Nexus MCP
An MCP server that enables AI models to invoke AI CLI agents (Codex, Claude Code, OpenCode) as
tools. Provides parallel execution, automatic retries with exponential backoff, JSON-first response
parsing, 10 discoverable prompt templates, model tier classification, and persistent preferences
through seven MCP tools, four MCP resources, and ten MCP prompts.
Use Cases
Nexus MCP is useful whenever a task benefits from querying multiple AI agents in
parallel rather than sequentially:
- Research & summarization — fan out a topic to multiple agents, then
synthesize their responses into a single summary with diverse perspectives
- Code review — send different files or review angles (security, correctness,
style) to separate agents simultaneously
- Multi-model comparison — prompt the same question to different models and
compare outputs side-by-side for quality or consistency
- Bulk content generation — generate multiple test cases, translations, or
documentation pages concurrently instead of one at a time
- Second-opinion workflows — get independent answers from separate agents
before making a decision, reducing single-model bias
Features
- Parallel execution —
batch_prompt fans out tasks with asyncio.gather and a configurable
semaphore (default concurrency: 3)
- Automatic retries — exponential backoff with full jitter for transient errors (HTTP 429/503)
- Output handling — JSON-first parsing, brace-depth fallback for noisy stdout, temp-file
spillover for outputs exceeding 50 KB
- Execution modes —
default (safe, no auto-approve), yolo (full auto-approve)
- CLI detection — auto-detects binary path, version, and JSON output capability at startup
- Persistent preferences — set defaults for execution mode, model, retries, output limit, and timeout; preferences persist across MCP sessions via the backing store (MemoryStore default, FileTreeStore/RedisStore for restart persistence)
- Prompt templates — 10 discoverable workflow scaffolds (code review, debug, research, implement feature, etc.) via
list_prompts/get_prompt; each returns structured messages with expert framing the client can use or ignore
- Model tier classification — heuristic-based model classification into quick/standard/thorough tiers; clients can override with sampling or live benchmarks. The
nexus://runners resource includes tier data per model
- Tool timeouts — configurable safety timeout (default 15 min) cancels long-running tool calls to prevent the server from blocking indefinitely
- Client-visible logging — runner events (retries, output truncation, error recovery) are sent to MCP clients via protocol notifications, not just server stderr
- Elicitation — interactive parameter resolution via MCP elicitation; disambiguates missing CLI, offers model selection, confirms YOLO mode, and prompts for elaboration on vague prompts. Auto-detects client support and skips gracefully when unavailable. Suppression flags prevent repeat prompts within a session
- Benchmark data sources — server instructions include URLs for Artificial Analysis, OpenRouter, Chatbot Arena, and LLM Stats so clients can fetch live model benchmarks without API keys
- Extensible — implement
build_command + parse_output, register in RunnerFactory
| Agent | Status |
|---|
| Codex | Supported |
| Claude Code | Supported |
| OpenCode | Supported |
Installation
Run with uvx (recommended)
uvx installs the package in an ephemeral virtual environment and runs it — no cloning required.
