FrootAI API Reference

Technical reference for all public APIs — MCP tools, VS Code commands, config schemas, and Agentic OS files.

1. MCP Server Tools (16 tools)

The FrootAI MCP Server exposes tools over the Model Context Protocol (stdio transport). All tools return JSON-RPC 2.0 responses.

1.1 Static Tools

`get_module`

Retrieve a full knowledge module by ID or name.

Parameter	Type	Required	Description
`module`	`string`	Yes	Module ID (e.g., `"F1"`) or name (e.g., `"GenAI-Foundations"`)

Output: { content: [{ type: "text", text: "<full module content>" }] }

Example:

{
  "name": "get_module",
  "arguments": { "module": "RAG-Architecture" }
}

`list_modules`

List all available knowledge modules with metadata.

Parameter	Type	Required	Description
(none)	—	—	No parameters required

Output: { content: [{ type: "text", text: "<JSON array of modules>" }] }

Each module entry:

{
  "id": "F1",
  "name": "GenAI-Foundations",
  "layer": "Foundations",
  "title": "GenAI Foundations",
  "sizeBytes": 24500
}

`search_knowledge`

Full-text search across all knowledge modules.

Parameter	Type	Required	Description
`query`	`string`	Yes	Search query string
`limit`	`number`	No	Max results (default: 10)

Output: { content: [{ type: "text", text: "<JSON array of matches>" }] }

Each match:

{
  "module": "RAG-Architecture",
  "section": "## Vector Databases",
  "snippet": "...matching text with context...",
  "score": 0.87
}

`lookup_term`

Look up an AI term in the glossary (200+ terms).

Parameter	Type	Required	Description
`term`	`string`	Yes	Term to look up (e.g., `"LoRA"`, `"RAG"`, `"RLHF"`)

Output: { content: [{ type: "text", text: "<definition and context>" }] }

1.2 Live Tools

`fetch_azure_docs`

Retrieve current Azure documentation for a service or topic.

Parameter	Type	Required	Description
`topic`	`string`	Yes	Azure service or topic (e.g., `"AI Search"`, `"Container Apps"`)
`section`	`string`	No	Specific section (e.g., `"pricing"`, `"quickstart"`)

Output: { content: [{ type: "text", text: "<documentation content>" }] }

Note: Requires network connectivity.

`fetch_external_mcp`

Query external MCP server registries and catalogs.

Parameter	Type	Required	Description
`query`	`string`	Yes	What to search for (e.g., `"database tools"`, `"GitHub integration"`)
`registry`	`string`	No	Specific registry (default: all known registries)

Output: { content: [{ type: "text", text: "<JSON array of MCP servers>" }] }

1.3 Chain Tools

`agent_build`

Generate a new agent scaffold based on a scenario description.

Parameter	Type	Required	Description
`scenario`	`string`	Yes	Description of the agent's purpose
`model`	`string`	No	Preferred model (default: `"gpt-4o"`)
`style`	`string`	No	Agent style: `"conservative"`, `"balanced"`, `"creative"` (default: `"balanced"`)

Output: { content: [{ type: "text", text: "<generated agent.md + config>" }] }

`agent_review`

Review an agent's configuration for quality, security, and completeness.

Parameter	Type	Required	Description
`agentMd`	`string`	Yes	Contents of the agent.md file
`config`	`string`	No	Contents of agents.json config
`focus`	`string`	No	Review focus: `"security"`, `"completeness"`, `"performance"`, `"all"` (default: `"all"`)

Output: { content: [{ type: "text", text: "<review findings and recommendations>" }] }

`agent_tune`

Optimize agent parameters based on evaluation results.

Parameter	Type	Required	Description
`config`	`string`	Yes	Current agents.json content
`evalResults`	`string`	No	Evaluation results JSON
`goal`	`string`	No	Optimization goal: `"accuracy"`, `"latency"`, `"cost"`, `"balanced"` (default: `"balanced"`)

Output: { content: [{ type: "text", text: "<optimized config + explanation>" }] }

1.4 AI Ecosystem Tools

`get_architecture_pattern`

Get architecture patterns for a given scenario.

