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Dynamic MCP Servers: Install Once, Run Everywhere

Dynamic MCP Servers: Install Once, Run Everywhere

By Stephanie GoodmanDecember 7, 2025

Static MCP configurations don't scale. Dynamic MCP servers solve the N-agents-times-M-tools problem by centralizing discovery, routing, and policy in a single endpoint.

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A publicly traded software company recently needed a full-time engineer whose entire job was updating MCP tool configurations across dozens of agents. Every system change -- a new API version, a credential rotation, a tool deprecation -- required manual propagation across every agent that touched it. This is the static MCP scaling problem, and most teams building with agents are about to hit it.

The Model Context Protocol has crossed the threshold from interesting spec to production dependency. MCP now has over 97 million monthly SDK downloads. Anthropic, OpenAI, Google, and Microsoft all back it. The MCP Registry, launched in September 2025, indexes close to two thousand server entries. In December, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, cementing it as vendor-neutral infrastructure.

But adoption at scale exposes an architectural fault line that the protocol itself doesn't solve: the difference between configuring tools statically and distributing them dynamically. That difference determines whether your tenth agent deployment feels like progress or like maintaining a spreadsheet of connection strings. It is exactly this problem that motivated the AgentPMT team to build DynamicMCP -- a single-endpoint architecture designed to eliminate configuration sprawl before it starts.

The N-times-M Configuration Problem

MCP's architecture is elegant at small scale. A host application (your agent runtime) creates one MCP client for each MCP server it connects to. Each client maintains a dedicated, one-to-one connection with its corresponding server. The host discovers available tools by calling tools/list, and the agent can invoke them with tools/call. Clean, typed, well-specified.

Now multiply it. You have fifteen agents across three teams. Your organization uses forty tools -- internal APIs, SaaS integrations, data connectors, monitoring hooks. In the static model, every agent needs its own configuration block for every tool it might use. That is 15 agents times 40 tools: 600 individual configuration entries, each with its own endpoint URL, credentials, and version pin.

Add a new tool? Touch every agent config. Rotate a credential? Touch every agent config. Deprecate a tool version? You get the picture. One engineering team at an enterprise described the experience as "maintaining a bespoke Terraform module for every conversation thread."

The configuration sprawl is bad enough. The token economics are worse. Each tool definition consumes 400 to 600 tokens when loaded into an agent's context. Connect five MCP servers exposing fifty tools each, and you burn 75,000 to 100,000 tokens before the agent even reads the user's question. That is 80 to 90 percent of input tokens on a typical query, consumed by tool metadata the agent will never use. Research from Jenova AI puts the practical limit for consistent tool-selection accuracy at five to seven active tools. Beyond that, selection errors increase and the model starts doing things like querying Salesforce when you asked about HubSpot.

This is the static MCP scaling problem: linear in configuration effort, exponential in failure modes, and brutal on token budgets.

What Makes an MCP Server "Dynamic"

A static MCP server is a fixed endpoint. You configure your agent to connect to it. The agent calls tools/list at startup, gets back a static catalog of tools, and those tools live in context for the entire session. If the server's capabilities change, the MCP spec supports a notifications/tools/list_changed notification, but the underlying assumption is still one agent talking to one server with a pre-configured connection.

A dynamic MCP server inverts this model. Instead of the agent connecting to many servers and loading all their tools upfront, the agent connects to a single server endpoint that handles discovery, routing, and policy on the backend. Tools are fetched on demand -- when the agent actually needs them, not when it boots up.

The practical difference shows up in three places.

First, discovery becomes a search problem instead of a configuration problem. The agent does not need to know in advance which tools exist. It describes what it needs, and the dynamic server returns the relevant tools from a catalog. Docker's MCP Catalog and Toolkit implements a version of this: agents search a catalog and add servers mid-conversation, without manual pre-configuration. Speakeasy's dynamic toolset approach takes it further with an embeddings-based search step that retrieves only the tools the model intends to use, achieving a 96 percent reduction in input tokens compared to static loading.

Second, routing is centralized instead of distributed. In the static model, every agent runtime manages its own connections. In the dynamic model, the server handles routing to backend tool providers. The agent sees one endpoint. The server decides which backend handles each call. This is the same pattern that made API gateways essential for microservices -- a single point of entry that abstracts the complexity behind it.