To check the installed version:
To update to the latest version:
uvx --reinstall nexus-mcp
MCP Client Configuration
Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"nexus-mcp": {
"command": "uvx",
"args": ["nexus-mcp"],
"env": {
"NEXUS_CODEX_MODEL": "gpt-5.2",
"NEXUS_CODEX_MODELS": "gpt-5.4,gpt-5.4-mini,gpt-5.3-codex,gpt-5.2-codex,gpt-5.2,gpt-5.1-codex-max,gpt-5.1-codex-mini",
"NEXUS_CLAUDE_MODEL": "claude-sonnet-4-6",
"NEXUS_CLAUDE_MODELS": "claude-sonnet-4-6,claude-haiku-4-5-20251001",
"NEXUS_OPENCODE_MODEL": "ollama-cloud/kimi-k2.5",
"NEXUS_OPENCODE_MODELS": "ollama-cloud/glm-5,ollama-cloud/kimi-k2.5,ollama-cloud/qwen3-coder-next,ollama-cloud/minimax-m2.5,ollama/gemini-3-flash-preview"
}
}
}
}
Cursor (.cursor/mcp.json in your project or ~/.cursor/mcp.json globally):
{
"mcpServers": {
"nexus-mcp": {
"command": "uvx",
"args": ["nexus-mcp"],
"env": {
"NEXUS_CODEX_MODEL": "gpt-5.2",
"NEXUS_CODEX_MODELS": "gpt-5.4,gpt-5.4-mini,gpt-5.3-codex,gpt-5.2-codex,gpt-5.2,gpt-5.1-codex-max,gpt-5.1-codex-mini",
"NEXUS_CLAUDE_MODEL": "claude-sonnet-4-6",
"NEXUS_CLAUDE_MODELS": "claude-sonnet-4-6,claude-haiku-4-5-20251001",
"NEXUS_OPENCODE_MODEL": "ollama-cloud/kimi-k2.5",
"NEXUS_OPENCODE_MODELS": "ollama-cloud/glm-5,ollama-cloud/kimi-k2.5,ollama-cloud/qwen3-coder-next,ollama-cloud/minimax-m2.5,ollama/gemini-3-flash-preview"
}
}
}
}
Claude Code (CLI):
claude mcp add nexus-mcp \
-e NEXUS_CODEX_MODEL=gpt-5.2 \
-e NEXUS_CODEX_MODELS=gpt-5.4,gpt-5.4-mini,gpt-5.3-codex,gpt-5.2-codex,gpt-5.2,gpt-5.1-codex-max,gpt-5.1-codex-mini \
-e NEXUS_CLAUDE_MODEL=claude-sonnet-4-6 \
-e NEXUS_CLAUDE_MODELS=claude-sonnet-4-6,claude-haiku-4-5-20251001 \
-e NEXUS_OPENCODE_MODEL=ollama-cloud/kimi-k2.5 \
-e NEXUS_OPENCODE_MODELS=ollama-cloud/glm-5,ollama-cloud/kimi-k2.5,ollama-cloud/qwen3-coder-next,ollama-cloud/minimax-m2.5,ollama/gemini-3-flash-preview \
-- uvx nexus-mcp
Generic stdio config (any MCP-compatible client):
{
"command": "uvx",
"args": ["nexus-mcp"],
"transport": "stdio",
"env": {
"NEXUS_CODEX_MODEL": "gpt-5.2",
"NEXUS_CLAUDE_MODEL": "claude-sonnet-4-6",
"NEXUS_OPENCODE_MODEL": "ollama-cloud/kimi-k2.5"
}
}
All env keys are optional — see Configuration for the full list.
Setup for Development
Prerequisites:
- Python 3.13+ (download)
- uv dependency manager (install guide)
curl -LsSf https://astral.sh/uv/install.sh | sh
Optional (for integration tests):
- Codex — check with
codex --version
- Claude Code — check with
claude --version
- OpenCode — check with
opencode --version
Claude Code note: Nexus invokes Claude Code non-interactively via claude -p.
Anthropic says claude -p and Agent SDK usage draw from separate monthly Agent SDK
credits starting 2026-06-15, while interactive Claude Code usage remains on plan usage
limits:
https://support.claude.com/en/articles/15036540-use-the-claude-agent-sdk-with-your-claude-plan
Note: Integration tests are optional. Unit tests run without CLI dependencies via subprocess mocking.
git clone <repository-url>
cd nexus-mcp
uv sync
uv run pre-commit install
uv run pytest
uv run mypy src/nexus_mcp
uv run ruff check .
uv run python -m nexus_mcp
OpenCode Server (Docker) — experimental
⚠️ Experimental — This integration has not been validated end-to-end by the maintainer. Expect rough edges in setup, auth, and tool exposure. The MCP tools surfaced from upstream OpenCode track the upstream project and may change without notice. Feedback and bug reports are welcome.
Run an isolated OpenCode server for HTTP-based agent execution alongside the CLI runner. Provides session management, file search, permissions, and 38 additional MCP tools when the server is healthy.