Parameter	Type	Required	Description
`scenario`	`string`	Yes	Description of the architecture need
`constraints`	`string`	No	Constraints (e.g., `"low-latency"`, `"multi-region"`)

Output: { content: [{ type: "text", text: "<pattern description with diagram>" }] }

`get_froot_overview`

Get an overview of the FrootAI platform and its components.

Parameter	Type	Required	Description
(none)	—	—	No parameters required

Output: { content: [{ type: "text", text: "<platform overview>" }] }

`get_github_agentic_os`

Explain the .github Agentic OS — files, primitives, and composition.

Parameter	Type	Required	Description
`detail`	`string`	No	Detail level: `"summary"`, `"full"` (default: `"summary"`)

Output: { content: [{ type: "text", text: "<Agentic OS documentation>" }] }

`list_community_plays`

Browse community-contributed solution plays.

Parameter	Type	Required	Description
`category`	`string`	No	Filter by category (e.g., `"IT"`, `"Security"`, `"DevOps"`)

Output: { content: [{ type: "text", text: "<JSON array of plays>" }] }

`get_ai_model_guidance`

Get model selection guidance and comparison data.

Parameter	Type	Required	Description
`models`	`string`	No	Models to compare (comma-separated, e.g., `"gpt-4o,claude-3.5-sonnet"`)
`useCase`	`string`	No	Use case for recommendation (e.g., `"code generation"`, `"summarization"`)

Output: { content: [{ type: "text", text: "<model comparison and recommendation>" }] }

2. VS Code Extension Commands (13 commands)

All commands are prefixed with frootai. and appear in the Command Palette as FROOT:.

Command ID	Palette Label	Description
`frootai.browseModules`	FROOT: Browse Modules	Open the knowledge hub module browser
`frootai.searchKnowledge`	FROOT: Search Knowledge	Full-text search across all modules
`frootai.lookupTerm`	FROOT: Lookup Term	Look up an AI term in the glossary
`frootai.initDevKit`	FROOT: Init DevKit	Scaffold .github Agentic OS into the workspace
`frootai.initTuneKit`	FROOT: Init TuneKit	Add config and evaluation files
`frootai.showPlays`	FROOT: Show Solution Plays	Browse all 20 solution plays
`frootai.openPlay`	FROOT: Open Play	Open a specific play's folder
`frootai.deploy`	FROOT: Deploy Solution	Package and deploy the current play
`frootai.showMcpTools`	FROOT: Show MCP Tools	View MCP tool documentation
`frootai.readUserGuide`	FROOT: Read User Guide	Open the user guide
`frootai.showArchitecture`	FROOT: Show Architecture	Display system architecture
`frootai.showChangelog`	FROOT: Show Changelog	View version history
`frootai.checkUpdates`	FROOT: Check Updates	Check for new versions

3. Config File Schemas

3.1 `openai.json`

{
  "_comment": "Azure OpenAI deployment configuration",
  "deployment_name": "gpt-4o",
  "api_version": "2024-08-01-preview",
  "temperature": 0.7,
  "max_tokens": 4096,
  "top_p": 0.95,
  "frequency_penalty": 0,
  "presence_penalty": 0
}

Field	Type	Description
`deployment_name`	`string`	Azure OpenAI deployment name
`api_version`	`string`	API version string
`temperature`	`number`	Sampling temperature (0–2)
`max_tokens`	`number`	Maximum response tokens
`top_p`	`number`	Nucleus sampling threshold
`frequency_penalty`	`number`	Repetition penalty (-2 to 2)
`presence_penalty`	`number`	Topic presence penalty (-2 to 2)

3.2 `guardrails.json`

{
  "_comment": "Content safety and guardrail configuration",
  "max_input_tokens": 8192,
  "max_output_tokens": 4096,
  "blocked_topics": ["violence", "self-harm", "illegal-activity"],
  "content_filter_level": "medium",
  "rate_limit_rpm": 60,
  "require_grounding": true,
  "citation_required": true
}

Field	Type	Description
`max_input_tokens`	`number`	Maximum tokens in user input
`max_output_tokens`	`number`	Maximum tokens in response
`blocked_topics`	`string[]`	Topics to filter out
`content_filter_level`	`string`	Filter strictness: `"low"`, `"medium"`, `"high"`
`rate_limit_rpm`	`number`	Requests per minute limit
`require_grounding`	`boolean`	Require grounded (cited) responses
`citation_required`	`boolean`	Include source citations

