How to compute similarity between two MCP states?

 

Step 1: Understand the Components of MCP

 

• System Instructions:

  Define what your model is supposed to be, such as “You are a helpful assistant specialized in finance.”

• User Profile:

  Capture the name, preferences, and goals of the user interacting with the system.

• Document Context:

  Monitor the knowledge base or any recent uploads that the model has access to.

• Active Tasks / Goals:

  Specify objectives and to-dos that the model should focus on.

• Tool Access:

  Determine what external tools the model is permitted to call, such as web, Python, or databases.

• Rules / Constraints:

  Outline specific limitations, such as “never suggest medical diagnoses.”

 

Step 2: Implement MCP in Your System

 

• Choose an AI/LLM that supports MCP, such as GPT, Claude, or Gemini.
• Use a programming framework or platform like AutoGPT, LangChain, or CrewAI to integrate MCP into your application.
• Define the MCP structure and elements based on your use case.

 

Step 3: Structure MCP Payloads

 

Create a JSON structure to represent the MCP context. Here’s an example skeleton:

{
  "system_instructions": "You are a helpful assistant specialized in finance.",
  "user_profile": {
    "name": "John Doe",
    "preferences": {
      "preferencesList": ["short answers", "data-driven insights"]
    },
    "goals": ["improve budgeting skills"]
  },
  "document_context": {
    "knowledge_base": ["financial terms glossary", "recent uploads"],
    "recentuploads": ["financialreport_q1.pdf"]
  },
  "active_tasks": ["learn about stock market basics"],
  "tool_access": {
    "allowed_tools": ["web browser", "calculator"]
  },
  "rules_constraints": {
    "avoid": ["medical diagnoses"]
  }
}

 

Step 4: Compute Similarity Between Two MCP States

 

To determine the similarity between two MCP states, you need to compare the JSON structures.

• Extract the key components from both MCP states.
• Convert each section into a comparable vector or structure. For instance, concatenate key strings or use NLP techniques for sentence meanings.
• Calculate the similarity. You can use cosine similarity, Jaccard index, or other similarity measures that suit your needs.

Example Python code using cosine similarity from the sklearn library:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

Example MCP JSON strings
mcpstate1 = """{
  "system_instructions": "You are a helpful assistant specialized in finance...",
  ...
}"""

mcpstate2 = """{
  "system_instructions": "You are a chatbot designed for assisting users with finance...",
  ...
}"""

vectorizer = TfidfVectorizer()

Convert text into vectors
vectors = vectorizer.fittransform([mcpstate1, mcpstate_2])

Compute cosine similarity
similaritymatrix = cosinesimilarity(vectors)

similarityscore = similaritymatrix[0, 1]

print(f"Similarity score between MCP states: {similarity_score}")

 

Step 5: Evaluate and Adjust MCP Settings

 

• Based on the similarity scores, adjust MCP elements to bring desired predictability and effectiveness.
• If a similarity score is lower than expected, refine the system instructions, document context, or constraints to align more closely with your goals.
• Test the changes in a controlled environment to observe behavior changes.

 

The above steps guide you through understanding, implementing, and analyzing the similarity between two MCP states, facilitating predictable and effective model behavior.