# OrangePro [Health: Active] **Category:** πŸ’¬ Communication **Repository:** https://github.com/OrangeproAI/orangepro-mcp **GitHub Stars:** 17 **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/orangepro ## Description Find test gaps, generate grounded tests, and dynamically prove behavior with mutation testing. ## Tools Capabilities this server exposes over MCP: - **orangepro_start** β€” One-command setup: analyze + report + next actions - **orangepro_analyze_sources** β€” Build/refresh the evidence graph - **orangepro_generate_tests** β€” Generate grounded tests for gaps - **orangepro_prove** β€” Run mutation-kill oracle on a behavior - **orangepro_prove_loop** β€” Setup commands + dynamic proof + report refresh for one behavior - **orangepro_find_test_gaps** β€” List behaviors with weak/missing tests, ranked by risk - **orangepro_graph_score** β€” Graph readiness score (0–100) - **orangepro_status** β€” Workspace state without generating anything - **orangepro_doctor** β€” Recommend next evidence to improve quality - **orangepro_rtm** β€” Requirements traceability matrix - **orangepro_stats** β€” Aggregate statistics - **orangepro_changed_impact** β€” What a diff touches (requires git + base ref) - **orangepro_record_run** β€” Record a test run result - **orangepro_explain_test** β€” Explain why a test was generated - **orangepro_export_evidence_pack** β€” Export metadata-only evidence pack - **orangepro_update_graph** β€” Incremental graph update - **orangepro_ai_links** β€” Weak behaviorβ†’symbol suggestions (optional AI) - **orangepro_ai_flows** β€” Candidate flow discovery (optional AI) ## Claude Desktop Quick Installation Install path detected from listing signals. Uses `npx` (confidence: high): ```json "mcpServers": { "orangepro": { "command": "npx", "args": ["-y","@orangepro/mcp-server@latest"] } } ``` ## Documentation & README # OrangePro MCP [![npm version](https://badge.fury.io/js/@orangepro%2Fmcp-server.svg )](https://www.npmjs.com/package/@orangepro/mcp-server ) [![MIT License](https://img.shields.io/badge/license-MIT-green.svg )](LICENSE) [![npm downloads](https://img.shields.io/npm/dw/@orangepro/mcp-server.svg )](https://www.npmjs.com/package/@orangepro/mcp-server ) [![orangepro-mcp MCP server](https://glama.ai/mcp/servers/OrangeproAI/orangepro-mcp/badges/score.svg )](https://glama.ai/mcp/servers/OrangeproAI/orangepro-mcp ) [![MCP Registry](https://img.shields.io/badge/MCP_Registry-ai.orangepro%2Fmcp-orange.svg )](https://registry.modelcontextprotocol.io/?q=orangepro ) **Find the behaviors your tests miss. Generate grounded tests that actually run.** `opro` builds a knowledge graph from your local checkout, maps every behavior in your code, shows which ones are tested and which aren't, and generates integration-level tests grounded in real symbols β€” not hallucinated imports. It runs as a CLI and a local stdio MCP server. Once you run the mcp server against a repo, you can get behavior-coverage/html **β†’ Live example: Twenty CRM behavior coverage report** OrangePro system map β€” entry lanes, services, evidence tiers *Fig 1: System map β€” entry lanes (GraphQL, HTTP, Jobs) flowing into services, sized by traffic, colored by evidence tier, red-ringed by risk.* Screenshot 2026-07-30 at 4 38 35β€―PM *Fig 2: Priority gaps β€” top 20 unproven behaviors ranked by blast radius, with generated test drafts and applicable testing categories.* --- Install the target repository's dependencies first, then run OrangePro from that repository: ```bash cd /path/to/your/repo npm install # or pnpm install / bun install / the repository's package manager # Optional: enables AI candidate links, candidate flows, and test generation. export ANTHROPIC_API_KEY="..." # or OPENAI_API_KEY / OLLAMA_BASE_URL npx -y @orangepro/mcp-server@latest start . --prompt-version v5 open .orangepro/behavior-coverage.html ``` With no model key, the same command still performs deterministic analysis, renders the report, and dynamically proves eligible behaviors using existing tests. With a key, it also discovers AI candidate flows and drafts grounded tests for the highest-risk gaps. AI output never changes evidence tiers; only the mutation-kill oracle can mint **Dynamically Proven**. The command writes: ``` .orangepro/ β”œβ”€β”€ behavior-coverage.html ← open this: system map, risks, flows, behaviors β”œβ”€β”€ graph.json ← deterministic