The Model Context Protocol (MCP) has changed how we connect LLMs to local data and services. It provides a standard way to give an AI agent "hands"; the ability to read files, query databases, or trigger API actions. However, as your local toolbox grows from five utilities to over a hundred, you hit a wall that every developer eventually faces: context exhaustion.

When you load 100+ tools into an MCP server, you aren't just adding functionality. You are cluttering the model's immediate memory with thousands of tokens of definitions, parameters, and descriptions. This leads to slower response times, massive API bills, and a significant drop in reasoning quality.

This guide covers the technical architecture required to scale MCP to a professional level. We will look at why standard scaling advice often fails and how to implement a Retrieval-Augmented Generation (RAG) pattern specifically for your tool definitions.

What are the Limitations of Large MCP Servers?

Standard MCP implementations assume the model can see every available tool at all times. While this works for a small set of "base" tools, it quickly becomes unsustainable. Each tool definition consumes tokens for the name, description, and the JSON schema of its arguments.

1. The Token Tax

A moderately complex tool (e.g., a Jira ticket creator or a database schema analyzer) can easily take up 500 to 1,000 tokens in the system prompt. If you have 100 tools, you are sending 50,000 to 100,000 tokens to the provider before the user even says "Hello." At current pricing for high-end models like GPT-5 or Claude 4.5 Sonnet, a simple greeting could cost anywhere from $0.30 to$ 1.00 per turn.

2. Model Performance Degradation

As the list of tools grows, models suffer from "distraction." LLMs have a finite attention span. When forced to choose from 150 different functions, the probability of selecting the wrong tool or hallucinating parameters increases. The model might overfit its solution to a tool that is only tangentially related because it was prominent in the context window.

3. The "Distributed Monolith" Problem

Common advice for scaling MCP involves splitting tools into multiple MCP servers (or sub-agents). Anthropic suggests this as the way to scale MCPs to lots of tools (link). However, for the developer, this often results in a "distributed monolith" architecture for the MCP tools. You end up managing a complicated web of dependencies where MCP Server A needs data from MCP Server B, and the overhead of managing 20 different MCP processes locally becomes a challenge. In short, it's likely too much overhead for simple projects.

Standard multi-server setups often lead to increased latency and management complexity as the tool count grows.

How to Scale with Tool-RAG

Instead of force-feeding every tool definition to the LLM, we can use a retrieval-based approach. This involves storing your tool definitions in a searchable index and only "injecting" the most relevant ones into the conversation based on the user's current intent.

The Core Workflow

Index: Convert every tool's name and description into a vector embedding.
Retrieve: When a user sends a message, generate an embedding for that query.
Filter: Search your local index for the top tools (usually 3 to 5) that match the query.
Prompt: Send only those specific tool schemas to the LLM.

This keeps the context window lean and the model focused.

Technical Implementation: File-Based Indexing

While many RAG tutorials suggest a dedicated Vector Database (like Pinecone or Weaviate), file-based storage is usually the better option for storing embeddings for MCP tools. Because tool definitions are relatively static and limited in number (even at 500+ tools), a local JSON or Parquet file is faster and easier to manage.

This script handles the hashing logic to detect changes automatically, ensuring you only update embeddings when the tool code actually changes.

import hashlib
import json
import os
import numpy as np

INDEX_FILE = "mcp_tools_index.json"

def calculate_hash(tool: dict) -> str:
    """Create a unique hash for a tool based on its content."""
    content = f"{tool['name']}{tool['description']}{tool.get('parameters', '')}"
    return hashlib.sha256(content.encode()).hexdigest()

def update_tool_index(current_tools: list):
    # Load existing index if it exists
    if os.path.exists(INDEX_FILE):
        with open(INDEX_FILE, "r") as f:
            index_data = json.load(f)
    else:
        index_data = {}

    for tool in current_tools:
        tool_hash = calculate_hash(tool)
        
        # Check if tool is new or has changed
        if tool['name'] not in index_data or index_data[tool['name']]['hash'] != tool_hash:
            print(f"Embedding change detected for: {tool['name']}")
            embedding = get_embedding(f"{tool['name']}: {tool['description']}")
            index_data[tool['name']] = {
                "hash": tool_hash,
                "embedding": embedding,
                "metadata": tool
            }
        
    # Save the updated index back to disk
    with open(INDEX_FILE, "w") as f:
        json.dump(index_data, f)
        
    return index_data

Step 2: The Retrieval Logic

When the user asks a question, we use the local index to find the most relevant tools.

def retrieve_tools(query: str, tool_list: List[Dict], top_k: int = 3):
    query_vector = np.array([get_embedding(query)]).astype('float32')
    
    # Search for the nearest tools using your stored embeddings
    # (Example using a simple cosine similarity or FAISS index)
    distances, indices = index.search(query_vector, top_k)
    
    retrieved_tools = []
    for idx in indices[0]:
        if idx != -1:
            retrieved_tools.append(tool_list[idx])
            
    return retrieved_tools

Tips for Effective Tool Retrieval

Setting up the code is the easy part. Making it work reliably in a production environment requires tuning.

1. Finding the Right Threshold

A common mistake is always returning the top_k results. If a user says "Hello," the embedding search will still return the most "relevant" tools, even if they aren't actually relevant.