3.3 `agents.json`

{
  "_comment": "Agent routing and model configuration",
  "default_model": "gpt-4o",
  "fallback_model": "gpt-4o-mini",
  "routing": {
    "complex_reasoning": "gpt-4o",
    "simple_qa": "gpt-4o-mini",
    "code_generation": "gpt-4o"
  },
  "temperature_overrides": {
    "code_generation": 0.2,
    "creative_writing": 0.9
  },
  "max_retries": 3,
  "timeout_ms": 30000
}

3.4 `model-comparison.json`

{
  "_comment": "Model comparison data for selection guidance",
  "models": [
    {
      "name": "gpt-4o",
      "provider": "Azure OpenAI",
      "context_window": 128000,
      "cost_per_1k_input": 0.005,
      "cost_per_1k_output": 0.015,
      "latency_p50_ms": 800,
      "strengths": ["reasoning", "code", "multimodal"]
    }
  ]
}

3.5 `search.json`

{
  "_comment": "Search configuration for RAG pipelines",
  "method": "hybrid",
  "top_k": 5,
  "min_score": 0.7,
  "reranker": true,
  "embedding_model": "text-embedding-3-large",
  "dimensions": 3072
}

3.6 `chunking.json`

{
  "_comment": "Document chunking strategy",
  "strategy": "semantic",
  "chunk_size_tokens": 512,
  "overlap_tokens": 64,
  "separators": ["\n## ", "\n### ", "\n\n", "\n"],
  "preserve_markdown_structure": true
}

4. Plugin Manifest (`plugin.json`)

The root plugin.json describes the FrootAI plugin for marketplace and registry listing.

{
  "name": "frootai",
  "version": "2.2.0",
  "description": "BIY AI Kit — From the Roots to the Fruits",
  "author": "Pavleen Bali",
  "license": "MIT",
  "repository": "https://github.com/gitpavleenbali/frootai",
  "components": {
    "mcp-server": { "version": "2.2.0", "tools": 16 },
    "vscode-extension": { "version": "0.9.2", "commands": 13 },
    "website": { "pages": 13 },
    "knowledge-modules": { "count": 18 },
    "solution-plays": { "count": 20 }
  },
  "keywords": ["ai", "mcp", "agents", "azure", "rag", "froot"],
  "categories": ["AI", "Developer Tools", "Azure"]
}

5. .github Agentic OS File Reference (19 files)

The Agentic OS is a scaffolded set of files that make any project agent-ready. Organized in 4 layers:

Layer 1: Agent Identity

File	Purpose
`.github/agent.md`	Primary agent behavior rules — scope, constraints, tools, error handling
`.github/copilot-instructions.md`	GitHub Copilot context — project structure, conventions, key files

Layer 2: Prompt Library

File	Purpose
`.github/prompts/init.prompt.md`	Bootstrap the agent with project context
`.github/prompts/review.prompt.md`	Code review prompt template
`.github/prompts/deploy.prompt.md`	Deployment preparation prompt
`.github/prompts/debug.prompt.md`	Debugging and troubleshooting prompt
`.github/prompts/test.prompt.md`	Test generation prompt
`.github/prompts/refactor.prompt.md`	Code refactoring prompt

Layer 3: CI/CD Automation

File	Purpose
`.github/workflows/validate.yml`	Validate play structure, file sizes, JSON formatting
`.github/workflows/build.yml`	Build and test pipeline
`.github/workflows/deploy.yml`	Deployment workflow
`.github/workflows/evaluate.yml`	Run evaluation scripts and report scores

Layer 4: Collaboration Templates

File	Purpose
`.github/ISSUE_TEMPLATE/bug.yml`	Structured bug report template
`.github/ISSUE_TEMPLATE/feature.yml`	Feature request template
`.github/ISSUE_TEMPLATE/play-request.yml`	New solution play request
`.github/pull_request_template.md`	PR description template with checklist
`.github/CODEOWNERS`	Review assignment rules
`.github/FUNDING.yml`	Sponsorship information
`.github/dependabot.yml`	Dependency update configuration

Composition

These files compose through 7 primitives:

Agent Rules → agent.md
Context → copilot-instructions.md
Prompts → prompts/*.prompt.md
Workflows → workflows/*.yml
Templates → ISSUE_TEMPLATE/, pull_request_template.md
Config → config/*.json
Evaluation → evaluation/

Each primitive is independent but they strengthen each other. An agent with rules + context + prompts is significantly more capable than rules alone.