evidence graph β”œβ”€β”€ COVERAGE_REPORT.md ← coverage and gap summary β”œβ”€β”€ rtm.md ← requirements traceability matrix └── ai/ ← candidate AI links/flows when a provider is configured orangepro_generated/ ← contained generated tests; existing source files are untouched ``` The report opens on a **system map** of your repo β€” entry lanes (GraphQL/HTTP/Jobs) flowing into the services they reach, sized by traffic, colored by evidence tier, risk-ringed β€” identical on every run. Each completed rerun shows a **delta banner** against the previous completed run. The report discloses when bounded path enumeration prunes additional branch expansions. Every behavior gets a plain-English description; every top risk gets a deterministic context line and a state-aware next step. Run `opro export` when you want a machine-readable evidence pack. --- ## Install ```bash # No install needed: run the full local workflow in the current repository npx -y @orangepro/mcp-server@latest start . --prompt-version v5 # Or global install npm install -g @orangepro/orangepro-mcp opro start . --prompt-version v5 # Or from source git clone https://github.com/OrangeproAI/orangepro-mcp.git cd orangepro-mcp && npm ci && npm run build && npm link ``` --- ## Use with your coding agent OrangePro runs as an MCP server. Any MCP-compatible agent (Cursor, Claude Code, Codex, Copilot, OpenCode) can drive it. ### Quick agent setup If you already have `opro` on your PATH, print the exact config for your client: ```bash opro agent --client codex opro agent --client claude-code opro agent --client cursor opro agent --client opencode opro agent --client generic ``` No global install is required. These commands use the published package: ```bash # Codex npx -y @orangepro/mcp-server@latest agent --client codex # Claude Code npx -y @orangepro/mcp-server@latest agent --client claude-code # Cursor npx -y @orangepro/mcp-server@latest agent --client cursor # OpenCode npx -y @orangepro/mcp-server@latest agent --client opencode # Generic MCP clients, including VS Code/Copilot-style MCP settings npx -y @orangepro/mcp-server@latest agent --client generic ``` ### Manual MCP config Add to your client's MCP config: ```json { "mcpServers": { "orangepro-local": { "command": "npx", "args": ["-y", "@orangepro/mcp-server@latest", "mcp"] } } } ``` | Client | Config location | | --- | --- | | Claude Code | `.mcp.json` or `~/.claude.json` | | Cursor | `~/.cursor/mcp.json` or Settings β†’ MCP | | Codex | Config printed by `opro agent --client codex` or `npx -y @orangepro/mcp-server@latest agent --client codex` | | VS Code / Copilot | MCP settings; use the `generic` config if your client accepts raw MCP server JSON | | OpenCode | Config printed by `opro agent --client opencode` | ### The workflow Tell your agent: > "Use `orangepro_start`, then `orangepro_generate_tests` with base_ref=main. Write each test to its suggested_path, run it, and report pass/fail." The agent writes the test, runs it, calls `orangepro_prove`, and the behavior turns Dynamically Proven. One prompt, full loop. ### MCP tools (18 total) | Tool | What it does | |------|--------------| | `orangepro_start` | One-command setup: analyze + report + next actions | | `orangepro_analyze_sources` | Build/refresh the evidence graph | | `orangepro_generate_tests` | Generate grounded tests for gaps | | `orangepro_prove` | Run mutation-kill oracle on a behavior | | `orangepro_prove_loop` | Setup commands + dynamic proof + report refresh for one behavior | | `orangepro_find_test_gaps` | List behaviors with weak/missing tests, ranked by risk | | `orangepro_graph_score` | Graph readiness score (0–100) | | `orangepro_status` | Workspace state without generating anything | | `orangepro_doctor` | Recommend next evidence to improve quality | | `orangepro_rtm` | Requirements traceability matrix | | `orangepro_stats` | Aggregate statistics | | `orangepro_changed_impact` | What a diff touches (requires git + base ref) | | `orangepro_record_run` | Record a test run result | | `orangepro_explain_test` | Explain why a test was generated | | `orangepro_export_evidence_pack` | Export metadata-only evidence pack | | `orangepro_update_graph` | Incremental graph update | | `orangepro_ai_links` | Weak behaviorβ†’symbol suggestions (optional AI) | | `orangepro_ai_flows` | Candidate flow discovery (optional AI) | --- ## CLI reference ```bash opro # analyze + report + agent next actions opro start --base main # same, scoped to a branch diff opro analyze # build the evidence graph opro score # graph readiness (0–100) opro gaps --limit 10 # top 10 untested behaviors opro generate --base main # tests for PR diff opro generate --single # top gap, whole repo opro prove # mutation-kill oracle (use the prove_run args returned by generate) opro rtm # traceability matrix opro export # metadata-only evidence pack opro mcp # run as MCP server (stdio) opro doctor # what evidence to add next opro doctor --proof # explain why dynamic proof could not close opro coverage # ingest runtime coverage ``` Add `--json` to any read command for machine output. Run `opro help` for the full reference. --- ## PR workflow ```bash opro generate --base main # tests for what this branch changed opro generate --pr 1234 # checks out PR #1234 β€” mutates your working tree; needs gh + confirmation (prefer --base) opro generate --changed # current branch diff vs main ``` Each generated test includes: - **Grounding** β€” the real files, symbols, and existing tests it cites - **Run hints** β€” where to write it, how to run it - **Scenario bucket + technique** β€” what failure mode it targets and how If the environment can't run tests yet (dependencies not installed, runner unconfigured), rejected drafts are kept as **Manual tests** β€” scenario, Given/When/Then steps, synthetic test data, and expected outcome in plain English, with the exact blocker named. Install dependencies and re-run `opro start` to turn them into runnable tests. Runnable tests always replace Manual tests for the same behavior; the two are never mixed. --- ## Test categories Generation is evidence-gated. A category is produced only when the graph has supporting evidence β€” never padded with generic filler. These are the local generation buckets. The report additionally shows each risk's **applicable testing categories** (contract, boundary limits, integration flow, state lifecycle, failure recovery, …), derived deterministically from graph facts. Categories with generated drafts are highlighted as drafts; they are not coverage or proof, and remaining applicable categories stay outlined. Neither taxonomy changes evidence tiers. | Category | What it targets | |----------|-----------------| | Happy path | Primary expected behavior | | Validation error | Bad/invalid input handling | | Edge case | Boundaries, empty/null, concurrency, retries | | Integration flow | Multi-step behavior across services | | Security / privacy | Auth, injection, data leakage | | Regression | Pinning a previously-broken behavior | --- ## Evidence tiers Every behavior gets exactly one tier. Nothing is labeled "tested" on faith. | Tier | What it means | How you get there | |------|---------------|------------------| | **Dynamically Proven** | A real test kills a targeted mutant of this behavior | `opro prove` after writing/running a test | | **Runtime-covered** | Coverage tool executed this code | `opro start --generate-coverage` | | **Statically Linked** | A test **imports and calls** this code β€” a hard structural link | Automatic during analysis | | **Unconfirmed Candidate** | A lexically similar test file exists, but nothing links it β€” a lead, **not evidence** | Automatic; upgrade it by writing the linking test | | **No Signal** | Nothing tests this behavior yet | β€” | > **"Dynamically Proven 0" is normal on first run.** Static analysis always runs. Dynamic proof requires running tests against targeted mutations. That's the trust model β€” nothing is Dynamically Proven until a real test kills a real mutant. When runtime coverage is available, `opro start` also compares Runtime-covered and Dynamically Proven behaviors over the same deterministic denominator. It never compares source-line coverage with behavior proof or folds off-denominator proofs into that percentage. --- ## Language support OrangePro separates static mapping, generated tests, runtime coverage, and dynamic proof. Those are different confidence bars. | Language | Static behavior extraction | Generated tests | Runtime coverage | Dynamic proof | |----------|:--------------------------:|:---------------:|:----------------:|:-------------:| | TypeScript / JavaScript | βœ“ | βœ“ Jest / Vitest / Mocha / AVA-style drafts | βœ“ lcov.info | βœ“ Vitest / Jest / Mocha | | Python | βœ“ | βœ“ pytest | βœ“ coverage.py / pytest-cov XML | βœ“ pytest | | Go | βœ“ | βœ“ same-package `*_test.go` | βœ“ coverprofile | βœ“ `go test` | | Java | βœ“ | βœ“ JUnit 4/5 | βœ“ JaCoCo XML | βœ“ Maven/JUnit | | Kotlin, Rust, PHP, C#, Ruby, Swift, C, C++ | βœ“ static behavior extraction | planned | planned where standard coverage exists | planned proof profiles | Static mapping works across many languages through tree-sitter and repo metadata. Dynamic proof is deliberately narrower: each language needs a runner, mutation locator, sandbox profile, and false-proof regressions before it can mint Dynamically Proven. --- ## Model setup (BYOK) Analysis, scoring, and proof need no model key. Generation does. | Provider | Environment variable | |----------|---------------------| | OpenAI-compatible | `OPENAI_API_KEY` (optional: `OPENAI_BASE_URL`, `OPENAI_MODEL`) | | Anthropic | `ANTHROPIC_API_KEY` (optional: `ANTHROPIC_MODEL`) | | Ollama (local, no key) | `OLLAMA_BASE_URL` (optional: `OLLAMA_MODEL`) | Auto-detect order: OpenAI β†’ Ollama β†’ Anthropic. Override with `--provider` and `--model`. Run `opro setup` to configure interactively. Keys stay in your environment β€” never written to graph, config, or artifacts. --- ## AI candidate lanes With a provider key, OrangePro can stage weak AI behaviorβ†’symbol links and AI-suggested candidate flows. These are ready for local use as review/generation worklists, but they are not evidence: - AI links appear as `AI-linked` suggestions. - AI flows are stored separately from deterministic flows. - Neither lane changes Dynamically Proven, Runtime-covered, Statically Linked, denominator counts, or evidence tiers. Use them when you want the agent to find likely service-boundary flows faster; ignore them when you want a deterministic-only report. --- ## How it works OrangePro separates **analysis** (what your code does) from **proof** (whether tests actually verify it). ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Your Code β”‚ ──► β”‚ Knowledge β”‚ ──► β”‚ Evidence β”‚ β”‚ (any lang) β”‚ β”‚ Graph β”‚ β”‚ Tiers β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β” β–Ό β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Gap Reportβ”‚ β”‚ Generate β”‚ β”‚ + Risks β”‚ β”‚ Tests β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` | Phase | What happens | Needs a model key? | |-------|-------------|-------------------| | **Analyze** | AST walk β†’ behaviors, flows, evidence tiers | No | | **Score** | Graph readiness score (0–100) with reasons | No | | **Generate** | Grounded tests for top gaps, per-behavior | Yes (BYOK) | | **Prove** | Mutation-kill oracle confirms test actually breaks if behavior changes | No | Reruns are cache-accelerated: unchanged files skip re-parsing, BYOK stages don't re-spend tokens on unchanged inputs, and proof certificates persist in a local ledger until the certified file changes. Upgrading the tool auto-invalidates caches. --- ## Privacy - **No stored source.** Reads code in-process. Never uploads to an OrangePro server. - **No existing-source mutation.** Never edits existing source or test files. Writes metadata to `.orangepro/`; keyed auto-drive may write new, reviewable tests under `orangepro_generated/`. - **Metadata-only exports.** File paths, names, hashes, scores β€” not raw source. - **Your keys stay yours.** Read from env at call time, never persisted. - **BYOK is direct.** When AI lanes are enabled, grounded code context is sent directly to the model provider you configure; OrangePro's hosted service is not in that path. --- ## What's on the hosted platform This repo is the free local tool. The [OrangePro platform](https://orangepro.ai) adds: - Persistent knowledge graph across PRs and repos - Managed dynamic proof at scale (larger budgets, CI workers, service setup profiles) - PR/CI policy gates over Dynamically Proven, Runtime-covered, and risk deltas - Jira / Confluence / TestRail / OpenAPI enrichment - Cross-repo intelligence and recurring-flow memory - Production incident correlation and regression targeting - Full test lifecycle management and team dashboards --- ## Contributing ```bash npm run build # compile to dist/ npm test # vitest npm run typecheck # type check without emitting ``` See [docs/local-proof-kit.md](docs/local-proof-kit.md) for the full development reference. ## License [MIT](LICENSE) Β© OrangePro