MCP server exposing Chutes.ai management + inference endpoints. BETA.
11 tools with consistent naming (verb_noun pattern: list_, get_, chutes_*). All have descriptions (avg ~80 chars, within baseline 34-392). Input schemas present for all tools with proper types and defaults. However, descriptions are terse and lack LLM-optimized guidance on WHEN to use each tool or what to do with results. Parameter descriptions are minimal (e.g., 'Page number for pagination' lacks range constraints). Output schemas are not documented, LLMs cannot plan downstream calls. Error handling is absent from tool definitions. No tool annotations (readOnlyHint/destructiveHint). chutes_deploy_vllm is marked [BETA] but lacks confirmation/dry-run pattern for a destructive write operation.
Run a chat completion against Chutes.ai. Returns the assistant message only.
[BETA] Deploy a vLLM chute. WRITES: POST /chutes/vllm. Consumes paid compute.
Get current user active discounts.
Fetch TEE attestation evidence for a specific chute. Requires a 64-hex-char nonce (auto-generated if omitted).
Get current user quotas.
Get aggregated invocation usage for the current user.
No output schemas documented. LLMs cannot infer what fields to expect from tool responses, blocking downstream tool chaining and forcing exploratory calls.
Parameter descriptions lack constraints. 'Page number for pagination' does not specify valid range (0 - N?). 'Maximum tokens in response' lacks bounds. LLMs will pass invalid values without guidance.
chutes_deploy_vllm is a destructive write (consumes paid compute) with no confirmation, dry-run, or idempotent hint. Agents can accidentally deploy expensive resources without safeguards.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 55 | 2026-07-28+ | v2 |
List stable model aliases configured for this account.
List API keys on the current account (does not return secret values).
List chutes owned by or shared with the current user.
List models available on Chutes.ai via the OpenAI-compatible /v1/models endpoint.
Introspect an OAuth access token issued by Chutes (RFC 7662).
Tool descriptions are terse (avg ~80 chars, below LLM-optimized baseline of 50 - 200). Missing WHEN to use each tool and dependency hints. E.g., 'List models available on Chutes.ai' does not explain when to call this vs chutes_list_chutes or chutes_list_aliases.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). LLMs cannot distinguish safe reads from destructive writes without explicit hints, risking misuse.