How do I use Manifold Engine MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Manifold Engine MCP Server index and analyze the repo for chaos" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Manifold Engine MCP Server

by SepDynamics

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Manifold Engine MCP Server

A Model Context Protocol server that gives AI coding assistants high-speed, structural codebase analysis and dynamic context management. Instead of reading code token-by-token, it maps repositories structurally — treating source files as continuous byte-streams, computing structural signatures, and detecting architectural chaos in O(1) time.

Current Status: All Phase 0 critical bugs have been resolved, including FAISS semantic search deserialization, ingest_repo relative path resolution, combined risk formula transparency, and distinct cluster naming heuristics. All 21 analysis tools are fully functional.

Core Capabilities

Capability	Description
Structural Indexing	Scans every file into a Valkey-backed grid with compressed signatures and chaos profiles. Sub-millisecond retrieval.
Chaos Detection	Calculates a `chaos_score` per file. Predicts which files will become unmaintainable.
Dependency & Risk Analysis	Traces import graphs via AST parsing, measures blast radius, combines chaos × blast into a single risk score.
Working Memory	Inject persistent facts (project conventions, architecture rules) into a Dynamic Semantic Codebook for zero-shot learning.
Real-Time Sync	Filesystem watcher auto-ingests file saves in milliseconds, keeping the index current without manual re-scans.

Related MCP server: OpenLore

Installation

Prerequisites

Python 3.10+
Valkey (or Redis) running on localhost:6379
C++20 compiler (for building the native structural engine)
faiss-cpu (required for running the cluster_codebase_structure tool)

Setup

# 1. Start the Valkey backend
valkey-server

# 2. Install the manifold engine
pip install -e .

# 3. Configure your MCP client (Kilo Code, Claude Desktop, Cursor, etc.)

Add to your MCP client configuration:

{
  "mcpServers": {
    "manifold": {
      "command": "path/to/virtualenv/bin/python",
      "args": ["path/to/SEP-mcp/mcp_server.py"],
      "cwd": "path/to/SEP-mcp"
    }
  }
}

Common Workflows & Use Cases

1. New Repository Analysis

When entering a new codebase, execute an initial mapping to immediately understand technical debt distribution.

# 1. Index the repository
ingest_repo  (root_dir=".", clear_first=true, compute_chaos=true)

# 2. Verify ingestion was successful
get_index_stats

# 3. Quickly identify the most complex, chaotic files
batch_chaos_scan  (pattern="*.py", max_files=30)

2. Refactoring Decisions & Sprint Planning

Use the structural risk metrics to objectively decide what code to rewrite.

# 1. Scan for files with BOTH high complexity and high import dependencies
scan_critical_files       (pattern="*.py", max_files=10)

# 2. For candidates, predict how soon the file will become unmaintainable
predict_structural_ejection (path="target.py", horizon_days=30)

# 3. For visual confirmation, generate a 4-panel chaos dashboard
visualize_manifold_trajectory (path="target.py")

3. Finding Implementation Patterns

Rather than searching by text, search by structural signatures to find similar logic blocks or clones.

# 1. Get the mathematical signature of an existing file
get_file_signature  (path="found_file.py")

# 2. Search for any other files that match this structure globally
search_by_structure (signature="c0.213_s0.000_e0.928", tolerance=0.05)

4. Continuous Flow

# Run the watcher inside the repo
start_watcher  (watch_dir=".")

# The engine now automatically syncs and re-evaluates risk natively whenever files are saved.

Tool Reference (20 Tools)

All tools are documented with parameters, examples, and workflows in MCP_TOOL_GUIDE.md.

