Available Tools

All Converra MCP tools with examples and common use cases.

Prompts

list_prompts

List all your prompts.

You say: "Show me my prompts"

Response:

Found 3 prompts:
- Customer Support (gpt-4o) - Active
- Sales Assistant (gpt-4o) - Active
- Code Review (claude-3-5-sonnet) - Draft

create_prompt

Create a new prompt. Requires: name, content, llmModel.

You say: "Create a customer support prompt for gpt-4o"

Example with full content:

Create a prompt with:
- name: "Customer Support Agent"
- llmModel: "gpt-4o"
- content: "You are a helpful customer support agent. Be friendly,
  concise, and always try to resolve issues on first contact."
- description: "Main support chatbot"
- tags: ["support", "production"]

Supported models: gpt-4o, gpt-4, gpt-3.5-turbo, claude-3-5-sonnet, claude-3-opus, o1, o3, gemini-1.5-pro

update_prompt

Update an existing prompt.

You say: "Update my support prompt to be more friendly"

Example:

Update prompt abc123:
- content: "You are an exceptionally friendly customer support agent..."

get_prompt_status

Get details about a specific prompt including performance metrics.

You say: "Show details for my support prompt"

Response:

Prompt: Customer Support Agent
Model: gpt-4o
Status: Active
Conversations: 1,247
Last optimized: 3 days ago
Performance: 87% task completion

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Optimization

trigger_optimization

Start an optimization to improve your prompt.

You say: "Optimize my support prompt with 3 variants"

Full example:

Optimize prompt abc123:
- mode: "exploratory"  (or "validation" for statistical rigor)
- variantCount: 3
- intent:
  - targetImprovements: ["clarity", "task completion"]
  - hypothesis: "Adding examples will help users understand better"

What happens:

1. Converra generates variant prompts
2. Simulates conversations with AI personas
3. Evaluates which variant performs best
4. Reports results with improvement percentages

get_optimization_details

Check progress and results of an optimization.

You say: "How's my optimization going?"

Response (in progress):

Optimization abc123
Status: Running (Iteration 2/5)
Progress: Simulating conversations...
Variants: 3 being tested

Response (complete):

Optimization abc123
Status: Complete
Winner: Variant B
Improvement: +23% task completion, +15% clarity
Recommendation: Apply Variant B

list_optimizations

See recent optimization runs.

You say: "Show my recent optimizations"

Response:

Recent optimizations:
1. Customer Support - Completed 2h ago - Variant B won (+23%)
2. Sales Assistant - Running - Iteration 3/5
3. Code Review - Completed yesterday - No clear winner

get_variant_details

Compare variants from an optimization.

You say: "Show me the variants from my last optimization"

Response:

Variant A (Control):
- Task completion: 72%
- Clarity: 68%

Variant B (Winner):
- Task completion: 89% (+17%)
- Clarity: 83% (+15%)
- Key change: Added step-by-step instructions

Variant C:
- Task completion: 75% (+3%)
- Clarity: 71% (+3%)

apply_variant

Deploy a winning variant to your prompt.

You say: "Apply the winning variant"

Response:

Applied Variant B to "Customer Support Agent"
Previous version saved. You can revert anytime.

stop_optimization

Stop a running optimization.

You say: "Stop the current optimization"

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Insights

get_insights

Get performance insights for a prompt based on logged conversations.

You say: "How is my support prompt performing?"

Response:

Insights for Customer Support (last 30 days):
- Task completion: 87%
- Avg sentiment: Positive
- Common topics: order status, refunds, shipping
- Improvement opportunity: Users often confused about return policy

---

Conversations

list_conversations

List logged conversations for a prompt.

You say: "Show recent conversations for my support prompt"

get_conversation

Get details of a specific conversation including insights.

You say: "Show me conversation xyz789"

create_conversation

Log a conversation for analysis.

Example:

Log conversation:
- promptId: "abc123"
- content: "User: I need help with my order\nAI: Happy to help! What's your order number?"
- status: "completed"

---

Personas

list_personas

List simulation personas for testing.

You say: "What personas are available for testing?"

Response:

Available personas:
- Frustrated Customer (impatient, had bad experiences)
- Enterprise Buyer (technical, detail-oriented)
- First-time User (needs guidance, asks basic questions)
- Power User (efficient, knows what they want)

create_persona

Create a custom persona for simulations.

Example:

Create persona:
- name: "Confused Senior"
- description: "An elderly user unfamiliar with technology,
  needs patient explanations, may ask the same thing twice"
- tags: ["senior", "patience-test"]

---

Simulation

simulate_prompt

Test your prompt against personas without optimization.

You say: "Test my support prompt against 5 personas"

Response:

Simulation complete:
- 5 conversations generated
- Avg task completion: 78%
- Issues found: Struggled with technical users
- Recommendation: Add more technical details

analyze_prompt

Get structural analysis and improvement recommendations.

You say: "Analyze my support prompt for weaknesses"

Response:

Analysis of Customer Support:
Strengths:
- Clear role definition
- Good tone instructions

Weaknesses:
- No examples provided
- Missing edge case handling
- Could be more concise

Recommendations:
1. Add 2-3 example interactions
2. Add instructions for handling complaints
3. Remove redundant phrases

run_head_to_head

Compare two prompts directly.

You say: "Compare my old and new support prompts"

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Account

get_account

Get account info and usage.

You say: "What's my Converra usage?"

get_settings

Get optimization settings.

update_settings

Update default settings.

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Webhooks

list_webhooks

List configured webhooks.

create_webhook

Create a webhook for events.

Example:

Create webhook:
- url: "https://myapp.com/converra-webhook"
- events: ["optimization.completed", "prompt.updated"]

delete_webhook

Remove a webhook.

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Common Workflows

New User Setup

1. "Create a prompt for [your use case] using gpt-4o"
2. "Analyze this prompt for weaknesses"
3. "Run an optimization with 3 variants"
4. "Show me the results"
5. "Apply the winner"

Continuous Improvement

1. "How is my support prompt performing?"
2. "What are the common issues?"
3. "Optimize focusing on [specific issue]"
4. "Apply the improvement"

Testing Before Production

1. "Simulate my prompt against 10 personas"
2. "What issues were found?"
3. "Update the prompt to address [issue]"
4. "Simulate again to verify"