Figure 1: Abstract visualization of AI agent architecture with interconnected nodes.

The operational paradigm for agentic AI centers on the model's ability to select and utilize external tools—calendar management, email clients, database queries, web search APIs. The objective: create a versatile assistant capable of executing multi-step, real-world tasks. The assumption driving most implementations has been intuitive: more tools equal more capability.

Reality has proven otherwise. As tool inventories grow, Large Language Model (LLM) performance degrades in measurable ways: tool selection accuracy drops, task completion rates fall, operational costs rise. This "tool overload" phenomenon is not a peripheral implementation issue but a fundamental architectural constraint that has stalled the progress of the Model Context Protocol (MCP) ecosystem and agentic AI as a whole.

"My point is that adding more and more and more tools doesn't scale and doesn't work. It only works when you have a few tools. If you have 50 MCP servers enabled, your requests are probably degraded."

— Hacker News Developer

This technical limitation manifests as user frustration. One user described their experience managing multiple AI tools as "chaotic," losing track of "what tool I used for what." This article examines the technical origins of this bottleneck and how Jenova has solved it through a fundamentally different architectural approach—one built on years of dedicated engineering focused specifically on making multi-agent systems use tools reliably and scalably.

2. Technical Origins of Tool Overload

The tool overload problem stems from architectural constraints in contemporary LLMs, specifically their finite context windows and attention mechanisms. While context windows have expanded dramatically—from thousands to millions of tokens—the methods for utilizing that space efficiently have not kept pace.

2.1 Context Window Bloat and Reasoning Degradation

An LLM's ability to use a tool is contingent on that tool's definition being present within its context window—the model's effective short-term memory. This definition includes the tool's name, a natural-language description of its function, and its parameters. As more tools are added, the aggregate size of these definitions, or "token bloat," consumes an increasingly large portion of the context window.

This has two negative consequences. First, it directly increases the computational cost and latency of every request. Research from Meibel AI demonstrates a direct correlation between the number of input tokens and the time it takes to generate an output token. Second, and more critically, it crowds out the space available for the actual task-specific context—the user's instructions, conversation history, and the intermediate "thoughts" the model needs to reason through a problem. As Sean Blanchfield notes in his analysis, "The MCP Tool Trap," this forces a compromise between providing detailed tool descriptions (for accuracy) and leaving sufficient space for the model's reasoning process.

Figure 2: Diagram showing the context window constraint and tool definition bloat.

2.2 Diminished Accuracy and the "Lost in the Middle" Problem

When an LLM is presented with an extensive set of tools, its ability to select the correct one for a given task deteriorates. The model's "attention" mechanism must evaluate a larger set of possibilities, which increases the probability of error. This is compounded by the "lost in the middle" phenomenon, where models show better performance recalling information placed at the beginning or end of the context window, while information in the middle is often ignored or misremembered.

This manifests as:

Incorrect Tool Selection: Choosing a tool that is functionally inappropriate for the task

Parameter Hallucination: Invoking the correct tool but with invented or incorrect parameters

Tool Interference: Descriptions of similarly-named or functionally-overlapping tools can "confuse" the model, leading to unpredictable behavior

Inability to Generalize: Monolithic models struggle to use tools they haven't been explicitly trained on, often hallucinating parameters or using tools for the wrong purpose

The research paper "Less is More: On the Selection of Tools for Large Language Models" demonstrates a clear negative correlation between the number of available tools and the accuracy of tool-calling. A developer on the r/AI_Agents subreddit corroborates this from practical experience: "[once an agent has to access to 5+ tools... the accuracy drops. Chaining multiple tool calls becomes unreliable.]"

3. The Model Context Protocol Ecosystem

The tool overload problem is particularly acute within the MCP ecosystem. MCP is a standardized protocol designed to facilitate seamless interaction between AI agents and thousands of third-party tools (MCP servers). While this standard has catalyzed innovation, it has also become a focal point for the scaling issue.

