Skip to main content
Glama

Fegis

Fegis does 3 things:

  1. Easy to write tools - Write prompts in YAML format. Tool schemas use flexible natural language instructions.

  2. Structured data from tool calls saved in a vector database - Every tool use is automatically stored in Qdrant with full context.

  3. 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: AGI 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.

A
license - permissive license
-
quality - not tested
D
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

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

Related MCP Servers

View all related MCP servers

Related MCP Connectors

  • Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.

  • User-owned memory for AI agents, Copilot, Claude, IDEs, CLIs, and chat apps over remote MCP.

  • A Model Context Protocol server for Wix AI tools

View all MCP Connectors

Latest Blog Posts

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/p-funk/FEGIS'

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