JSON MCP Filter

The JSON MCP Filter addresses a common bottleneck in AI‑driven data pipelines: the need to ingest only relevant portions of large or complex JSON payloads while preserving type safety and context limits. By turning raw JSON into well‑structured TypeScript interfaces and offering shape‑based extraction, the server lets developers trim massive API responses or local files down to precisely the fields that an LLM needs. This keeps prompt sizes within model limits, reduces network traffic for remote calls, and eliminates the risk of inadvertently exposing sensitive data.

At its core, the server exposes three intuitive tools. The json_schema tool consumes any JSON source—local or remote—and produces a TypeScript interface that mirrors the data’s shape. This gives developers confidence that downstream code can rely on accurate typings, preventing runtime surprises when the data structure evolves. The json_filter tool applies a declarative “shape” specification, selecting only the keys that match. It handles nested objects and arrays naturally, so a single filter can pull out deeply embedded fields without writing custom parsing logic. When the input exceeds 400 KB, the server automatically splits it into chunks, returning each slice with metadata that indicates its position in the original dataset. The json_dry_run tool is a lightweight pre‑flight check: it reports the expected size and number of chunks, allowing callers to plan resource usage before committing to a full extraction.

The practical use cases are broad. In chatbot integrations, developers can fetch a user profile from an external API and filter only the name, avatar URL, and preferences before passing it to Claude or another LLM. In data‑analysis workflows, a large telemetry log can be sliced into manageable chunks and typed for downstream processing or visualisation. The server’s built‑in 50 MB safety guard and clear error messages make it safe for production use, while the auto‑chunking feature removes the need to manually split files.

Integration is straightforward because the server follows MCP conventions. Clients such as Claude Desktop or Claude Code can call the tools directly over HTTP, receiving JSON payloads that are immediately consumable. The output interfaces can be imported into TypeScript projects, ensuring a seamless bridge between the AI’s generated context and the developer’s codebase. The combination of schema generation, smart filtering, remote support, and automatic chunking gives the JSON MCP Filter a unique edge: it turns arbitrary JSON into typed, context‑ready fragments in one step, freeing developers from boilerplate and allowing them to focus on business logic rather than data wrangling.