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Decentralized peer-to-peer LLM inference network with MCP tool integration
SwarmLLM provides 7 tools with complete input schemas and descriptions. Naming follows verb_noun convention (chat, compare, research, batch_prompts). Schemas are well-structured with typed parameters and descriptions. However, output schemas are not documented in the source, parameter descriptions lack depth (many under 72 chars baseline), and error handling guidance is absent. The delegate and node_info tools have empty input schemas with minimal descriptions, suggesting incomplete implementation. No evidence of pagination support, result limits, or recovery guidance in error cases.
Execute multiple independent prompts in parallel, each targeting a specific model. Returns all results once complete. Ideal for offloading parallel subtasks — e.g., ask one model to summarize, another to translate, another to review code, all at once.
Send a chat completion request to the SwarmLLM inference engine
Send the same prompt to multiple models concurrently and return all responses side-by-side for comparison. Supports local, network, and cloud models.
Offload a task to the most appropriate model based on capability matching
List available models on this SwarmLLM node and network
Get information about this SwarmLLM node
delegate and node_info tools have empty input schemas ({}), making them non-functional for LLM invocation. No parameters defined, no validation possible.
Output schemas are not documented anywhere in the source. LLMs cannot plan downstream tool calls or extract required fields (e.g., model IDs from compare/research results) without knowing response structure.
No error handling guidance. Tools lack recovery hints (e.g., 'If model not found, call models() first'). No categorization of errors as retryable vs fatal.
models and node_info descriptions are under 50 characters, below the 72-char baseline for parameter annotations. Insufficient context for LLM tool selection.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 64 | <=2025-11-25 | v2 |
Fan out a research question to multiple models in parallel and collect all responses. Designed for knowledge gathering — send a question to cheap/fast models to get diverse perspectives without using expensive model tokens. Each model's response is returned separately with latency and token usage.
batch_prompts and research accept optional model arrays but lack guidance on behavior when omitted (e.g., 'uses all available models'). Undocumented defaults invite misuse.