Quick start:
- Copy
.env.example to .env and set PROJECT_DIR to your project path:
- Start the server:
- Authenticate with your provider:
docker exec -it opencode-server opencode auth login
- Verify the server is healthy:
curl -u opencode:nexus http://localhost:4096/global/health
The server binds to 127.0.0.1 (localhost only) by default for security. See docs/opencode-server-setup.md for the full guide including remote access, multi-project setup, and network security.
Usage
Once nexus-mcp is configured in your MCP client, your AI assistant automatically sees its tools.
The reliable trigger is explicitly asking for output from an external AI agent (e.g. Codex, Claude Code, OpenCode).
Generic "do this in parallel" prompts may be handled by the host AI's own capabilities instead.
The cli parameter is optional — if omitted and the client supports MCP elicitation, the server will
ask which runner to use. The server provides runner metadata (names, models, availability,
execution modes) in its connection instructions — no discovery call needed. The cli parameter
includes a JSON schema enum listing valid runner names.
Usage Examples
Fan out a research question (batch_prompt)
You say: "Get perspectives from Codex, Claude Code, and OpenCode on transformer architectures."
{
"tasks": [
{ "cli": "codex", "prompt": "Summarize the key findings of the Attention Is All You Need paper", "label": "codex-summary" },
{ "cli": "claude", "prompt": "What are the main limitations of transformer architectures?", "label": "claude-limitations" },
{ "cli": "opencode", "prompt": "List 3 real-world applications of transformers beyond NLP", "label": "opencode-applications" }
]
}
Code review from multiple angles (batch_prompt)
You say: "Have Codex, Claude Code, and OpenCode each review this diff in parallel."
{
"tasks": [
{ "cli": "codex", "prompt": "Review this diff for security vulnerabilities:\n\n<paste diff>", "label": "codex-security-review" },
{ "cli": "claude", "prompt": "Review this diff for correctness and edge cases:\n\n<paste diff>", "label": "claude-correctness-review" },
{ "cli": "opencode", "prompt": "Review this diff for style and maintainability:\n\n<paste diff>", "label": "opencode-review" }
]
}
Single-agent prompt
You say: "Ask Codex to explain the difference between TCP and UDP."
{ "cli": "codex", "prompt": "Explain the difference between TCP and UDP in simple terms", "model": "gpt-5.2" }
Elicitation (server picks the runner)
You say: "Explain the CAP theorem using one of the available agents."
{ "prompt": "Explain the CAP theorem in simple terms" }
If the client supports MCP elicitation, the server asks which runner to use. Pass "elicit": false to skip.
Persistent preferences
You say: "Use YOLO mode with Codex from now on."
{ "execution_mode": "yolo", "model": "gpt-5.2", "max_retries": 5 }
Subsequent calls inherit these settings. Preferences persist across MCP sessions until explicitly cleared.
Fallback chain: explicit parameter → saved preference → per-runner env → global env → hardcoded default.
All prompt tools run as background tasks — they return a task ID immediately so the client can
poll for results, preventing MCP timeouts for long operations (e.g. YOLO mode: 2–5 minutes).
| Tool | Task? | Description |
|---|
batch_prompt | Yes | Fan out prompts to multiple runners in parallel; returns MultiPromptResponse |
prompt | Yes | Single-runner convenience wrapper; routes to batch_prompt |
set_preferences | No | Set or selectively clear persistent defaults for execution mode, model, retries, timeouts, elicitation, and trigger suppression |
get_preferences | No | Retrieve current preferences |
clear_preferences | No | Reset all preferences |
set_model_tiers | No | Save model tier classifications (client sends sampling/benchmark results; server persists) |
get_model_tiers | No | Retrieve saved model tier classifications |
batch_prompt
| Parameter | Required | Default | Description |
|---|
tasks | Yes | — | List of task objects (see below) |
max_concurrency | No | 3 | Max parallel agent invocations |
elicit | No | pref or true | Enable/disable interactive elicitation for this call |
Task object fields:
| Field | Required | Default | Description |
|---|
cli | No | — | Runner name (e.g. "codex"); if omitted, elicitation asks which runner to use |
prompt | Yes | — | Prompt text |
label | No | auto | Display label for results |
context | No | {} | Optional context metadata dict |
execution_mode | No | pref or "default" | "default" or "yolo" |
model | No | pref or CLI default | Model name override |
max_retries | No | pref or env default | Max retry attempts for transient errors |
output_limit | No | pref or env default | Max output bytes |
timeout | No | pref or env default | Subprocess timeout in seconds |
retry_base_delay | No | pref or env default | Base delay for exponential backoff |
retry_max_delay | No | pref or env default | Max delay cap for backoff |
Note: elicit is a batch-level parameter. When enabled, the server runs a single upfront elicitation pass across all tasks rather than prompting per-task.
