Deploying an MCP server to production means moving from a local stdio process on your laptop to a stable, remotely accessible service that handles multiple clients reliably. The shift introduces requirements that local development sidesteps: persistent availability, authentication, TLS, monitoring, and graceful handling of errors that would just crash a local process. This guide covers … Read more
MCP authentication controls which clients can connect to an MCP server and which actions they can perform. For local stdio servers running on your own machine, authentication is not required — only local processes can communicate with the server through OS-level pipes. For remote SSE servers accessible over a network, authentication is essential. MCP’s authentication … Read more
MCP security matters because MCP servers can have significant access to sensitive systems — filesystems, databases, APIs, internal tools. A poorly secured MCP deployment can expose sensitive data, allow unauthorised actions, or become a vector for prompt injection attacks. The threat model is different from typical web application security because the attack surface includes the … Read more
ChatGPT Plugins and MCP (Model Context Protocol) are both ways to extend AI assistants with external tools and data, but they were built with fundamentally different goals and architectures. ChatGPT Plugins were designed as a consumer marketplace — a curated store of capabilities users could add to ChatGPT. MCP is designed as a developer protocol … Read more
MCP servers communicate with clients over one of two transport mechanisms: stdio (standard input/output) or SSE (Server-Sent Events over HTTP). The choice of transport determines how the server is deployed, how clients connect to it, and what operational complexity it introduces. Most developers start with stdio for local development and migrate to SSE or HTTP … Read more
MCP servers expose three capability types to clients: tools, resources, and prompts. These three types are not interchangeable — they represent fundamentally different interaction patterns, and choosing the right one for each capability you want to expose is a core design decision when building an MCP server. Getting this wrong leads to servers that are … Read more
MCP (Model Context Protocol) and function calling both let AI models use external tools and data, but they solve the problem at different layers. Function calling is an API feature — a capability built into specific model providers that lets the model request structured function invocations within a single conversation. MCP is a protocol — … Read more
Processing PDFs with a vision LLM means treating each page as an image and passing it to a multimodal model rather than extracting the text layer first. This approach handles PDFs that break standard text extraction: scanned documents with no text layer, PDFs with complex layouts where text extraction produces garbled output, files with important … Read more
Extracting text from images with a language model means using a vision-capable LLM as an OCR engine — passing an image to the model and asking it to return the text it contains. For many use cases this works better than traditional OCR: it handles handwriting, low-quality scans, unusual fonts, mixed languages, and complex layouts … Read more
Speech-to-text models have diversified rapidly since Whisper’s release. In 2026, practitioners choosing a local STT model face a genuine selection problem: there are fast models and accurate models and streaming models and multilingual models, and the best choice depends on the specific deployment scenario. This guide cuts through the options with a task-first framework — … Read more
