, correlation, FFT analysis
docs/search-tools.md	Complete searchTools reference with examples and technical architecture
examples/README.md	Practical examples + runnable library config files (`examples/*.yaml`)
docs/grpc-sandbox-architecture.md	Sandbox architecture, gRPC protocol, and security configuration
docs/integration-testing.md	Integration test workflow and container tests

License

MIT

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security – no known vulnerabilities

license - permissive license

quality - confirmed to work

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