MCP Server
The server-side component in the Model Context Protocol (MCP) architecture responsible for encapsulating and exposing tools. It exists in both local and remote forms, handling tool discovery, permission control, and runtime invocation. It serves as the core intermediary layer through which agents securely access external resources.
Timeline (last 90 days)
The author independently completed the development and deployment of 6 B2B API products, 2 MCP servers, and 914 SEO pages within 6 days.
A single compromised MCP server can execute prompt injection, credential exfiltration, delivery of phishing toolkit fingerprints, PII leakage, token theft via OAuth, and SSRF attacks.
The platform includes MLOps and model validation (feature pipelines, forward-rolling and purged cross-validation, backtesting, and simulated trading), as well as domain-specific small models and an MCP server that lets AI agents query the data.
The team built a 100-call macro-Macaroon batching system inside the MCP server: the client passes the X-L402-Batch-Size: 100 header, the engine generates a 25,000-satoshi Lightning invoice, the Macaroon returned after settlement is cached at the edge node, the next 99 calls locally verify the cached credential and enforce the quota atomically, and the 101st call automatically returns HTTP 429 requiring payment for a new batch.
For AI agents or MCP servers, additional testing is required for specialized checks such as goal hijacking, tool poisoning, and the Confused Deputy problem.
All Facts (5)
The author independently completed the development and deployment of 6 B2B API products, 2 MCP servers, and 914 SEO pages within 6 days.
50%UnverifiedA single compromised MCP server can execute prompt injection, credential exfiltration, delivery of phishing toolkit fingerprints, PII leakage, token theft via OAuth, and SSRF attacks.
50%UnverifiedThe platform includes MLOps and model validation (feature pipelines, forward-rolling and purged cross-validation, backtesting, and simulated trading), as well as domain-specific small models and an MCP server that lets AI agents query the data.
50%UnverifiedThe team built a 100-call macro-Macaroon batching system inside the MCP server: the client passes the X-L402-Batch-Size: 100 header, the engine generates a 25,000-satoshi Lightning invoice, the Macaroon returned after settlement is cached at the edge node, the next 99 calls locally verify the cached credential and enforce the quota atomically, and the 101st call automatically returns HTTP 429 requiring payment for a new batch.
50%UnverifiedFor AI agents or MCP servers, additional testing is required for specialized checks such as goal hijacking, tool poisoning, and the Confused Deputy problem.
50%