parallelix-mcp
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PARALLELIX_API_KEY | Yes | Your pk_live_… API key for the ParalleliX Compute API. | |
| PARALLELIX_BASE_URL | No | Base URL for the ParalleliX API. Can be used to point at a local coordinator for testing. | https://api.parallelix.io |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| parallel_mapA | Run the SAME instruction over MANY items in parallel on the ParalleliX network. Ideal for bulk classify / extract / summarize / translate where each item is independent. Returns one result per item with a Proof-of-Execution hash. Use this instead of looping single calls: it fans out across the network's nodes simultaneously. |
| inferA | Run a single prompt on the ParalleliX network. Returns the completion plus the serving node id and Proof-of-Execution hash. For bulk independent work, prefer parallel_map. |
| network_statusB | List the models the ParalleliX network currently serves. |
| usageA | Show this API key's request count, $PRLX credits spent, and remaining credit balance. |
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 4 tools
Each tool serves a distinct purpose: infer for single prompts, parallel_map for bulk, network_status for model info, usage for account info. No overlap.
All tool names follow the same lowercase_with_underscores pattern (infer, network_status, parallel_map, usage), providing a predictable and clean interface.
Four tools is perfectly scoped for a focused API wrapping inference, network info, and usage. Each tool is necessary and there are no redundant ones.
Covers the essential operations: single inference, bulk inference, network status, and usage. Minor gap: no explicit model selection parameter in infer/parallel_map, but it may be handled elsewhere.