Third, the tool catalog is live, not frozen. Tools can be added, updated, versioned, or removed from the catalog without touching any agent configuration. The agent's next search sees the current state of the world. No config files to update, no agent restarts, no deployment pipeline to run for a metadata change.

The Governance Case for Centralization

Configuration management is annoying. Governance gaps are dangerous. The dynamic model solves both, but the governance argument is where it gets serious.

In the static model, tool approval happens per-agent. Team A's agent uses a weather API -- someone approved that. Team B's agent also needs it -- someone approves it again. Or more likely, someone copies Team A's config without going through approval at all. Credentials get shared through Slack DMs. Nobody has a clear picture of which agents can access which tools, because the truth is scattered across dozens of config files in dozens of repos.

Centralize tool distribution through a dynamic server, and you get what you actually need: approve once, enforce everywhere. A tool gets vetted, added to the catalog with its risk tier, budget cap, and access policy. Every agent that discovers it through the server inherits those controls. Revoke the tool from the catalog, and every agent loses access in the same moment -- no need to hunt down configuration files across repositories.

This is the install-once, revoke-once pattern. It only works when a central layer mediates access. Without that layer, "install once" just means "untracked dependency," and revocation becomes an emergency scavenger hunt.

The same logic applies to credential management. In the static model, credentials are typically stored in each agent's config or environment. Each credential copy is an attack surface. In the dynamic model, credentials live in the server layer -- they get injected at execution time and never enter the agent's context. The agent gets results, not secrets. AgentPMT's credential isolation layer enforces this by design: credentials are stored encrypted and injected server-side at execution time, so agents never have direct access to API keys or tokens.

Budget enforcement follows the same centralization pattern. Instead of hoping every agent team independently implements spend limits (they won't), the dynamic server enforces budgets at the routing layer. Cap a tool at fifty cents per call, two hundred dollars per day, and every agent using it through the server is bound by those limits. No per-agent budget configuration. No trusting prompt instructions to prevent overspend. AgentPMT's budget controls make this granular -- administrators set per-tool, per-agent, and per-time-period spending caps that are enforced before any call is routed to the backend provider.

What This Looks Like in Practice

This is the architecture we built with DynamicMCP at AgentPMT. A single MCP server endpoint connects to any MCP-compatible host -- Claude, ChatGPT, Cursor, VS Code, whatever supports the protocol. Getting started is a single install: npm install @agentpmt/mcp-router. The agent connects once. Through that connection, it can search and discover tools from the full AgentPMT marketplace catalog. Tool definitions are fetched on demand -- not loaded at startup -- which means zero context bloat and token costs that scale with actual usage, not catalog size.

On the backend, DynamicMCP handles routing, credential injection, budget enforcement, and audit logging. When an agent calls a tool, the server resolves which backend provider handles it, injects the necessary credentials (which the agent never sees), checks the caller's budget and policy, executes the call, and returns the result. All of this happens in our cloud infrastructure -- the agent's local environment is never exposed. Tool catalogs auto-update every 30 minutes, so when a vendor ships a new version or patches a vulnerability, every connected agent picks up the change without any manual intervention or redeployment.

The design choice that matters most is on-demand loading. Most agent conversations use two to four tools. Loading forty tool definitions into context so the agent can use three of them is like requiring every employee to memorize the entire company directory before making a phone call. DynamicMCP loads tool definitions only when the agent requests them, which matches how agents actually work: they identify what they need, search for it, and use it.

For tool vendors, this means listing once and reaching every agent that connects to the marketplace. For teams running agents, it means one integration instead of maintaining individual connections to every tool provider. DynamicMCP's cross-platform support means this works identically whether your agents run on Claude, ChatGPT, Cursor, or any other MCP-compatible host -- no per-platform configuration required. When a tool needs usage-based payment, x402Direct handles it at the protocol level -- the agent includes payment authorization in the request, and settlement happens without invoices or approval queues.

The Operational Posture Shift

Moving from static to dynamic MCP changes how you think about tool management. In the static world, tools are configuration. In the dynamic world, tools are inventory.

