MCP Prompts

Prompts are reusable query templates that help users effectively search and retrieve vCons. They provide guided workflows for common search patterns.

Overview

The vCon MCP Server provides 9 prompt templates:

find_by_exact_tags - Exact tag matching
find_by_semantic_search - AI-powered meaning search
find_by_keywords - Keyword/phrase search
find_recent_by_topic - Recent conversations by topic
find_by_customer - Search by party/customer
discover_available_tags - Explore available tags
complex_search - Multi-criteria searches
find_similar_conversations - Find similar vCons
help_me_search - Query strategy guidance

Prompt Definitions

find_by_exact_tags

Find vCons using exact tag matches for precise queries.

Arguments:

tag_criteria (required): Natural language description of tags to match
- Examples: "angry customers", "high priority sales", "department support"
date_range (optional): Date range description
- Examples: "from June", "last week", "Q1 2024"

Best For:

Queries with known categories
Filtering by metadata
Precise department/priority/status matching

Example Usage:

Query: "Find all angry customers from June"
tag_criteria: "angry customers"
date_range: "from June"

→ Guides to: search_by_tags with {sentiment: "angry"}

Strategy:

Parse tag criteria to identify key-value pairs
Convert date range to ISO 8601
Use search_by_tags tool
Handle exact matching (no fuzzy logic)

find_by_semantic_search

Find vCons using AI-powered semantic search to understand meaning and intent.

Arguments:

search_description (required): Natural language description
- Examples: "angry customers", "billing disputes", "positive feedback"
date_range (optional): Date range description

Best For:

Concept-based queries
Finding similar meanings
Natural language questions
Fuzzy matching needs

Example Usage:

Query: "Find frustrated customers from last month"
search_description: "frustrated customers"
date_range: "last month"

→ Guides to: search_vcons_semantic
→ Matches: angry, upset, dissatisfied, complaining (similar concepts)

Strategy:

Understand intent (AI embeddings)
Find related concepts and synonyms
Use search_vcons_semantic tool
Adjust similarity threshold as needed

find_by_keywords

Find vCons containing specific keywords or phrases.

Arguments:

keywords (required): Keywords or phrases to search for
- Examples: "refund", "billing issue", "technical support"
filters (optional): Additional filters (tags, dates, parties)

Best For:

Exact word matching
Specific terminology
Phrase searches
ID or code lookups

Example Usage:

Query: "Find conversations mentioning 'refund' or 'billing issue'"
keywords: "refund OR billing issue"
filters: "department: support"

→ Guides to: search_vcons_content

What Gets Searched:

✅ Subject lines
✅ Dialog bodies
✅ Analysis bodies
✅ Party information
❌ Attachments (not indexed)

Strategy:

Extract specific keywords
Parse additional filters
Use search_vcons_content tool
Review relevance scores and snippets

find_recent_by_topic

Find recent vCons filtered by topic or category.

Arguments:

topic (required): Topic or category
- Examples: "support", "sales", "billing"
timeframe (optional): Recency timeframe
- Examples: "today", "this week", "last 7 days", "this month"
- Default: "recent" (last 30 days)

Best For:

Dashboard views
Recent activity monitoring
Time-sensitive queries
Category browsing

Example Usage:

Query: "Show me recent support calls"
topic: "support"
timeframe: "this week"

→ Combines date filtering with topic search

Strategy:

Convert timeframe to date range
Determine if topic is tag or concept
Choose appropriate search tool
Apply date filtering

find_by_customer

Find all vCons involving a specific customer, party, or participant.

Arguments:

party_identifier (required): Customer/party identifier
- Email: [email protected]
- Phone: +1-555-1234
- Name: John Smith
date_range (optional): Date range to filter

Best For:

Customer history lookup
Party-specific searches
Contact tracing
Relationship mapping

Example Usage:

Query: "Find all conversations with [email protected]"
party_identifier: "[email protected]"

→ Guides to: search_vcons with party_email filter

Search Behavior:

party_name: Case-insensitive partial match
party_email: Exact match
party_tel: Exact match

Strategy:

Identify party type (email/phone/name)
Use search_vcons with appropriate filter
Show all parties in each conversation
Suggest follow-up actions

discover_available_tags

Discover what tags are available in the system for filtering.

Arguments:

tag_category (optional): Focus on specific tag category
- Examples: "department", "priority", "sentiment"

Best For:

Exploring data
Understanding tag schema
Planning searches
Documentation

Example Usage:

Query: "What tags are available for filtering?"
→ Uses: get_unique_tags with include_counts

Response shows:
{
  "department": ["sales", "support", "billing"],
  "priority": ["high", "medium", "low"],
  "sentiment": ["positive", "neutral", "negative"]
}

Strategy:

Use get_unique_tags tool
Organize by category
Show usage counts
Suggest example searches

complex_search

Perform complex searches combining multiple criteria: tags, keywords, dates, and semantic meaning.