prompt
Same parameters as a single task object in batch_prompt, plus elicit (batch-level in batch_prompt, per-call here).
set_preferences
| Parameter | Required | Default | Description |
|---|
execution_mode | No | — | "default" or "yolo" |
model | No | — | Model name (e.g. "gpt-5.2") |
max_retries | No | — | Max total attempts (≥1; 1 = no retries) |
output_limit | No | — | Max output bytes (≥1) |
timeout | No | — | Subprocess timeout seconds (≥1) |
retry_base_delay | No | — | Backoff base delay seconds (≥0) |
retry_max_delay | No | — | Backoff max delay seconds (≥0) |
elicit | No | true | Enable/disable elicitation |
confirm_yolo | No | true | Prompt before YOLO mode (auto-suppressed after first accept) |
confirm_vague_prompt | No | true | Prompt on very short prompts |
confirm_high_retries | No | true | Prompt when max_retries > 5 |
confirm_large_batch | No | true | Prompt when batch > 5 tasks |
clear_* | No | false | Clear any field individually (e.g. clear_model: true) |
get_preferences / clear_preferences
get_preferences — no parameters, returns all fields (null when unset).
clear_preferences — no parameters, resets all to null. Does not clear model tiers.
set_model_tiers
| Parameter | Required | Default | Description |
|---|
tiers | Yes | — | Dict mapping model names to tiers ("quick", "standard", "thorough") |
Persists tier classifications. Clients typically call once via sampling or benchmark fetch.
get_model_tiers
No parameters. Returns saved tiers as dict[str, str], or {} if none saved.
Managing Preferences
| Operation | Tool | Notes |
|---|
| Set fields | set_preferences | Persists across sessions |
| Read values | get_preferences | null for unset fields |
| Clear all | clear_preferences | Does not clear model tiers |
| Clear one field | set_preferences with clear_*: true | Others preserved |
| Suppress elicitation | set_preferences with confirm_*: false | YOLO/batch/retry auto-suppress after accept |
| Re-enable prompt | set_preferences with clear_confirm_*: true | Resets to default |
| Save/read tiers | set_model_tiers / get_model_tiers | Persists across sessions |
MCP Prompts
Nexus MCP provides 10 discoverable prompt templates that clients can browse via list_prompts() and render via get_prompt(name, args). Each prompt returns structured messages with expert framing — the client decides how (or whether) to use them.
Design principle: Server informs, client decides. Prompts provide the scaffold (role, structure, methodology); the client decides runner, model, depth, and orchestration. Prompts are completely optional — existing prompt/batch_prompt tools work exactly as before.
| Prompt | Tags | Parameters | Purpose |
|---|
code_review | analysis | file, instructions | Structured code review with findings by severity |
debug | analysis | error, context, file | Systematic diagnosis: reproduce, isolate, root cause, fix |
quick_triage | analysis | description, file | Fast assessment: what's wrong, severity, next step |
research | analysis | topic, scope | Structured research with source citations |
second_opinion | analysis | original_output, question | Independent review of another AI's output |
implement_feature | generation | description, language, constraints | Feature implementation with quality checklist |
refactor | generation | file, goal, constraints | Behavior-preserving restructuring |
bulk_generate | generation | template, variables | Expand template across variable sets |
write_tests | testing | file, framework, coverage_goal | Test generation with configurable coverage approach |
compare_models | comparison | prompt, criteria | Multi-runner comparison framework |
Example — using a prompt template
# 1. Client discovers available prompts
list_prompts() → sees "code_review", "debug", "compare_models", etc.
