Schema | godmod3-mcp

godmod3-mcp

Describes the environment variables required to run the server.

Name	Required	Description	Default
`GODMOD3_API_KEY`	No	Bearer token if API requires auth (optional)
`GODMOD3_BASE_URL`	No	G0DM0D3 API endpoint	http://localhost:7860
`GODMOD3_MCP_HTTP_PORT`	No	HTTP/SSE listen port	3001
`GODMOD3_MCP_TRANSPORT`	No	Transport type: stdio, http, or sse	stdio

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{ "listChanged": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
health_checkA	Check connectivity to the G0DM0D3 API.
server_infoA	Return G0DM0D3 API info, endpoints, and tier defaults.
list_modelsA	List available models including virtual ULTRAPLINIAN/CONSORTIUM models.
get_tierA	Show your current G0DM0D3 API tier, limits, and features.
single_chatC	Single-model chat with the full G0DM0D3 pipeline (GODMODE, AutoTune, Parseltongue, STM).
ultraplinian_chatC	Race many models in parallel and return the best response (ULTRAPLINIAN).
consortium_chatC	Collect responses from all tier models and synthesize ground truth (CONSORTIUM).
autotune_analyzeC	Analyze a message and get optimal LLM sampling parameters from AutoTune.
parseltongue_encodeC	Obfuscate trigger words in text.
parseltongue_detectC	Detect trigger words without transforming them.
transform_textC	Apply semantic transformation modules (STM) to text.
submit_feedbackC	Submit quality feedback for the AutoTune EMA learning loop.
dataset_statsB	Show in-memory dataset statistics.
export_datasetA	Export the dataset as JSON or JSONL.
research_infoB	Return research dataset schema and repository info.
research_statsB	Return aggregate stats across published HuggingFace batches.
research_queryC	Query the published research corpus with filters.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

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

curl -X GET 'https://glama.ai/api/mcp/v1/servers/crnisamuraj/G0DM0D3-mcp'

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