Drand
MCP ServerFree** - An MCP server for fetching verifiable random numbers from the [drand network](https://drand.love).
Capabilities5 decomposed
verifiable-random-number-fetching-from-drand-network
Medium confidenceFetches cryptographically verifiable random numbers from the drand distributed randomness network by querying beacon endpoints and returning signed randomness values with cryptographic proofs. The MCP server acts as a bridge between Claude/LLM clients and drand's HTTP API, handling beacon selection, response parsing, and proof validation to ensure randomness integrity without requiring clients to directly interact with blockchain or distributed systems.
Implements drand integration as an MCP server, allowing LLM agents to access verifiable randomness through Claude's native tool-calling interface without requiring direct HTTP client management or cryptographic library dependencies in the agent code. Uses drand's public beacon endpoints and BLS signature verification to guarantee randomness authenticity.
Unlike simple PRNG libraries or centralized randomness APIs, drand provides cryptographically-verifiable, publicly-auditable randomness that cannot be manipulated by any single entity, making it ideal for trustless AI systems; MCP integration makes it accessible to LLM agents without custom networking code.
drand-beacon-endpoint-abstraction
Medium confidenceAbstracts the complexity of selecting and querying drand beacon endpoints by providing a unified interface that handles beacon discovery, endpoint routing, and fallback logic. The server manages multiple beacon configurations (mainnet, testnet, etc.) and automatically routes requests to healthy endpoints, hiding network topology details from the LLM client.
Provides beacon endpoint abstraction at the MCP server level, allowing Claude agents to reference beacons by logical name rather than URL, with server-side configuration enabling multi-beacon support and transparent failover without agent code changes.
Simpler than agents managing their own beacon endpoint lists and retry logic; more flexible than hardcoding a single drand endpoint, enabling network-agnostic agent code.
cryptographic-proof-validation-for-randomness
Medium confidenceValidates drand randomness proofs using BLS signature verification to ensure that returned random values are authentic and have not been tampered with. The server performs signature validation against drand's public key material, allowing clients to trust randomness integrity without implementing cryptographic verification themselves.
Implements BLS signature verification at the MCP server boundary, validating drand proofs before returning randomness to Claude agents, ensuring agents receive only authenticated randomness without requiring cryptographic libraries in the agent runtime.
Provides cryptographic assurance that alternatives like centralized randomness APIs cannot offer; validation happens server-side, reducing client complexity compared to agents implementing their own BLS verification.
round-number-aware-randomness-queries
Medium confidenceSupports querying randomness for specific drand rounds or fetching the latest available round, with automatic round number management and validation. The server handles round-to-timestamp mapping and allows agents to request historical randomness (within drand's retention window) or the current round without manual round calculation.
Abstracts drand's round-based randomness model, allowing Claude agents to query by round number or request 'latest' without understanding drand's internal round timing, while preserving round metadata for audit and reproducibility.
More precise than timestamp-based randomness APIs; enables reproducible randomness queries that can be audited and replayed, unlike one-time randomness generation.
mcp-tool-schema-for-randomness-access
Medium confidenceExposes drand randomness fetching as an MCP tool with a well-defined JSON schema, enabling Claude to discover, understand, and invoke randomness queries through the standard MCP tool-calling interface. The schema documents parameters, return types, and usage patterns, allowing Claude's native tool-use capabilities to orchestrate randomness-dependent workflows.
Implements drand as a first-class MCP tool with complete JSON schema, enabling Claude's native tool-use orchestration without requiring custom integration code or agent-side API management.
Cleaner integration than agents managing raw HTTP calls; leverages Claude's built-in tool-use reasoning to decide when and how to invoke randomness, compared to hardcoded randomness calls in agent logic.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓AI agents and LLM applications requiring cryptographically-secure randomness
- ✓Teams building trustless systems where randomness provenance matters
- ✓Developers integrating drand into Claude-based workflows via MCP
- ✓LLM agents requiring high-availability randomness access
- ✓Applications supporting multiple drand networks or custom beacon configurations
- ✓Teams wanting to abstract infrastructure complexity from AI logic
- ✓Trustless systems where randomness authenticity is critical
- ✓Auditable AI applications requiring cryptographic proof of randomness source
Known Limitations
- ⚠Depends on drand network availability and beacon response times (typically 12-second rounds)
- ⚠No built-in caching of randomness values — each request hits the drand network
- ⚠Limited to drand's supported beacon chains; cannot generate custom randomness schemes
- ⚠Proof validation requires understanding drand's BLS signature scheme; not suitable for applications needing simple PRNG
- ⚠Endpoint health checking is passive (based on response success/failure) rather than active probing
- ⚠No built-in load balancing across multiple healthy endpoints
Requirements
Input / Output
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** - An MCP server for fetching verifiable random numbers from the [drand network](https://drand.love).
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