# 2. Client renders a prompt with arguments
get_prompt("code_review", {file: "src/auth.py", instructions: "security vulnerabilities"})
# 3. Server returns structured messages
→ PromptResult(
messages=[
Message("You are a senior code reviewer...", role="assistant"),
Message("Review the file `src/auth.py`...\nFocus: security vulnerabilities\n...", role="user"),
],
description="Code review of src/auth.py"
)
# 4. Client feeds messages into prompt/batch_prompt with chosen runner+model
prompt(cli="claude", prompt=<rendered messages>)
MCP Resources
Read-only data endpoints that clients query for runner metadata, configuration, and preferences.
| Resource URI | Description |
|---|
nexus://runners | All registered CLI runners with models (enriched with tier data), modes, availability |
nexus://runners/{cli} | Single runner details by name (URI template) |
nexus://config | Resolved operational config defaults (timeouts, retries, output limits) |
nexus://preferences | Current preferences with config fallback |
Models in nexus://runners include tier data: {"name": "gpt-5.4-mini", "tier": "quick"}. Tiers are quick (fast/cheap), standard (balanced), or thorough (max quality). Models with only heuristic tiers appear in unclassified_models — calling set_model_tiers moves them out.
Model tier enrichment examples
Before set_model_tiers — all tiers are heuristic guesses, all models are unclassified:
{
"models": [
{"name": "gpt-5.1-codex-max", "tier": "thorough"},
{"name": "gpt-5.4-mini", "tier": "quick"},
{"name": "claude-sonnet-4-6", "tier": "standard"}
],
"unclassified_models": ["gpt-5.1-codex-max", "gpt-5.4-mini", "claude-sonnet-4-6"]
}
After set_model_tiers — saved tiers replace heuristics, classified models leave the list:
{
"models": [
{"name": "gpt-5.1-codex-max", "tier": "thorough"},
{"name": "gpt-5.4-mini", "tier": "quick"},
{"name": "claude-sonnet-4-6", "tier": "standard"}
],
"unclassified_models": []
}
Configuration
Global Environment Variables
| Variable | Default | Description |
|---|
NEXUS_OUTPUT_LIMIT_BYTES | 50000 | Max output size in bytes before temp-file spillover |
NEXUS_TIMEOUT_SECONDS | 600 | Subprocess timeout in seconds (10 minutes) |
NEXUS_TOOL_TIMEOUT_SECONDS | 900 | Tool-level timeout in seconds (15 minutes); set to 0 to disable |
NEXUS_RETRY_MAX_ATTEMPTS | 3 | Max attempts including the first (set to 1 to disable retries) |
NEXUS_RETRY_BASE_DELAY | 2.0 | Base seconds for exponential backoff |
NEXUS_RETRY_MAX_DELAY | 60.0 | Maximum seconds to wait between retries |
NEXUS_CLI_DETECTION_TIMEOUT | 30 | Timeout in seconds for CLI binary version detection at startup |
NEXUS_EXECUTION_MODE | default | Global execution mode (default or yolo) |
Per-Runner Environment Variables
Pattern: NEXUS_{AGENT}_{KEY} (agent name uppercased). Per-runner values override global values.
Valid {AGENT} values: CLAUDE, CODEX, OPENCODE, OPENCODE_SERVER
| Variable pattern | Example | Description |
|---|
NEXUS_{AGENT}_MODEL | NEXUS_CODEX_MODEL=gpt-5.2 | Default model for this runner |
NEXUS_{AGENT}_MODELS | NEXUS_CODEX_MODELS=gpt-5.2,gpt-5.4-mini | Comma-separated model list (surfaced in server instructions) |
NEXUS_{AGENT}_TIMEOUT | NEXUS_CODEX_TIMEOUT=900 | Subprocess timeout override |
NEXUS_{AGENT}_OUTPUT_LIMIT | NEXUS_CODEX_OUTPUT_LIMIT=100000 | Output limit override |
NEXUS_{AGENT}_MAX_RETRIES | NEXUS_CLAUDE_MAX_RETRIES=5 | Max retry attempts override |
NEXUS_{AGENT}_RETRY_BASE_DELAY | NEXUS_CLAUDE_RETRY_BASE_DELAY=1.0 | Backoff base delay override |
NEXUS_{AGENT}_RETRY_MAX_DELAY | NEXUS_OPENCODE_RETRY_MAX_DELAY=30.0 | Backoff max delay override |
NEXUS_{AGENT}_EXECUTION_MODE | NEXUS_CODEX_EXECUTION_MODE=yolo | Execution mode override |
Invalid per-runner values are silently ignored (the global or hardcoded default is used instead).