Scripts & Automation

deploy-play.sh / deploy-play.ps1

Deploy any solution play end-to-end (infra + config + eval).

Usage:

./scripts/deploy-play.sh <play-number> [--resource-group <rg>] [--skip-eval]

Parameter	Description	Required
`play-number`	Play number (01-20)	Yes
`--resource-group`	Azure resource group name	No (uses default)
`--skip-eval`	Skip evaluation step	No

rebuild-knowledge.sh / rebuild-knowledge.ps1

Rebuild knowledge.json from docs/ and sync to VS Code extension.

Usage:

./scripts/rebuild-knowledge.sh [--publish]

export-skills.sh / export-skills.ps1

Export FROOT modules as .github/skills/ folders for GitHub Copilot.

Usage:

./scripts/export-skills.sh <module-id>   # e.g., F1, R2, O3
./scripts/export-skills.sh --all          # export all 16 modules

auto-update.js (MCP Server)

Knowledge auto-refresh module. Checks if knowledge.json is older than 7 days and fetches latest from GitHub.

import { getLatestKnowledge } from './auto-update.js';
const knowledge = await getLatestKnowledge();

Azure Integration

azure.yaml

Configuration for azd up deployment. Place in the root of any solution play.

registry-entry.json

MCP ecosystem registry entry for FrootAI. Located at mcp-server/registry-entry.json.

agent-card.json

A2A (Agent-to-Agent) protocol card. Located at mcp-server/agent-card.json.

Next: Admin Guide · User Guide · Architecture Overview

1. MCP Server Tools (16 tools)​

1.1 Static Tools​

get_module​

list_modules​

search_knowledge​

lookup_term​

1.2 Live Tools​

fetch_azure_docs​

fetch_external_mcp​

1.3 Chain Tools​

agent_build​

agent_review​

agent_tune​

1.4 AI Ecosystem Tools​

get_architecture_pattern​

get_froot_overview​

get_github_agentic_os​

list_community_plays​

get_ai_model_guidance​

2. VS Code Extension Commands (13 commands)​

3. Config File Schemas​

3.1 openai.json​

3.2 guardrails.json​

3.3 agents.json​

3.4 model-comparison.json​

3.5 search.json​

3.6 chunking.json​

4. Plugin Manifest (plugin.json)​

5. .github Agentic OS File Reference (19 files)​

Layer 1: Agent Identity​

Layer 2: Prompt Library​

Layer 3: CI/CD Automation​

Layer 4: Collaboration Templates​

Composition​

Scripts & Automation​

deploy-play.sh / deploy-play.ps1​

rebuild-knowledge.sh / rebuild-knowledge.ps1​

export-skills.sh / export-skills.ps1​

auto-update.js (MCP Server)​

Azure Integration​

azure.yaml​

registry-entry.json​

agent-card.json​

1. MCP Server Tools (16 tools)

1.1 Static Tools

`get_module`

`list_modules`

`search_knowledge`

`lookup_term`

1.2 Live Tools

`fetch_azure_docs`

`fetch_external_mcp`

1.3 Chain Tools

`agent_build`

`agent_review`

`agent_tune`

1.4 AI Ecosystem Tools

`get_architecture_pattern`

`get_froot_overview`

`get_github_agentic_os`

`list_community_plays`

`get_ai_model_guidance`

2. VS Code Extension Commands (13 commands)

3. Config File Schemas

3.1 `openai.json`

3.2 `guardrails.json`

3.3 `agents.json`

3.4 `model-comparison.json`

3.5 `search.json`

3.6 `chunking.json`

4. Plugin Manifest (`plugin.json`)

5. .github Agentic OS File Reference (19 files)

Layer 1: Agent Identity

Layer 2: Prompt Library

Layer 3: CI/CD Automation

Layer 4: Collaboration Templates

Composition

Scripts & Automation

deploy-play.sh / deploy-play.ps1

rebuild-knowledge.sh / rebuild-knowledge.ps1

export-skills.sh / export-skills.ps1

auto-update.js (MCP Server)

Azure Integration

azure.yaml

registry-entry.json

agent-card.json