The very design of MCP, which relies on discoverable, natural-language tool definitions, directly exposes agents to the context window bloat and attention deficit problems. The temptation for users and developers is to enable a large number of MCP servers to maximize an agent's potential capabilities. However, as a Hacker News commenter powerfully stated, this approach is fundamentally flawed:

"MCP does not scale. It cannot scale beyond a certain threshold. It is Impossible to add an unlimited number of tools to your agents context without negatively impacting the capability of your agent. This is a fundamental limitation with the entire concept of MCP... You will see posts like ‘MCP used to be good but now…’ as people experience the effects of having many MCP servers enabled. They interfere with each other."

— Hacker News Developer

Another discussion pointed out that the bottleneck is the models themselves, which "struggle when you give them too many tools to call. They're poor at assessing the correct tool to use when given tools with overlapping functionality or similar function name/args."

Figure 3: Visualization of tool interference and selection errors in traditional architectures.

4. Architectural Solutions: The Industry Response

The industry has converged on two primary approaches to mitigate the tool overload problem, though both have significant limitations when implemented in isolation.

4.1 Server-Side Solutions: Tool Abstraction and Hierarchies

This approach focuses on making the tool servers themselves more intelligent. Instead of exposing a large number of granular, low-level tools to the agent, a server-side solution can abstract them into higher-level, composite capabilities.

Rube, an MCP server built on the Composio integration platform, exemplifies this approach. Rather than exposing individual tools for each of its 500+ supported applications (Gmail, Slack, GitHub, Notion, Linear, etc.), Rube presents a consolidated interface. When a user issues a natural-language command like "send an email to the latest customer" or "create a Linear issue," Rube handles authentication, API routing, and execution server-side.

Another example is the "strata" concept proposed by Klavis AI, which allows for the dynamic creation of tool hierarchies. This enables an agent to first select a broad category (e.g., "file management") and only then be presented with a smaller, more relevant subset of tools.

The limitation: While server-side abstraction reduces the number of tool definitions, it fails to address the core issue of the agent's limited cognitive capacity when dealing with diverse, cross-domain tasks that require coordination across multiple tool categories.

4.2 Client-Side Solutions: Dynamic Tool Selection

This approach implements a pre-processing or routing layer that analyzes the user's intent before engaging the primary LLM, dynamically selecting a small, highly relevant subset of tools from a much larger library.

This aligns with insights from Memgraph, which argues that the key is "feeding LLMs the right context, at the right time, in a structured way," rather than simply building bigger models (Source).

The limitation: Simple routing and filtering, while helpful, still relies on a single monolithic model to handle all tool execution, which creates a fundamental ceiling on both reliability and scalability.

5. Jenova's Solution: A Multi-Agent, Mixture-of-Experts Architecture

The solution to this tooling bottleneck requires a fundamental architectural shift away from the monolithic model. This is the approach pioneered by Jenova, which has been tackling this specific problem since early last year, long before "tooling" became a mainstream concept. Jenova recognized that true scalability and reliability could not be achieved through simple architectural or system innovations alone. Instead, it required years of compounded engineering experience focused obsessively on a single goal: making multi-agent architectures use tools reliably and scalably.

Figure 4: Jenova's multi-agent architecture diagram.

This new paradigm, centered on a proprietary multi-agent, mixture-of-experts (MoE) system, was engineered to address both the reliability and scalability challenges head-on. Here is a technical breakdown of how Jenova's architecture solves the problem:

5.1 Mixture-of-Experts (MoE) Routing

When a complex request is received, the system employs a sophisticated routing layer. This router first classifies the user's intent into a specific domain. For example, some models are highly specialized for information-retrieval domains, excelling at understanding queries and using search-based tools. Others are optimized for action-oriented domains, adept at executing tasks like drafting emails or creating calendar invites. A third category might specialize in analytical domains, handling data processing and logical reasoning. The request is then routed to a specialized agent best equipped for that specific domain, ensuring the most qualified model handles each part of the task.