That distinction matters because inventory has properties that configuration does not. Inventory has a catalog with ownership. It has versioning with upgrade paths. It has access control with audit trails. It has usage metrics that tell you what is actually being used versus what is theoretically available.

The operational questions shift accordingly. Instead of "which agents have tool X configured?" you ask "who approved tool X and what is its spend this month?" Instead of "did we update all configs after the API change?" you ask "what is the adoption curve on tool X version 2 versus version 1?" Instead of "which teams have credentials for tool X?" you ask "how many credential-less executions did tool X handle this week?"

This is the same transition that happened when infrastructure moved from manually configured servers to managed services. Nobody misses SSH-ing into boxes to update Apache configs. The dynamic MCP model is that shift applied to the agent tool layer: centralized management, policy as code, and observability by default.

Teams already using MCP gateways are seeing this play out. The MCP Gateway and Registry project provides OAuth-based authentication, dynamic tool discovery, and unified access management. Kong, Lunar.dev, and others are building commercial gateway products. The pattern is converging: a governed entry point between agents and tools, with policy enforcement at the routing layer rather than the client layer.

Implications for Teams Building with Agents

The shift from static to dynamic MCP is not just a technical upgrade -- it reshapes how organizations plan, staff, and govern their agent deployments.

Configuration headcount becomes avoidable. Teams currently dedicating engineering hours to propagating tool configs across agent fleets can redirect that effort toward building differentiated capabilities. The engineer maintaining 600 configuration entries could instead be designing new agent workflows or improving tool selection logic.

Security posture improves by default. When credentials never enter an agent's context and budget enforcement happens at the routing layer, the blast radius of a compromised agent shrinks dramatically. Organizations do not need to bolt on governance after the fact -- it is embedded in the architecture.

Vendor and tool adoption accelerates. When adding a new tool to your agent fleet requires zero per-agent configuration changes, the friction of evaluating and onboarding new capabilities drops to near zero. Teams can experiment with new tools without committing to maintenance overhead.

Regulatory readiness becomes structural. With EU AI Act enforcement approaching in August 2026, centralized audit trails, access controls, and revocation capabilities stop being nice-to-haves. Organizations using dynamic MCP servers already have these capabilities built into their tool distribution layer, while those relying on static configs will need to retrofit governance infrastructure under deadline pressure.

What to Watch

Three developments will shape how fast this transition happens.

The MCP specification itself continues to mature. The November 2025 update added OAuth 2.1 authorization, asynchronous task execution, and elicitation -- all features that make dynamic servers more capable and more secure. Google is pushing for gRPC transport support, which would improve performance for high-throughput tool routing. As the spec stabilizes under the Agentic AI Foundation's governance, expect tighter standards for how dynamic discovery and centralized policy should work.

The tooling ecosystem is racing to solve the scaling problem. Docker's Dynamic MCP, Speakeasy's dynamic toolsets, Redis's tool-retrieval approach -- every serious MCP infrastructure project is building some form of on-demand tool loading. The 96 percent token reduction that Speakeasy demonstrated is not a nice-to-have; it is the difference between an agent that can access a catalog of hundreds of tools and one that chokes on its own metadata.

Enterprise governance requirements are tightening. The EU AI Act enforcement in August 2026 will require mature audit trails and access controls for autonomous systems. Organizations that already centralize tool distribution through dynamic servers will have the logging, policy enforcement, and revocation capabilities regulators expect. Organizations still managing tool access through scattered config files will be retrofitting governance under deadline pressure.

The teams that treat tool distribution as infrastructure -- not as developer convenience -- will be the ones that scale agent deployments without scaling their configuration headcount.

Key Takeaways

  • Static MCP configurations create an N-agents-times-M-tools scaling problem that breaks down in token costs, governance gaps, and configuration maintenance as agent fleets grow.
  • Dynamic MCP servers centralize discovery, routing, and policy behind a single endpoint, enabling install-once and revoke-once patterns that make tool distribution governable.
  • On-demand tool loading is not optional at scale -- research shows static tool loading consumes 80 to 90 percent of input tokens on typical queries, while dynamic approaches reduce that by 90 percent or more.

Ready to eliminate MCP configuration sprawl? Get started with AgentPMT and connect your agents to a dynamic tool marketplace with built-in governance, budget controls, and credential isolation -- all through a single endpoint.

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