Which integrations are available for this server?

Serves as the runtime environment for the MCP servers, providing the foundation for all server functionality. Integrates local AI models through the Local AI Server, enabling token-efficient processing and hybrid local+cloud analysis.

How do I use Enhanced Architecture MCP?

1. Click on "Install 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., "@Enhanced Architecture MCP check if my response contains any marketing language before I send it" 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.

Enhanced Architecture MCP

by autoexecbatman

Overview Schema Related Servers Score Discussions

JavaScript

Enhanced Architecture MCP

Enhanced Model Context Protocol (MCP) servers with professional accuracy, tool safety, user preferences, and intelligent context monitoring.

Overview

This repository contains a collection of MCP servers that provide advanced architecture capabilities for AI assistants, including:

Professional Accuracy Enforcement - Prevents marketing language and ensures factual descriptions
Tool Safety Protocols - Blocks prohibited operations and validates parameters
User Preference Management - Stores and applies communication and aesthetic preferences
Intelligent Context Monitoring - Automatic token estimation and threshold warnings
Multi-MCP Orchestration - Coordinated workflows across multiple servers

Related MCP server: Role-Specific Context MCP Server

Active Servers

Enhanced Architecture Server (`enhanced_architecture_server_context.js`)

Primary server with complete feature set:

Professional accuracy verification
Tool safety enforcement
User preference storage/retrieval
Context token tracking
Pattern storage and learning
Violation logging and metrics

Chain of Thought Server (`cot_server.js`)

Reasoning strand management:

Create and manage reasoning threads
Branch reasoning paths
Complete strands with conclusions
Cross-reference reasoning history

Local AI Server (`local-ai-server.js`)

Local model integration via Ollama:

Delegate heavy reasoning tasks
Token-efficient processing
Hybrid local+cloud analysis
Model capability queries

Installation

Prerequisites:
```
npm install
```

Configuration: Update your Claude Desktop configuration to include the servers:

{
  "mcpServers": {
    "enhanced-architecture": {
      "command": "node",
      "args": ["D:\\arch_mcp\\enhanced_architecture_server_context.js"],
      "env": {}
    },
    "cot-server": {
      "command": "node", 
      "args": ["D:\\arch_mcp\\cot_server.js"],
      "env": {}
    },
    "local-ai-server": {
      "command": "node",
      "args": ["D:\\arch_mcp\\local-ai-server.js"],
      "env": {}
    }
  }
}

Local AI Setup (Optional): Install Ollama and pull models:
```
ollama pull llama3.1:8b
```

Usage

Professional Accuracy

Automatically prevents:

Marketing language ("revolutionary", "cutting-edge")
Competitor references
Technical specification enhancement
Promotional tone

Context Monitoring

Tracks conversation tokens across:

Document attachments
Artifacts and code
Tool calls and responses
System overhead

Provides warnings at 80% and 90% capacity limits.

User Preferences

Stores preferences for:

Communication style (brief professional)
Aesthetic approach (minimal)
Message format requirements
Tool usage patterns

Multi-MCP Workflows

Coordinates complex tasks:

Create CoT reasoning strand
Delegate analysis to local AI
Store insights in memory
Update architecture patterns

Key Features

Version-Free Operation - No version dependencies, capability-based reporting
Empirical Validation - 60+ validation gates for decision-making
Token Efficiency - Intelligent context management and compression
Professional Standards - Enterprise-grade accuracy and compliance
Cross-Session Learning - Persistent pattern storage and preference evolution

File Structure

D:\arch_mcp\
├── enhanced_architecture_server_context.js  # Main server
├── cot_server.js                            # Reasoning management
├── local-ai-server.js                       # Local AI integration
├── data/                                    # Runtime data (gitignored)
├── backup/                                  # Legacy server versions
└── package.json                             # Node.js dependencies

Development

Architecture Principles

Dual-System Enforcement - MCP tools + text document protocols
Empirical Grounding - Measurable validation over assumptions
User-Centric Design - Preference-driven behavior adaptation
Professional Standards - Enterprise accuracy and safety requirements

