Unverified50% confidenceTradeoffExact time
When using a large model as the sole central scheduler to coordinate multiple MCP tools, if the number of tool calls is too large, the model is prone to scheduling confusion, resulting in poor stability and weak scalability.
1
Sources
50%
Confidence
Long-term
Relevance
9/24/2026
First Seen
Sources
Related Entities
Related Claims
Unverified中心化多MCP架构在工具数量增多时存在不稳定性、可扩展性差和工具调用混乱的问题76% similarUnverifiedTool parameter descriptions that are too broad cause the model to hallucinate parameters, while descriptions that are too strict limit the tool's applicability and flexibility.73% similarUnverifiedMCP在工具数量增长到数十甚至数百个时，每次对话都需要将所有工具的Schema描述注入上下文，会造成显著的性能瓶颈72% similarVerifiedSmaller models are more prone to embedding collapse than larger models, due to reasons including limited parameter capacity, a tendency toward optimization shortcuts, and amplified anisotropy issues.71% similarUnverifiedIn a multi-model collaborative architecture, errors from different models accumulate at each stage, and the overall system stability depends on the weakest link.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/946867API
curl https://kongchang.com/api/v1/knowledge/claims/946867MCP
get_claim(id=946867)