MCP Cross-Pollination Server

Overview

This project implements a Model Context Protocol (MCP) server that exposes three specialized tools for a software-engineering cross-pollination workflow:

Abstract → Compare → Implement

The same MCP server can be consumed by different compatible clients. The experiment uses the server from two different contexts to explore how capabilities can be decoupled from the agent or client that consumes them.

The project focuses on the architectural separation between:

• the agent that decides what to do,
• the tools that provide concrete capabilities,
• and the protocol used to expose and discover those capabilities.

Architecture

Client / Agent → MCP Server → Tools

The agent interprets the problem and determines whether a tool is useful. The MCP server exposes the available capabilities, while each tool encapsulates a specific responsibility.

The three tools are:

abstrae_tool

Transforms a concrete software-engineering problem into a more general abstraction by identifying its fundamental principles and constraints.

compara_tool

Uses the resulting abstraction to explore analogous mechanisms or ideas in another domain.

implementa_tool

Transforms the selected analogy into a concrete software-engineering implementation proposal.

The tools can be used as part of a larger reasoning process, but the protocol itself does not prescribe a fixed sequence. The client/model determines when a tool should be invoked.

Why MCP?

The main motivation was to experiment with a standardized interface between an AI application and external capabilities.

Instead of coupling each tool directly to a particular agent framework, the capabilities are exposed through MCP. This allows different compatible clients to discover and consume the same tools without requiring changes to the server implementation.

In this project, the same MCP server is consumed from:

• Claude Desktop
• CrewAI

The server remains unchanged while the consuming context changes.

This illustrates an important architectural property:

The capability interface can be separated from the client or agent that consumes it.

MCP should not be understood as the agent, the LLM, or the workflow itself. It provides a protocol through which an MCP client can discover and interact with capabilities exposed by an MCP server.

Why Separate Tools from the Agent?

The separation provides a clear division of responsibilities.

Agent

The agent interprets the task and determines whether an external capability is necessary.

Tools

Each tool implements a specific capability with a defined interface.

This separation makes it possible to:

• keep individual capabilities focused,
• avoid coupling the tools to a specific agent framework,
• reuse the same capabilities from different clients,
• replace or evolve a tool without redesigning the complete agent,
• make tool invocation observable at the protocol boundary.

A useful mental model is:

The agent decides what capability it needs; the tool defines how that capability is executed.

Why These Three Tools?

The three tools represent different stages of the cross-pollination process.

1. Abstract

A concrete problem often contains implementation details that make analogies difficult to identify. The abstraction stage removes unnecessary details and extracts the underlying mechanism.

2. Compare

The abstraction is then used to explore structurally similar mechanisms in another domain.

The objective is not superficial similarity, but identifying a useful correspondence between principles.

3. Implement

The final stage translates the selected correspondence back into a software-engineering context and produces an implementation proposal.

The three tools therefore represent distinct capabilities rather than three independent agents.

Why One Agent?

The project deliberately uses a single agent.

The three stages do not require independent goals, separate memories, or autonomous decision-making. The main requirement is to provide the agent with several capabilities and allow it to decide when those capabilities are useful.

Introducing multiple agents would add coordination overhead without addressing a requirement of the experiment.

A multi-agent architecture could become appropriate if the problem required, for example:

• independent specialized objectives,
• different access permissions,
• separate evaluation or review responsibilities,
• persistent specialized memory,
• or explicit negotiation between agents.

That was not necessary for this experiment.

Clients and Interoperability

The server was tested from two different contexts:

Claude Desktop

A general-purpose MCP client capable of discovering and invoking the server's tools.

CrewAI

An agent framework consuming the same MCP-exposed capabilities.

The important property is that the tool implementation is not rewritten for each client.

Conceptually:

Claude Desktop → MCP Server → Tools

and

CrewAI → MCP Server → Tools

Both paths use the same capability layer.

Tool Selection

The agent is not required to invoke a tool for every request.

A general question may be answerable directly by the model, while a task that requires the capabilities exposed by the server can trigger one or more tool calls.

This distinction is important because exposing a capability does not mean that it must always be used.

The architecture therefore separates:

Capability availability

from

Capability invocation

The server can expose a tool while the model decides that using it is unnecessary for a particular request.

Failure Handling

This prototype does not implement a dedicated fault-tolerance layer.

If a tool invocation fails, the final behavior depends on how the MCP client and model handle the returned error or unsuccessful execution.