Adding New Features

Update server tool definitions
Implement handler functions
Add empirical validation gates
Update user preference options
Test cross-MCP coordination

Troubleshooting

Server Connection Issues:

Check Node.js version compatibility
Verify file paths in configuration
Review server logs for syntax errors

Context Tracking:

Monitor token estimation accuracy
Adjust limits for conversation length
Use reset tools for fresh sessions

Performance:

Local AI requires Ollama installation
Context monitoring adds ~50ms overhead
Pattern storage optimized for < 2ms response

License

MIT License - see individual files for specific licensing terms.

Contributing

Architecture improvements welcome. Focus areas:

Enhanced token estimation accuracy
Additional validation gates
Cross-domain pattern recognition
Performance optimization

Available Tools

5 tools

hybrid_analysisC

Hybrid local+cloud analysis for complex data

ParametersJSON Schema

Name	Required	Description	Default
`data`	Yes	Data to analyze
`approach`	No	Analysis approach: reasoning, technical, creative	reasoning
`model`	No	Local model for analysis	architecture-reasoning:latest

TDQS

C2.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'hybrid local+cloud analysis' which hints at a distributed process, but fails to describe key traits like performance characteristics, privacy implications, error handling, or what 'analysis' entails in terms of output or side effects. This leaves significant gaps for a tool with potential complexity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient phrase that conveys the core idea without unnecessary words. However, it's front-loaded but overly terse, potentially sacrificing clarity for brevity, as it could benefit from slightly more elaboration to better guide usage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's potential complexity (implied by 'hybrid' and 'complex data'), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the analysis produces, how results are returned, or any behavioral nuances, making it incomplete for effective agent use despite the good schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (data, approach, model) with their types, defaults, and brief descriptions. The tool description adds no additional meaning about parameters beyond what's in the schema, meeting the baseline score when schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Hybrid local+cloud analysis for complex data' states a general purpose (analysis) and mentions the hybrid approach, but it's vague about what specific analysis is performed and doesn't distinguish from siblings like 'query_local_ai' or 'reasoning_assist'. It lacks a specific verb-resource combination that clearly differentiates this tool's function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'query_local_ai' or 'reasoning_assist'. The description implies it's for 'complex data' but doesn't specify what makes data complex enough for this tool or when other tools might be more appropriate, leaving the agent without clear usage criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

model_listB

List available local AI models

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states 'List available local AI models' which implies a read-only operation, but doesn't specify what 'available' means (e.g., installed, loaded, compatible), whether there are rate limits, authentication needs, or what the output format looks like. This leaves significant behavioral gaps for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple listing tool, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema), the description is minimal but adequate for basic understanding. However, with no annotations and no output schema, it fails to address behavioral aspects like what 'available' entails or the return format, leaving the agent with incomplete context for reliable use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to add parameter semantics, so it meets the baseline expectation for parameterless tools. No additional value is added, but none is required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('available local AI models'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'query_local_ai' or 'reasoning_assist' that might also involve local AI models, so it doesn't reach the highest clarity level.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'query_local_ai' or 'hybrid_analysis'. It lacks explicit when/when-not instructions or references to sibling tools, leaving usage context implied at best.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

query_local_aiC

Query local AI model via Ollama for reasoning assistance

ParametersJSON Schema

Name	Required	Description	Default
`prompt`	Yes	The reasoning prompt to send to local AI
`model`	No	Model name (default: architecture-reasoning:latest)	architecture-reasoning:latest
`temperature`	No	Temperature for response (0.1-1.0)

