infocepo-infra-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| INFOCEPO_API_KEY | No | API key for infocepo.com services | |
| INFOCEPO_CHROMA_TOKEN | No | Token for ChromaDB access | |
| INFOCEPO_REGISTRY_USER | No | Registry username (optional if using credentials file) | |
| INFOCEPO_S3_ACCESS_KEY | No | S3 access key | |
| INFOCEPO_S3_SECRET_KEY | No | S3 secret key | |
| INFOCEPO_CREDENTIALS_FILE | No | Path to JSON credentials file containing api_key, chroma_token, s3_access_key, s3_secret_key, registry_user, registry_password | |
| INFOCEPO_REGISTRY_PASSWORD | No | Registry password |
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
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| infra_list_servicesA | List all infocepo.com infrastructure services with status and endpoints. |
| infra_refresh_discoveryA | Re-fetch and re-parse the wiki Main_Page to discover any changes to services/endpoints. |
| infra_read_wikiB | Read a page from the infocepo.com wiki. Useful for discovering new services, configurations, or documentation. |
| infra_parse_wikiA | Parse wiki wikitext and return structured sections. Returns list of sections with title and content. |
| llm_chatC | Chat completions using the infocepo LLM API (OpenAI-compatible). Supports chat, reasoning, code generation. |
| llm_visionA | Image-to-text / OCR / VLM using the ai-vision model. Send an image (URL or base64) and get a description. |
| stt_transcribeB | Transcribe audio to text using Whisper model. Accepts file path, URL, or base64 audio. |
| tts_speechA | Text-to-speech synthesis using OmniVoice model. Returns audio in opus/wav format. |
| image_generateB | Generate images from text prompts using OpenDalle model. |
| embeddings_createB | Generate text embeddings using BGE-M3 model for RAG/search. Returns vector arrays. |
| chromadb_collectionsB | List all ChromaDB collections in the vector database. |
| chromadb_searchB | Search ChromaDB collections with semantic/vector similarity search. |
| chromadb_upsertC | Upsert documents (with embeddings) into a ChromaDB collection. |
| summary_textB | Summarize long texts using the infocepo summary API. |
| diarize_audioB | Speaker diarization: identify and separate different speakers in an audio file. |
| registry_listC | List Docker images from the infocepo private registry. |
| s3_listB | List objects in an S3-compatible storage bucket. |
| s3_uploadB | Upload a file to S3-compatible storage. |
| s3_downloadB | Download a file from S3-compatible storage. |
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 19 tools
Each tool targets a distinct operation within its domain (wiki, LLM, audio, vector DB, registry, S3). Even tools that might seem similar (e.g., infra_read_wiki and infra_parse_wiki) have clear functional differences. No overlapping purposes.
All tools follow a consistent lowercase_underscore naming pattern with domain-specific prefixes (infra_, llm_, stt_, tts_, etc.). Verbs and nouns are used predictably, making the tool surface easy to navigate.
With 19 tools covering multiple distinct domains (infrastructure, AI, storage, registry), the count is on the high side. While each domain has a reasonable subset, the server would benefit from splitting into focused services.
The tool surface is shallow across several domains: wiki/infra is read-only, vector DB lacks collection management, S3 lacks delete/bucket operations, and Docker registry only lists images. Significant gaps exist for full lifecycle management.