The project does not claim silent recovery or guaranteed fallback behavior.

A production-oriented implementation would add explicit mechanisms such as:

• structured error handling,
• retries where appropriate,
• timeouts,
• logging,
• fallback policies,
• tool-level health monitoring,
• and clear distinction between successful and unsuccessful tool execution.

This is intentionally treated as a limitation of the prototype rather than hidden behind model-generated output.

Evaluation Strategy

The initial validation focuses on the integration and behavior of the tool layer.

Integration validation

Verify that the client can:

1. discover the available tools,
2. understand their interfaces,
3. invoke them with valid arguments,
4. receive their results,
5. and continue the workflow using those results.

Result validation

A stronger evaluation should also assess the quality of the generated research process.

Potential criteria include:

• quality of the abstraction,
• relevance of the cross-domain analogy,
• correctness of the proposed implementation,
• coherence between intermediate stages,
• unsupported claims,
• appropriate tool selection,
• and final result quality.

Counting successful tool calls alone is therefore insufficient to evaluate the quality of the system.

Trade-offs

The single-agent architecture keeps the experiment relatively simple, but it introduces an attribution problem.

If the final result is poor, several different causes are possible:

• the agent selected the wrong tool,
• a tool produced a poor intermediate result,
• the agent interpreted the tool result incorrectly,
• too many or too few tools were invoked,
• or the final synthesis was incorrect.

This makes individual tool performance harder to isolate.

The trade-off was accepted because the primary objective was to investigate capability decoupling and interoperability rather than build a complete evaluation framework.

Observability

A natural next step would be to add observability at the tool and agent levels.

Useful measurements would include:

• tool invocation count,
• invocation order,
• latency per tool,
• input/output sizes,
• token usage,
• errors,
• retries,
• number of reasoning/tool-selection cycles,
• and total execution cost.

This would make it possible to distinguish whether resource consumption comes from a specific tool, repeated iterations, or the agent's final synthesis.

A trace such as the following would be particularly useful:

Request → Tool selection → Tool call → Tool result → Next decision → Tool call → Final synthesis

This would provide a more reliable basis for optimizing the workflow.

Deterministic Logic vs LLM Reasoning

A useful architectural boundary is to keep deterministic operations outside the LLM whenever the behavior can be expressed as a clear, verifiable rule.

Examples of deterministic responsibilities include:

• input validation,
• schema validation,
• calculations,
• database queries,
• data transformations,
• authentication and authorization,
• and explicit business rules.

LLMs are more appropriate for tasks involving:

• semantic interpretation,
• abstraction,
• natural-language generation,
• contextual comparison,
• synthesis,
• and reasoning over ambiguous information.

The boundary is therefore not based on whether a component "learns". During a normal inference call, the model does not modify its weights. The relevant distinction is whether the task has a clear deterministic rule or requires context-sensitive interpretation.

Limitations

This project is an experimental implementation and has several limitations:

• It uses a single cross-pollination workflow.
• It does not provide a formal benchmark across multiple problem sets.
• Tool quality is not evaluated independently with a standardized metric.
• The system does not include comprehensive fault tolerance.
• Token and latency consumption are not instrumented at every stage.
• Results depend on the client, model, prompts, and execution environment.
• MCP client and framework capabilities may evolve over time.

The purpose is to explore architecture and interoperability rather than provide a production-ready agent platform.

Key Takeaways

1. MCP separates capabilities from clients

The same server can expose capabilities to different compatible consumers without rewriting the tools for each framework.

2. Tools and agents have different responsibilities

The agent determines what it needs to accomplish; tools provide concrete capabilities that can be invoked when appropriate.

3. One agent can be sufficient

Multiple tools do not automatically imply a multi-agent architecture. Agent boundaries should be introduced when independent responsibilities or coordination requirements justify them.

4. Tool availability does not imply tool invocation

An agent can have access to a capability and still determine that the capability is unnecessary for a particular request.

5. Observability is essential for production systems

Once the workflow becomes more complex, tool calls, latency, token consumption, errors, and intermediate results should be observable rather than inferred from the final response.

Technology

• Python
• Model Context Protocol (MCP)
• FastMCP
• Claude Desktop
• CrewAI
• LLM-based agents

Project Status

Experimental / research prototype

The implementation is intended to explore MCP-based capability exposure, client interoperability, and agent/tool separation. It is not presented as a production-ready system.