Multi-tenant AI agent runtime with OS-level isolation. Sandboxed execution, encrypted credentials, private memory per tenant — one server, no Docker.
Cloison Runtime exposes 5 tools with significant quality gaps. Tool definitions are duplicated (memory_search and memory_store each appear twice), indicating registration issues. Schemas are present but minimal, parameters lack descriptions (query, maxResults, content, skillId, params all undescribed in schema). Descriptions exist but are generic and do not explain WHEN to use each tool or what downstream actions they enable. The skill_execute tool accepts an untyped 'params' object, violating constrained-input patterns. No error handling guidance, no pagination support, no output schema documentation. This is a D-grade server: definitions are skeletal, not LLM-optimized.
Search your memory for relevant information. Returns matching memories ranked by relevance.
Search your memory for relevant information. Returns matching memories ranked by relevance.
Store information in your memory for future reference. Use this to remember important facts, user preferences, and context that should persist across conversations.
Store information in your memory for future reference. Use this to remember important facts.
Execute an enabled skill. Credentials are injected automatically by the platform.
Duplicate tool registration: memory_search and memory_store each registered twice in the tool list. This indicates a registration or export bug that will confuse LLMs and clients.
Parameter descriptions missing in JSON schemas. 'query', 'maxResults', 'content', 'skillId', and 'params' have no descriptions in the schema properties. LLMs cannot infer parameter meaning from names alone.
skill_execute accepts untyped 'params' object (type: object, no properties defined). This violates constrained-input pattern, LLMs cannot know what fields are valid, leading to hallucinated parameters.
No output schema documentation. Tool descriptions do not specify what fields are returned or their types. LLMs cannot plan downstream tool calls without knowing response structure.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 47 | <=2025-11-25 | v2 |
No error handling guidance. Error responses are generic strings ('Error: query must be a non-empty string'). LLMs receive no actionable recovery path, should suggest alternatives or next steps.