godmod3-mcp
Server Configuration
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 |
Instructions
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.
Capabilities
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 | {} |
Tools
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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 17 tools
Most tools have clearly distinct purposes, such as single_chat vs. ultraplinian_chat vs. consortium_chat, and parseltongue_encode vs. parseltongue_detect. Minor overlap exists between health_check and server_info (both relate to connectivity/info) and between dataset_stats and research_stats (both provide statistics but on different datasets), but these are distinguishable with careful reading.
Naming conventions are mixed: some tools follow verb_noun patterns (list_models, get_tier, transform_text, submit_feedback, export_dataset), while others use noun_verb or compound nouns (health_check, parseltongue_encode, parseltongue_detect, server_info, dataset_stats). All names are readable and use snake_case, but the inconsistent verb placement and noun-only names prevent a higher score.
With 17 tools, the server falls into the 'heavy' range (16-25). The tool count is justified by the broad scope of the API, covering chat, optimization, text transformation, and research, but it still feels slightly bloated and could be consolidated by merging some informational tools or research stats endpoints.
The tool surface is largely complete for the stated domain: it covers chat (single, ULTRAPLINIAN, CONSORTIUM), AutoTune analysis, Parseltongue encoding/detection, STM transformation, feedback, dataset export, and research querying. Minor gaps include no Parseltongue decoding and no dataset upload/management, but these are not critical for the core API usage.