MCP Tools Reference

The FrootAI MCP Server exposes 25 tools that any MCP-compatible client (VS Code Copilot, Claude Desktop, Cursor, etc.) can invoke. Install and start using them immediately:

npx frootai-mcp@latest

:::tip Quick Start Add the MCP server to your VS Code mcp.json or Claude Desktop config, then call any tool by name. See the CLI Commands page for alternative CLI usage. :::

Complete Tool Catalog

Tool	Description	Category
search_knowledge	Search across all 17 FROOT modules	Knowledge
get_module	Get full content of a specific module (F1–T3)	Knowledge
lookup_term	Look up AI/ML term in glossary (200+ terms)	Knowledge
list_modules	List all modules by FROOT layer	Knowledge
semantic_search_plays	Natural language search for solution plays	Plays
get_play_detail	Detailed architecture for a specific play	Plays
list_community_plays	List all solution plays with status	Plays
compare_plays	Side-by-side play comparison	Plays
agent_build	Builder agent — implementation guidance	Agents
agent_review	Reviewer agent — security + quality review	Agents
agent_tune	Tuner agent — production readiness validation	Agents
compare_models	AI model comparison for use case	Models
get_model_catalog	List Azure OpenAI models with pricing	Models
estimate_cost	Calculate monthly Azure costs for a play	Cost
get_azure_pricing	Azure AI pricing for scenarios	Cost
run_evaluation	Check AI quality scores against thresholds	Evaluation
validate_config	Validate TuneKit config files	Validation
generate_architecture_diagram	Mermaid.js architecture diagram	Architecture
get_architecture_pattern	Architecture guidance for scenarios	Architecture
embedding_playground	Compare text similarity	Learning
list_primitives	Browse agents, skills, hooks, plugins, etc.	Primitives
fetch_azure_docs	Latest Azure documentation	Azure
fetch_external_mcp	Search for external MCP servers	MCP
get_froot_overview	Complete FROOT framework overview	Overview
get_github_agentic_os	.github Agentic OS guide	Reference

---

Detailed Tool Reference

1. search_knowledge

Search across all 17 FrootAI modules for a topic. Returns relevant sections matching the query.

Parameters:

Parameter	Type	Required	Default	Description
query	string	✅	—	Natural language search query
max_results	number	❌	5	Maximum matching sections to return

Example call:

{
  "tool": "search_knowledge",
  "arguments": {
    "query": "how to reduce hallucination in RAG",
    "max_results": 3
  }
}

Example response:

{
  "results": [
    {
      "module": "R2 — RAG",
      "section": "Grounding & Citation",
      "content": "Use groundedness checks (score ≥ 4.0)..."
    }
  ]
}

---

2. semantic_search_plays

Describe what you want to build in natural language and get the top matching solution plays ranked by relevance.

Parameters:

Parameter	Type	Required	Default	Description
query	string	✅	—	What you want to build
top_k	number	❌	3	Number of results (max 5)

Example call:

{
  "tool": "semantic_search_plays",
  "arguments": {
    "query": "process invoices and extract line items",
    "top_k": 3
  }
}

Example response:

{
  "matches": [
    { "play": "06-document-intelligence", "confidence": 0.94 },
    { "play": "15-document-processing", "confidence": 0.87 },
    { "play": "01-enterprise-rag", "confidence": 0.62 }
  ]
}

info

For full play details after finding a match, chain with get_play_detail passing the play number. See the fai-manifest.json spec for how plays are wired.

---

3. agent_build

Triggers the Builder agent — returns implementation guidelines based on FrootAI best practices, then suggests review. Part of the Build → Review → Tune chain.

Parameters:

Parameter	Type	Required	Description
task	string	✅	What the user wants to build

Example call:

{
  "tool": "agent_build",
  "arguments": {
    "task": "IT ticket classification API with Azure OpenAI"
  }
}

warning

After building, always follow up with agent_review and then agent_tune to complete the full quality chain. Skipping review or tuning may leave security or production-readiness gaps.

---

4. compare_models

Side-by-side comparison of AI models for a specific use case. Recommends the best model based on your priority.

Parameters:

Parameter	Type	Required	Default	Description
useCase	string	✅	—	What you're building
priority	enum	❌	quality	cost, quality, speed, or context

Example call:

{
  "tool": "compare_models",
  "arguments": {
    "useCase": "RAG chatbot with 50-page documents",
    "priority": "cost"
  }
}

---

5. estimate_cost

Calculate itemized monthly Azure costs for any solution play at dev or production scale.

Parameters:

Parameter	Type	Required	Default	Description
play	string	✅	—	Play number: 01–20
scale	enum	❌	dev	dev or prod

Example call:

{
  "tool": "estimate_cost",
  "arguments": {
    "play": "01",
    "scale": "prod"
  }
}

Example response:

{
  "play": "01-enterprise-rag",
  "scale": "prod",
  "monthly_total": "$1,240",
  "breakdown": {
    "Azure OpenAI (GPT-4o)": "$800",
    "Azure AI Search (S1)": "$250",
    "Azure App Service (P1v3)": "$140",
    "Azure Cosmos DB (Serverless)": "$50"
  }
}

---

Related Pages

• CLI Commands — command-line alternatives to MCP tools
• JSON Schemas — validation schemas for all FrootAI primitives
• fai-manifest.json — the wiring spec that ties plays together