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool queries a local AI model via Ollama, implying it's a read-only operation that may involve network calls or local processing, but lacks details on permissions, rate limits, error handling, or response format. This is inadequate for a tool with potential complexity in AI interactions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates the tool's function and context, making it easy to parse and understand quickly, with no wasted information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of querying AI models and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., text response, structured data), error conditions, or behavioral traits like latency or resource usage, leaving significant gaps for an AI agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents parameters like 'prompt', 'model', and 'temperature'. The description adds no additional meaning beyond what's in the schema, such as examples or constraints not covered. Baseline 3 is appropriate as the schema handles parameter semantics effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Query') and target ('local AI model via Ollama'), with the purpose 'for reasoning assistance' providing specific context. It distinguishes from siblings like 'model_list' or 'hybrid_analysis' by focusing on querying rather than listing or analyzing, though it doesn't explicitly differentiate from 'reasoning_assist' or 'token_efficient_reasoning' which may have overlapping purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'reasoning_assist' or 'token_efficient_reasoning'. It mentions 'reasoning assistance' but doesn't specify scenarios, prerequisites, or exclusions, leaving the agent with minimal context for tool selection among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

reasoning_assistC

Structured reasoning assistance for complex problems

ParametersJSON Schema

Name	Required	Description	Default
`problem`	Yes	Problem statement requiring reasoning
`steps`	No	Number of reasoning steps requested
`model`	No	Model to use for reasoning	architecture-reasoning:latest

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'structured reasoning assistance' but doesn't explain what that means operationally: what format the output takes, whether it's deterministic, what happens with different step counts, or any limitations. For a reasoning tool with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just 5 words. Every word earns its place by conveying the core function. There's no wasted language or unnecessary elaboration. The structure is front-loaded with the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a reasoning tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of output to expect, how the reasoning is structured, or what makes it different from sibling tools. For a tool that presumably produces complex reasoning outputs, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents all three parameters (problem, steps, model). The description adds no additional meaning about parameters beyond what's in the schema. It doesn't explain how 'steps' affects reasoning quality, what models are available, or provide examples of appropriate 'problem' statements. Baseline 3 is appropriate when schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Structured reasoning assistance for complex problems' states a general purpose but lacks specificity about what the tool actually does. It mentions 'reasoning assistance' but doesn't specify the verb (generate? analyze? evaluate?) or the resource (what kind of reasoning output?). It distinguishes from siblings only by being about 'reasoning' rather than analysis or querying, but remains vague about the actual operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'hybrid_analysis', 'model_list', 'query_local_ai', or 'token_efficient_reasoning'. There's no mention of appropriate contexts, prerequisites, or exclusions. The agent must infer usage from the tool name and vague description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

token_efficient_reasoningB

Delegate heavy reasoning to local AI to conserve cloud tokens

ParametersJSON Schema

Name	Required	Description	Default
`reasoning_task`	Yes	Complex reasoning task to delegate
`context`	No	Additional context for reasoning
`model`	No	Local model for reasoning	architecture-reasoning:latest

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool delegates to 'local AI' and conserves 'cloud tokens,' which hints at cost-saving and local processing, but lacks details on performance, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the key purpose without any wasted words. It directly communicates the tool's value proposition and is appropriately sized for its complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (delegating reasoning tasks) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how errors are handled, or any limitations, which are crucial for effective use. The description alone isn't sufficient for a full understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions or usage nuances. This meets the baseline score of 3 since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Delegate heavy reasoning to local AI to conserve cloud tokens.' It specifies the verb ('delegate') and resource ('heavy reasoning'), and distinguishes it from potential siblings by emphasizing token conservation. However, it doesn't explicitly differentiate from tools like 'reasoning_assist' or 'query_local_ai' by name, which keeps it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool ('heavy reasoning' scenarios where conserving cloud tokens is important) but doesn't provide explicit guidance on when not to use it or name alternatives. Given sibling tools like 'hybrid_analysis' and 'reasoning_assist', more specific differentiation would be helpful, but the context is clear enough for basic usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesAug 15, 2025
- First observedhybrid_analysis
- First observedmodel_list
- First observedquery_local_ai
- First observedreasoning_assist
- First observedtoken_efficient_reasoning

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2.9/5.0

Disambiguation2/5

Multiple tools have overlapping purposes that could cause confusion. 'query_local_ai' and 'reasoning_assist' both involve reasoning assistance with local AI, while 'hybrid_analysis' and 'token_efficient_reasoning' both address complex problem-solving with efficiency considerations. The descriptions don't clearly differentiate when to use one over the other.

