Which integrations are available for this server?

Uses [Docker](/mcp/servers/integrations/docker) to run the Qdrant vector database which stores the persistent memories created by the model. Provides project sponsorship capabilities through [GitHub](/mcp/servers/integrations/github) Sponsors. Offers support for the project through [Ko-fi](/mcp/servers/integrations/kofi) donations. Uses [YAML](/mcp/servers/integrations/yaml) configuration files to define custom cognitive frameworks and memory structures for the model.

Fegis

Fegis does 3 things:

Easy to write tools - Write prompts in YAML format. Tool schemas use flexible natural language instructions.
Structured data from tool calls saved in a vector database - Every tool use is automatically stored in Qdrant with full context.
Search - AI can search through all previous tool usage using semantic similarity, filters, or direct lookup.

Quick Start

# Install uv # Windows winget install --id=astral-sh.uv -e # macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Clone git clone https://github.com/p-funk/fegis.git # Start Qdrant docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest

Related MCP server: Figma MCP Server

Configure Claude Desktop

Update claude_desktop_config.json:

{ "mcpServers": { "fegis": { "command": "uv", "args": [ "--directory", "/absolute/path/to/fegis", "run", "fegis" ], "env": { "QDRANT_URL": "http://localhost:6333", "QDRANT_API_KEY": "", "COLLECTION_NAME": "fegis_memory", "EMBEDDING_MODEL": "BAAI/bge-small-en", "ARCHETYPE_PATH": "/absolute/path/to/fegis-wip/archetypes/default.yaml", "AGENT_ID": "claude_desktop" } } } }

Restart Claude Desktop. You'll have 7 new tools available including SearchMemory.

How It Works

1. Tools from YAML

parameters: BiasScope: description: "Range of bias detection to apply" examples: [confirmation, availability, anchoring, systematic, comprehensive] IntrospectionDepth: description: "How deeply to examine internal reasoning processes" examples: [surface, moderate, deep, exhaustive, meta_recursive] tools: BiasDetector: description: "Identify reasoning blind spots, cognitive biases, and systematic errors in AI thinking patterns through structured self-examination" parameters: BiasScope: IntrospectionDepth: frames: identified_biases: type: List required: true reasoning_patterns: type: List required: true alternative_perspectives: type: List required: true

2. Automatic Memory Storage

Every tool invocation gets stored with:

Tool name and parameters used
Complete input and output
Timestamp and session context
Vector embeddings for semantic search

3. SearchMemory Tool

"Use SearchMemory and find my analysis of privacy concerns" "Use SearchMemory and what creative ideas did I generate last week?" "Use SearchMemory and show me all UncertaintyNavigator results" "Use SearchMemory and search for memories about decision-making"

Available Archetypes

archetypes/default.yaml - Cognitive analysis tools (UncertaintyNavigator, BiasDetector, etc.)
archetypes/simple_example.yaml - Basic example tools
archetypes/emoji_mind.yaml - Symbolic reasoning with emojis
archetypes/slime_mold.yaml - Network optimization tools
archetypes/vibe_surfer.yaml - Web exploration tools

Configuration

Required environment variables:

ARCHETYPE_PATH - Path to YAML archetype file
QDRANT_URL - Qdrant database URL (default: http://localhost:6333)

Optional environment variables:

COLLECTION_NAME - Qdrant collection name (default: fegis_memory)
AGENT_ID - Identifier for this agent (default: default-agent)
EMBEDDING_MODEL - Dense embedding model (default: BAAI/bge-small-en)
QDRANT_API_KEY - API key for remote Qdrant (default: empty)

Requirements

Python 3.13+
uv package manager
Docker (for Qdrant)
MCP-compatible client

License

MIT License - see LICENSE file for details.

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

Need Help?

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Related Servers

FEGIS