Arguments:

search_criteria (required): Complete search description
- Example: "high-priority sales calls from Q1 mentioning pricing"

Best For:

Multi-dimensional queries
Business intelligence
Complex filtering
Advanced searches

Example Usage:

Query: "Find high-priority sales calls from Q1 where customer mentioned pricing"
search_criteria: "high-priority sales calls from Q1 mentioning pricing"

→ Parses into:
  - Tags: {priority: "high", department: "sales"}
  - Date: Q1 2024 (Jan 1 - Mar 31)
  - Content: "pricing"
→ Uses: search_vcons_hybrid or search_vcons_content

Strategy:

Parse criteria into components (tags, dates, keywords, concepts)
Choose best search strategy
Build comprehensive query
Refine results as needed

find_similar_conversations

Find conversations similar to a specific vCon or topic.

Arguments:

reference (required): vCon UUID or topic description
limit (optional): Number of results (default: 10)

Best For:

Similar issue lookup
Pattern discovery
Related conversation finding
Contextual search

Example Usage:

Query: "Find conversations similar to this one"
reference: "123e4567-e89b-12d3-a456-426614174000"
limit: "10"

→ Uses semantic similarity to find related vCons

Strategy:

Determine if reference is UUID or description
Extract or generate embedding
Use search_vcons_semantic with similarity threshold
Adjust threshold for precision vs recall

help_me_search

Get guidance on the best way to search for vCons based on your needs.

Arguments:

what_you_want (required): Description of what you're trying to find

Best For:

New users
Complex queries
Strategy planning
Tool selection

Example Usage:

Query: "I want to find frustrated customers who complained about billing"
what_you_want: "frustrated customers who complained about billing"

→ Analyzes query type
→ Recommends: search_vcons_semantic or search_vcons_hybrid
→ Provides step-by-step guidance

Decision Tree:

Query Type

Recommended Tool

Exact tag values

search_by_tags

Specific keywords

search_vcons_content

Natural language

search_vcons_semantic

Mixed criteria

search_vcons_hybrid

Party information

search_vcons (party filters)

Date ranges

Any tool + date filters

Using Prompts

Claude Desktop

Prompts appear in the prompt selector:

// User selects prompt: "find_by_exact_tags"
// Fills in arguments:
//   tag_criteria: "angry customers"
//   date_range: "June 2024"

// Claude receives guidance and executes appropriate tools

Custom MCP Client

import { Client } from '@modelcontextprotocol/sdk/client/index.js';

const client = new Client({
  name: 'my-client',
  version: '1.0.0'
});

// List available prompts
const prompts = await client.listPrompts();

// Get a specific prompt
const prompt = await client.getPrompt({
  name: 'find_by_exact_tags',
  arguments: {
    tag_criteria: 'angry customers',
    date_range: 'June 2024'
  }
});

// The prompt.messages array contains guidance

Prompt Strategy Guide

When to Use Each Prompt

📋 Known categories/tags → find_by_exact_tags
🔍 Specific words → find_by_keywords
🤖 Concepts/meaning → find_by_semantic_search
📅 Recent + topic → find_recent_by_topic
👤 Specific person → find_by_customer
🏷️  Explore tags → discover_available_tags
🔧 Multiple criteria → complex_search
🔗 Similar content → find_similar_conversations
❓ Not sure → help_me_search

Query Analysis Flow

1. Is it a simple query?
   → Yes: Use specific prompt (tags, keywords, party)
   → No: Continue

2. Does it involve multiple criteria?
   → Yes: Use complex_search
   → No: Continue

3. Is it concept-based or exact words?
   → Concept: Use find_by_semantic_search
   → Exact: Use find_by_keywords

4. Still unsure?
   → Use help_me_search

Examples by Use Case

Customer Service

// Find recent angry customers
Prompt: find_recent_by_topic
  topic: "angry customers"
  timeframe: "this week"

// Find all conversations with customer
Prompt: find_by_customer
  party_identifier: "[email protected]"

// Find billing complaints
Prompt: find_by_keywords
  keywords: "billing complaint refund"
  filters: "department: support"

Sales

// Find high-value sales calls
Prompt: find_by_exact_tags
  tag_criteria: "high value sales"
  date_range: "this quarter"

// Find pricing discussions
Prompt: find_by_keywords
  keywords: "pricing discount contract"
  filters: "department: sales"

// Find successful sales patterns
Prompt: find_similar_conversations
  reference: "uuid-of-successful-call"
  limit: "20"

Analytics

// Explore available tags
Prompt: discover_available_tags

// Complex multi-criteria search
Prompt: complex_search
  search_criteria: "high-priority support calls from Q1 with negative sentiment"

// Find similar issues
Prompt: find_similar_conversations
  reference: "customer complaint about delays"
  limit: "50"

Best Practices

1. Start Simple

Begin with specific prompts before moving to complex searches.

2. Discover First

Use discover_available_tags to understand your data before searching.

3. Refine Iteratively

Start broad, then narrow based on results.

4. Combine Filters

Use date ranges with every search for better performance.

5. Use Right Tool

Let prompts guide you to the appropriate search tool.

Next Steps

See Tools Reference for tool details
See Resources Reference for URI-based access
See Search Guide for search strategies
See Tag Guide for tag management

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Last updated 4 days ago