Development
Testing
This project follows Test-Driven Development (TDD) with strict Red→Green→Refactor cycles.
uv run pytest
uv run pytest --cov=nexus_mcp --cov-report=term-missing
uv run pytest -m integration
uv run pytest -m "not integration"
uv run pytest -m "not slow"
uv run pytest tests/unit/runners/test_codex.py
Test markers:
@pytest.mark.integration — requires real CLI installations@pytest.mark.slow — tests taking >1 second
Code Quality
All quality checks run automatically via pre-commit hooks. Run manually:
uv run ruff check .
uv run ruff check --fix .
uv run ruff format .
uv run mypy src/nexus_mcp
uv run pre-commit run --all-files
Adding Dependencies
uv add <package>
uv add --dev <package>
uv sync
- Ruff: line length 100, 17 rule sets (E/F/I/W + UP/FA/B/C4/SIM/RET/ICN/TID/TC/ISC/PTH/TD/NPY) —
pyproject.toml → [tool.ruff]
- Mypy: strict mode, all type annotations required —
pyproject.toml → [tool.mypy]
- Pytest:
asyncio_mode = "auto", no @pytest.mark.asyncio needed — pyproject.toml → [tool.pytest.ini_options]
- Pre-commit: ruff-check, ruff-format, mypy, trailing-whitespace, end-of-file-fixer —
.pre-commit-config.yaml
Python 3.13+ Syntax
type keyword for type aliases: type AgentName = str- Union syntax:
str | None (not Optional[str])
match statements for complex conditionals- NO
from __future__ import annotations
Project Structure
nexus-mcp/
├── src/nexus_mcp/
│ ├── __main__.py # Entry point
│ ├── server.py # FastMCP server + tools + prompt registration
│ ├── types.py # Pydantic models
│ ├── exceptions.py # Exception hierarchy
│ ├── config.py # Environment variable config
│ ├── store.py # Persistent backing store access (preferences + tiers)
│ ├── tiers.py # Heuristic model tier classification
│ ├── elicitation.py # ElicitationGuard — interactive parameter resolution
│ ├── resources.py # MCP resources (runners, config, preferences)
│ ├── process.py # Subprocess wrapper
│ ├── parser.py # JSON→text fallback parsing
│ ├── cli_detector.py # CLI binary detection + version checks
│ ├── prompts/
│ │ ├── __init__.py # register_prompts(mcp) entry point
│ │ ├── analysis.py # code_review, debug, quick_triage, research, second_opinion
│ │ ├── generation.py # implement_feature, refactor, bulk_generate
│ │ ├── testing.py # write_tests
│ │ └── comparison.py # compare_models
│ └── runners/
│ ├── base.py # Protocol + ABC
│ ├── factory.py # RunnerFactory
│ ├── claude.py # ClaudeRunner
│ ├── codex.py # CodexRunner
│ ├── opencode.py # OpenCodeRunner
│ └── opencode_server.py # OpenCode server runner
├── tests/
│ ├── unit/ # Fast, mocked tests
│ │ └── prompts/ # Prompt template tests
│ ├── e2e/ # End-to-end MCP protocol tests
│ ├── integration/ # Real CLI tests
│ └── fixtures.py # Shared test utilities
├── .github/
│ └── workflows/ # CI, security, dependabot
├── pyproject.toml # Dependencies + tool config
└── .pre-commit-config.yaml # Git hooks configuration
Releases
Stable releases are cut by running the Tag Release workflow from the Actions
tab and choosing a bump (auto infers it from Conventional Commits since the
last tag). Pre-releases are tagged manually. See RELEASE.md for
the full maintainer workflow, recovery steps, and notes on server.json
placeholder fields.
License
MIT