Power Rubber Duck MCP connector for Copilot Studio
April 20, 2026
High-stakes decisions usually break for the same reason: we only look from one angle.
Power Rubber Duck is a Power Platform custom connector that exposes a full MCP server to Copilot Studio. It gives your agent four analysis tools and a shared resource library, so you can challenge assumptions, compare tradeoffs, and check for reasoning blind spots before you commit.
Full source: GitHub repository
What this connector adds
You get two capabilities in one connector:
- Tool-driven analysis for dynamic decision support
- Resource-driven grounding for consistent frameworks and checklists
That combination matters. Tools help your agent reason in the moment. Resources keep the reasoning anchored to reusable standards.
MCP tools
| Tool | Description | Typical use |
|---|---|---|
get_second_opinion |
Gets an alternate perspective from a secondary Foundry model | Validate strategy, challenge assumptions, and reduce tunnel vision |
analyze_risk |
Produces structured risk analysis with mitigation ideas | Evaluate downside before launching initiatives |
identify_cognitive_biases |
Detects common bias patterns in your reasoning | Catch confirmation bias, anchoring, or overconfidence |
comparative_analysis |
Compares multiple options against criteria and tradeoffs | Build vs buy vs partner, platform choices, process changes |
MCP resources
The connector exposes 10 resources that Copilot Studio can read with resources/list and resources/read:
- 3 decision frameworks: investment, operational change, and strategic planning
- 2 knowledge assets: benchmarks and case studies
- 3 reasoning guides: bias checklist, decision process, and critical questions
- 2 best-practice guides: implementation and organizational decision-making
These resources make outcomes more consistent across prompts and users because the agent can pull from the same decision playbook every time.
How it works in Copilot Studio
User asks a decision question
-> Copilot Studio reads a decision framework resource
-> Copilot Studio calls get_second_opinion
-> Copilot Studio calls analyze_risk
-> Copilot Studio calls identify_cognitive_biases
-> Copilot Studio synthesizes recommendation + rationale
The MCP endpoint supports:
tools/listtools/callresources/listresources/read
It also includes direct REST operations for each tool and resource action, which is useful when you want to call them from Power Automate or test each operation independently.
Example decision flow
Say your team is deciding whether to enter a new market.
- Read
resource://decision-frameworks/strategic - Call
get_second_opinionwithanalysis_depth: "deep" - Call
analyze_riskfor technical, financial, and organizational risks - Call
identify_cognitive_biaseson your current plan narrative - Call
comparative_analysisacross options: build, partner, acquire - Return a recommendation with risks, mitigations, and monitoring signals
That gives you a recommendation plus an audit trail of why the recommendation was made.
Deploy the connector
Prerequisites
- Power Platform CLI (
pac) - A Copilot Studio environment
- A Foundry-compatible model endpoint (local or cloud)
Validate and create
# Validate connector
ppcv ".\Power Rubber Duck"
# Create connector
pac connector create --api-definition-file ".\Power Rubber Duck\apiDefinition.swagger.json" `
--api-properties-file ".\Power Rubber Duck\apiProperties.json" `
--script-file ".\Power Rubber Duck\script.csx"
Configure model endpoint
In script.csx, set:
private string foundryEndpoint = "http://endpoint";
private string foundryModel = "phi-4";
You can also override these with connection parameters (foundry_endpoint and foundry_model) in the connector configuration.
Observability with Application Insights
The script includes telemetry hooks for MCP requests, tool calls, resource reads, model calls, and exceptions.
Once you set your instrumentation key in script.csx, you can track:
- Which tools are used most
- Which resources are read most
- Error patterns by operation
- Model response characteristics over time
This helps you improve both agent quality and runtime reliability.
Why this pattern is useful
Most agent decisions fail quietly. You get fluent output, but weak reasoning.
Power Rubber Duck pushes your agent to:
- Use multiple perspectives
- Challenge its own assumptions
- Compare options explicitly
- Ground recommendations in shared decision assets
If you care about decision quality, not just answer quality, this is a practical pattern to adopt.
Files in the project
| File | Purpose |
|---|---|
apiDefinition.swagger.json |
OpenAPI definition, MCP endpoint, and direct operations |
apiProperties.json |
Connector metadata and connection parameters |
script.csx |
MCP routing, tool logic, resources, Foundry calls, and telemetry |
readme.md |
Setup guide, examples, and troubleshooting |