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run_demo

Run pre-built demonstration workflows that combine Sensei with external MCPs (Context7, Tavily, Playwright) to test multi-MCP coordination, generate examples, or train teams.

Instructions

Execute a demonstration workflow that showcases multi-MCP orchestration.

Runs a self-contained demo that combines Sensei + external MCPs (Context7, Tavily, Playwright) in realistic workflows. Perfect for:

  • Testing multi-MCP coordination

  • Generating example documentation

  • Demonstrating Sensei capabilities

  • Training and onboarding

Args: demo_type: Type of demo to run Available demos: - "auth-review": Authentication security review (Sensei + Context7 + Tavily + Playwright) - "performance-debug": Performance debugging (Sensei + Playwright + Context7) - "cost-analysis": Cloud cost optimization (Sensei + Tavily) - "api-review": API design review (Sensei + Context7 + Tavily)

custom_params: Optional custom parameters to override defaults
    Example for auth-review: {
        "user_query": "Review FastAPI authentication",
        "app_url": "https://myapp.com/login",
        "framework": "FastAPI"
    }

output_format: Output format ("markdown", "json", "text")

Returns: Comprehensive demo execution report showing: - Workflow steps and MCP coordination - Example findings from multi-MCP synthesis - Expected output structure - How to run the demo yourself

Examples: # Run auth security review demo with defaults run_demo(demo_type="auth-review")

# Run with custom parameters
run_demo(
    demo_type="auth-review",
    custom_params={
        "user_query": "Review OAuth implementation",
        "framework": "Django"
    }
)

# Get JSON output
run_demo(demo_type="performance-debug", output_format="json")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
demo_typeYes
custom_paramsNo
output_formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of disclosing behavior. It states that the tool runs a self-contained demo, combines external MCPs, and returns a comprehensive report including workflow steps and expected output structure. It does not explicitly mention side effects or that no real changes occur, but 'self-contained demo' strongly implies a safe, simulated execution.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Args, Returns, Examples) and uses bullet lists to enumerate options. Every sentence adds value, and the length is appropriate for the tool's complexity. It is front-loaded with the core purpose and then provides detailed parameter guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the nested custom_params object and multiple demo types, the description is complete enough for an agent to select and invoke correctly. It covers all parameters, provides concrete examples, explains the return value, and even tells the user how to run the demo themselves. No critical gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides rich detail beyond the schema: it lists all available demo types, gives an example of custom_params structure, and shows usage examples for output_format. This fully compensates for the 0% schema description coverage, making parameter usage unmistakable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Execute a demonstration workflow') and identifies the specific resource ('showcases multi-MCP orchestration'). It clearly distinguishes itself from sibling tools like list_demos or get_mcp_workflow_template by focusing on actually running a demo rather than listing or providing templates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear use cases ('Testing multi-MCP coordination', 'Generating example documentation', etc.), which helps an agent decide when to invoke it. However, it does not explicitly mention alternatives or when not to use this tool, so it falls short of fully explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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