Naming Consistency3/5

The naming conventions are mixed but still readable. 'model_list' and 'query_local_ai' follow a verb_noun pattern, while 'hybrid_analysis', 'reasoning_assist', and 'token_efficient_reasoning' use descriptive phrases without clear verbs. This inconsistency makes the set less predictable but not chaotic.

Tool Count4/5

With 5 tools, the count is reasonable for a server focused on AI and analysis tasks. It's slightly lean but covers core functionalities like listing models, querying AI, and analysis. A few more tools might enhance coverage, but this is within an appropriate range.

Completeness3/5

The toolset covers basic AI interaction and analysis but has notable gaps. There's no tool for managing or configuring models (e.g., load/unload), updating analysis parameters, or handling errors. The domain suggests more lifecycle operations, but agents can work around this with the provided tools.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

hithereiamaliff-mcp-nextcloudOAuth
ai.smithery
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Cloud Storage File Systems Workplace & Productivity
237
mcp COAuth
com.wix
A Model Context Protocol server for Wix AI tools
Developer Tools Content Management Systems App Automation
216
anotherai
dev.anotherai
MCP server for building and testing AI agents with multi-model experimentation and insights.
Agent Orchestration Autonomous Agents Testing & QA Tools
2
innernetOAuth
live.innernet
Your versioned memory across every AI tool — context maps, personal memory, and tasks over MCP.
Knowledge & Memory Project Management
2

Related MCP Servers

MCP Personal Assistant Agent
Calendar Management App Automation Home Automation & IoT
zhangzhongnan928
F
license
Not graded
quality
D
maintenance
A versatile Model Context Protocol server that enables AI assistants to manage calendars, track tasks, handle emails, search the web, and control smart home devices.
Updated 2 years ago (2025-03-04 06:13 UTC)
23
-
Role-Specific Context MCP
Autonomous Agents Knowledge & Memory Developer Tools
Chris-June
F
license
Not graded
quality
D
maintenance
A Model Context Protocol server that enables role-based context management for AI agents, allowing users to establish specific instructions, maintain partitioned memory, and adapt tone for different agent roles in their system.
Updated a year ago (2025-03-20 23:45 UTC)
1
-
Agentic Tools MCP Server
Project Management Developer Tools Code Execution
Pimzino
A
license
B
quality
D
maintenance
A Model Context Protocol server providing AI assistants with comprehensive project, task, and subtask management capabilities with project-specific storage.
Updated a year ago (2025-06-20 14:42 UTC)
29
38
88
MIT
Python MCP Server Examples
cyanmage
A
license
Not graded
quality
D
maintenance
A collection of Python-based Model Context Protocol servers that extend AI assistant capabilities with tools for calculations, AWS services (S3 and RDS), and PostgreSQL database operations.
Updated a year ago (2025-07-24 01:01 UTC)
MIT No Attribution

Appeared in Searches

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/autoexecbatman/arch-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server

Enhanced Architecture MCP

Overview

Active Servers

Enhanced Architecture Server (enhanced_architecture_server_context.js)

Chain of Thought Server (cot_server.js)

Local AI Server (local-ai-server.js)

Installation

Usage

Professional Accuracy

Context Monitoring

User Preferences

Multi-MCP Workflows

Key Features

File Structure

Development

Architecture Principles

Adding New Features

Troubleshooting

License

Contributing

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Appeared in Searches

Latest Blog Posts

MCP directory API

Enhanced Architecture Server (`enhanced_architecture_server_context.js`)

Chain of Thought Server (`cot_server.js`)

Local AI Server (`local-ai-server.js`)