Mars MCP Backend Analyzer
Provides optional agent mode using Ollama for local AI analysis of projects.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Mars MCP Backend Analyzer
Mars MCP Backend Analyzer is a local, read-only analyzer for Python backend projects. It is built to work well with Codex and other MCP clients by exposing small, focused tools instead of dumping an entire repository into the model context.
The project focus is simple:
scan Python backend projects safely
build compact project maps and project briefs
find files relevant to a user question
outline source files before reading them
read only specific line ranges when possible
provide a planning step before larger analysis work
Why This Exists
Large codebases are expensive to send to an AI model. Mars works as a local indexer and context compressor:
User question
-> mars_plan_task
-> mars_project_brief
-> mars_find_relevant_files
-> mars_outline_file / mars_search_code
-> mars_read_lines
-> final answer from Codex or another AI clientThis keeps token usage lower and makes the analysis process easier to monitor.
Related MCP server: LocalNest MCP
Features
Read-only MCP server over stdio
CLI fallback for local use
Backend-focused file scanner
Ignore rules for
.env, virtual environments, caches, build output, binary files, and common dependency foldersProject brief and project map tools
Relevant file selection
File outline and line-range reading
Deterministic task planner
Optional Ollama agent mode
MCP Tools
Mars exposes these tools:
mars_plan_taskmars_project_briefmars_find_relevant_filesmars_project_mapmars_backend_strategy_filesmars_scan_projectmars_search_codemars_outline_filemars_read_linesmars_read_filemars_analyze_backend
Prefer the low-token flow:
mars_plan_task
-> mars_project_brief
-> mars_find_relevant_files
-> mars_outline_file or mars_search_code
-> mars_read_lines
-> final answerUse mars_read_file only when exact full-file context is required.
Install
python -m venv venv
venv\Scripts\python.exe -m pip install -r requirements.txtOn Git Bash or Linux-like shells:
python -m venv venv
source venv/Scripts/activate
python -m pip install -r requirements.txtCLI Usage
Show a compact project brief:
./mars project-brief "C:\path\to\backend"Create a plan before analysis:
./mars plan "C:\path\to\backend" "berikan alur kerja backend ini" --depth normalFind relevant files:
./mars relevant-files "C:\path\to\backend" "debug error login"Read only a small range:
./mars read-lines "C:\path\to\backend" app/main.py 1 80Run the optional Ollama agent:
./mars agent "C:\path\to\backend" "analisis project ini" --depth normalCodex MCP Config
Example Codex config:
[mcp_servers.mars]
command = "C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\powershell.exe"
args = [
"-NoProfile",
"-ExecutionPolicy",
"Bypass",
"-File",
"C:\\project AI\\Mars-MCP-backend-analyzer\\mars-mcp.ps1"
]
cwd = "C:\\project AI\\Mars-MCP-backend-analyzer"
[mcp_servers.mars.env]
MARS_MCP_PYTHON = "C:\\path\\to\\python.exe"After changing the MCP server code or tool schema, restart Codex or reconnect the MCP server so the updated tools are loaded.
Token Benchmark
The exact token count depends on project size and the question, but the expected shape is:
Approach | Context sent to model | Expected token use | Notes |
Without Mars MCP | Many full files copied manually | High | Simple but wasteful for large projects |
With | Compact file list and symbols | Medium | Good for overview questions |
With | Brief, selected files, and small line ranges | Low | Best default for Codex workflows |
See docs/token-benchmark.md for the benchmark template.
Testing
Run the test suite:
pytestCurrent coverage focuses on the safety-critical local tools:
path traversal protection
.envblockingignored directory scanning
line read limits
search line numbers
Safety Model
Mars is intended to be read-only. The MCP tools are designed to inspect a local project, not modify it. Keep write operations in the AI/client layer explicit and separate from Mars.
Documentation
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
This server cannot be deployed
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