Distance Thresholding: When using FAISS IndexFlatL2, lower distances mean higher similarity. You should filter out results where the distance is too high to prevent the injection of irrelevant tools.
Sweet Spot: Experiment with your specific embedding model to find a cutoff where non-tool queries result in an empty tool list.

2. Handling Conversation History

A user's query rarely exists in a vacuum. If a user asks "Show me the logs," and then follows up with "Now filter by errors," the second query doesn't mention "logs."

To handle this, you have two choices:

Query Augmentation: Use the LLM to summarize the conversation into a standalone search query before doing the embedding lookup.
Windowed Retrieval: Embed the last 2-3 turns of the conversation as the search query.

3. Chunking for Long Descriptions

If your MCP tool has a massive description (common in auto-generated API wrappers), the embedding might lose focus. In these cases, focus the embedding on the name and a concise summary field, while keeping the full schema in the metadata to be injected only after retrieval.

Performance and Latency Trade-offs

Scaling to 100+ tools with RAG adds a small latency hit because you must perform an embedding call before the main LLM call.

Metric	Full Context (100 tools)	Tool-RAG (Top 5)
Initial Latency	Low (Single LLM call)	Medium (Embedding + LLM)
Token Cost	Very High ($$$)	Low ($)
Accuracy	Degraded (Distraction)	High (Focused)
Context Left	~20%	~95%

For a software engineer, the trade-off is almost always in favor of Tool-RAG. The extra 200ms spent on an embedding call is negligible compared to the 5-10 seconds saved by reducing the model's processing time for a massive context window.

Best Tools for Implementing Tool-RAG

File-Based Storage with Hashing (Top Recommendation)

For most developers, the best approach is to store embeddings in a local JSON or Parquet file rather than a database. Since tool definitions change only when you update your code, you don't need the overhead of a live DB.

To make this efficient, implement a hashing mechanism:

Generate a SHA-256 or MD5 hash of the tool's name and description. (MD5 is suitable for this since we want uniqueness, not security)
Compare it against a stored hash in your local index.
Only re-embed the tool if the hash has changed.

This ensures your startup time is near-instant and you aren't wasting money on redundant embedding API calls every time the server restarts.

When to use a Vector DB?

Consider a dedicated Vector DB (like PostgreSQL with pgvector, ChromaDB, Qdrant) only if:

Your toolset is highly dynamic (adding/removing tools at runtime, likely not the case most of the time)
You are managing thousands of tools across.
You need to perform complex metadata filtering alongside vector search.

Alternatives

Local SentenceTransformers: Use all-MiniLM-L6-v2 for a lightweight, free, and private solution if you have the compute.
OpenAI/Gemini APIs: Simple and standard, perfect for those who do not want to manage local models.

Handling Advanced Tool Dependencies

One possible overlooked part of the RAG-based approach is that it treats tools as isolated functions. In reality, tools often have dependencies. For example, you cannot update_jira_ticket unless you first search_jira_tickets to find the ID.

If your RAG system only retrieves the "update" tool because the user said "Update ticket #101," the model might lack the "search" tool it needs if it hits an error or needs more context.

Tool Prerequisites & Graph Search

To solve this, we can move from simple vector retrieval to a Prerequisite Graph approach using our local file-based index:

Metadata Tagging: Add a prerequisites list to your tool metadata in the JSON file.
Embedding Enrichment: When you index your tools, include the names of prerequisite tools in the text to be embedded.
Recursive Retrieval:
- Retrieve the top tools via vector search.
- For each retrieved tool, look up its prerequisites in your index_data.
- Inject both the primary tool and its immediate dependencies into the prompt.

# Example Graph Retrieval using the local index
def retrieve_with_dependencies(query: str, index_data: dict, top_k: int = 3):
    # 1. Perform vector search on the embeddings stored in index_data
    # (Assuming vector_search function handles the FAISS logic)
    primary_tool_names = vector_search(query, index_data, k=top_k)
    
    final_toolset = []
    seen_names = set()

    def add_tool_and_deps(tool_name):
        if tool_name not in seen_names and tool_name in index_data:
            seen_names.add(tool_name)
            tool_entry = index_data[tool_name]
            final_toolset.append(tool_entry['metadata'])
            
            # Recursively add prerequisites
            for pre_req in tool_entry['metadata'].get('prerequisites', []):
                add_tool_and_deps(pre_req)

    for name in primary_tool_names:
        add_tool_and_deps(name)
                
    return final_toolset

By using this "Retrieve + Expand" strategy, you ensure the LLM always has the full chain of command required to complete a complex task, without ever needing to load the other 95% of your tool library.

Conclusion

Scaling the Model Context Protocol to a performant level requires moving away from the "send everything" mentality. By treating tool definitions as a searchable index rather than a static prompt, you can build agents that are faster, cheaper, and more accurate.

Tool-RAG eliminates the need to split your MCP setup into a dozen fragile sub-servers. It allows you to maintain a single, powerful repository of capabilities that are only called upon when truly needed. This architecture ensures your system remains maintainable as your tool library grows from a handful to hundreds.

How to Scale MCP to 100+ Tools