Indexing & Monitoring

Tool	Purpose	Quick Command
`ingest_repo`	Full repository scan into Valkey with signatures and chaos profiles	`ingest_repo root_dir="." clear_first=true compute_chaos=true`
`get_index_stats`	Real-time index health: document count, memory, chaos averages	`get_index_stats`
`start_watcher`	Background filesystem observer — auto-ingests on file save/delete	`start_watcher watch_dir="."`

File Discovery & Search

Tool	Purpose	Quick Command
`list_indexed_files`	Browse indexed files by glob pattern	`list_indexed_files pattern="*.py" max_results=200`
`get_file`	Read full file content from the Valkey index	`get_file path="src/manifold/sidecar.py"`
`search_code`	Keyword or regex search across all indexed files with context	`search_code query="chaos_score" file_pattern="*.py"`
`get_file_signature`	Structural fingerprint (c/s/e) for a file	`get_file_signature path="mcp_server.py"`
`search_by_structure`	Find files with similar structural signatures	`search_by_structure signature="c0.213_s0.000_e0.928" tolerance=0.05`
`search_by_signature_sequence`	Find files containing a contiguous signature sequence	`search_by_signature_sequence text="..." min_signatures=3`

Chaos Analysis

Tool	Purpose	Quick Command
`analyze_code_chaos`	Per-file chaos score, entropy, coherence, collapse risk	`analyze_code_chaos path="mcp_server.py"`
`batch_chaos_scan`	Rank all files by chaos score (highest risk first)	`batch_chaos_scan pattern="*.py" max_files=50`
`predict_structural_ejection`	Forecast when a file becomes unmaintainable	`predict_structural_ejection path="mcp_server.py" horizon_days=30`
`visualize_manifold_trajectory`	Generate a 4-panel dashboard of chaos dynamics	`visualize_manifold_trajectory path="mcp_server.py"`
`cluster_codebase_structure`	K-Means clustering of files by structural signatures	`cluster_codebase_structure pattern="*.py" n_clusters=5`

Dependency & Combined Risk

Tool	Purpose	Quick Command
`analyze_blast_radius`	Import dependency tree and impact file count	`analyze_blast_radius path="src/manifold/sidecar.py"`
`compute_combined_risk`	Combined risk = 0.6×chaos + 0.4×blast	`compute_combined_risk path="src/manifold/sidecar.py"`
`scan_critical_files`	Repository-wide scan for highest combined risk	`scan_critical_files pattern="*.py" max_files=20`

Verification & Memory

Tool	Purpose	Quick Command
`compute_signature`	Compress arbitrary text into manifold signatures	`compute_signature text="def hello(): ..."`
`verify_snippet`	Check if code structurally matches existing codebase patterns	`verify_snippet snippet="..." coverage_threshold=0.5 scope="*.py"`
`inject_fact`	Add persistent knowledge to the Dynamic Semantic Codebook	`inject_fact fact_id="api_rules" fact_text="All endpoints use snake_case."`
`remove_fact`	Remove a previously injected fact	`remove_fact fact_id="api_rules"`

Empirical Validation

React 15.0 Case Study

The structural engine flagged the exact files (ReactReconcileTransaction.js, ReactInstanceHandles.js) that the React core team subsequently deleted and rewrote as React Fiber — without any semantic understanding of JavaScript. → Read the study

Langchain ROC Analysis

A blind forward-prediction study on langchain v0.0.300 proved that a chaos score threshold of 0.396 optimally predicts architectural ejection, achieving ~91% of the predictive power of expensive AST-parsing tools at O(1) speed. → Read the study

Memory Optimization

Zstandard compression reduced the cpython index from 3.4 GB to 28 MB (>99% reduction) with zero loss in retrieval accuracy.

Documentation

Document	Purpose
MCP_TOOL_GUIDE.md	Comprehensive tool reference, workflows, best practices
reports/	Validation studies and generated reports

License

MIT — see LICENSE

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Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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Manifold Engine MCP Server

Core Capabilities

Installation

Prerequisites

Setup

Common Workflows & Use Cases

1. New Repository Analysis

2. Refactoring Decisions & Sprint Planning

3. Finding Implementation Patterns

4. Continuous Flow

Tool Reference (20 Tools)

Indexing & Monitoring

File Discovery & Search

Chaos Analysis

Dependency & Combined Risk

Verification & Memory

Empirical Validation

React 15.0 Case Study

Langchain ROC Analysis

Memory Optimization

Documentation

License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Code Expert MCP Server

OpenLore

Blueprint MCP

Paparats MCP