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New: Dukehoff MCP for Java 0.9.1 →

apache 2.0 · open source

Dukehoff MCP for Java

The Java IDE for coding agents.

Coding agents edit Java like text: search, open, edit, repeat. Dukehoff MCP gives them what an IDE gives you, as tools they call over MCP. It runs locally, needs no open IDE, and keeps its index warm between sessions.

For Claude Code, GitHub Copilot (VS Code and CLI), Codex, Cursor and omp. Maven, Gradle and Ant. Windows, macOS, Linux; JDK 17 or newer.

Dukehoff MCP on GitHub

Debug: Run a test under the debugger, stop at a method, line or exception, and read the stack and variables, instead of guessing from print statements.
Migrate: Type-safe, rule-based search and replace across the whole code base (Jackpot rules), with a dry run first. Matches types, not text.
Refactor: Rename, move, change signature, safe delete and inline, for every caller and override, returned as one diff.
Navigate: Usages, implementations, symbols and outlines: semantic answers, not text matches that include comments and same-named methods.
Check: Compile errors of the whole workspace, without running a build.

Measured on a code base of about 8,900 Java files, three sessions each with and without Dukehoff MCP (Claude Sonnet 5, October 2026, credits at GitHub Copilot's rates).

task	turns without	turns with	credits without	credits with
Rename a public method and its 24 calls, leaving same-named methods of other classes alone	47.7	4.7	54.3	7.2
Find the 32 subclasses of a class, 5 of them indirect	9.0	5.3	17.8	9.1

The gain is largest in refactorings and cross-file questions, where text search needs many steps or gets it wrong.

New: dukehoff-decide 0.1.0 on Maven Central →

apache 2.0 · open source

dukehoff-decide

Type-safe decisions from small language models.

Many LLM calls ask a question whose answer is one of a few options: which team handles this ticket, is this spam, how urgent is it. dukehoff-decide turns the question into a Java type and answers it with a small local model. You get a typed value, probabilities you can rely on, and a gate that sends uncertain cases to a human.

Java 21. Any OpenAI-compatible server with log-probabilities, such as Ollama or llama.cpp. Spring Boot starter included. On Maven Central as com.dukehoff.decide.

dukehoff-decide on GitHub Maven Central

@Question("Triage an incoming support ticket")
record Triage(Priority priority, Team team,
              boolean churnRisk) {}

Decision<Triage> d = triage.decide(State.of(text));
d.value();                  // always a valid Triage
d.distribution("priority"); // Map<Priority, Double>

Calibrate: A few hundred labelled examples fit the model's confidence to reality, with a report of accuracy and errors per field.
Gate: Decide automatically above a threshold, hand the rest to a person.

Customer messages, 11 categories, 1.5B model: 93.7% accuracy after calibration; at a 0.9 threshold 71% are decided automatically, 99.5% of those correctly.

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