Jenova's custom AI agents leverage this architecture to provide specialized capabilities across different domains, from research assistants to productivity tools.

5.2 Multi-Model Orchestration

Because models from different labs (like OpenAI, Google, and Anthropic) are trained on different data and architectures, they develop distinct specializations that align with these domains. For instance, a model trained extensively on web data might be superior for the information-retrieval domain, while another model fine-tuned for instruction-following might excel in the action-oriented domain. An optimal multi-agent architecture must have the flexibility to leverage this diversity, using the best model for each specific domain rather than being locked into a single company's ecosystem. Jenova's system intelligently allocates the most appropriate LLM for each job, ensuring peak performance and reliability at every stage of the workflow.

5.3 Contextual Tool Scoping and Just-in-Time Loading

To solve the context window limitation and scalability problem, the architecture employs a "just-in-time" approach to tool loading. Rather than flooding the agent's context with every available tool, the system uses adaptive routing protocols to predict the most probable set of tools needed for the current task graph. Only the schemas for this relevant subset are loaded into the agent's active context, keeping the reasoning process clean and focused. This dramatically reduces token overhead and allows the system to scale to thousands of potential MCP servers and tools without degrading performance.

Figure 5: Jenova's just-in-time tool loading mechanism.

6. Production-Validated Performance

The efficacy of this approach is validated by Jenova's real-world performance metrics. It reports a 97.3% tool-use success rate. Critically, this is not a figure from a controlled benchmark or a fine-tuned lab environment. It is a metric reflecting performance in production, across a diverse and uncontrolled landscape of thousands of users interacting with a multitude of MCP servers and tools.

Achieving this level of reliability is not merely the result of a sophisticated architecture. The hardest part of building a truly scalable agentic system is ensuring that an infinite number of diverse tools work seamlessly with different models from different labs, all of which are trained on different data. This creates an astronomically complex compatibility matrix. Solving this is analogous to building a jet engine: having the blueprint is one thing, but manufacturing a reliable, high-performance engine that works under real-world stress requires years of specialized expertise, iteration, and deep, compounded engineering experience. This production-hardened robustness is what truly separates a theoretical design from a functional, enterprise-grade system.

This breakthrough has been recognized by key figures in the AI community. Darren Shepherd, a prominent thought leader and community builder in the MCP ecosystem, co-founder of Acorn Labs, and creator of the widely-used k3s Kubernetes distribution, observed that Jenova's architecture effectively solves the core tool scalability issue.

Users can experience this reliability firsthand by trying Jenova or exploring the available MCP integrations that power its capabilities.

7. Conclusion: An Architectural Imperative for the Future of Agentic AI

The empirical data and architectural principles lead to an undeniable conclusion: the future of capable, reliable, and scalable AI agents cannot be monolithic. The prevailing single-model paradigm is the direct cause of the tooling bottleneck that currently stalls the progress of the MCP ecosystem and agentic AI as a whole.

While many in the industry attempt to address this from the server side, this approach is fundamentally misguided as it fails to solve the core issue of the agent's limited cognitive capacity. The true solution must be agent-centric. As a McKinsey report on agentic AI notes, scaling requires a new "agentic AI mesh"—a modular and resilient architecture—to manage mounting technical debt and new classes of risk (Source).

As Jenova's success demonstrates, solving this problem is possible, but it requires far more than simply improving the base capabilities of models or adding a light logic layer. It demands a paradigm shift towards sophisticated, agent-centric architectures built on deep, compounded engineering and architectural expertise focused specifically on the unique challenges of agentic systems. The path forward lies not in limiting the number of available tools, but in developing more sophisticated architectures for managing them—architectures that can intelligently and dynamically orchestrate multiple specialized models, each operating with precisely scoped tool access, to navigate a vast ocean of potential capabilities with precision and focus