AI Config Tools

You're using a skill that will guide you through adding capabilities to your AI agents through tools (function calling). Your job is to identify what your AI needs to do, create tool definitions, attach them to variations, and verify they work.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Required MCP tools:

• create-ai-tool -- create a new tool definition with a schema

• update-ai-config-variation -- attach tools to an AI Config variation

• get-ai-config -- verify tools are attached to the variation

Optional MCP tools:

• list-ai-tools -- browse existing tools in the project

• get-ai-tool -- inspect a specific tool's schema

Core Principles

• Start with Capabilities: Think about what your AI needs to do before creating tools

• Framework Matters: LangGraph/CrewAI often auto-generate schemas; OpenAI SDK needs manual schemas

• Create Before Attach: Tools must exist before you can attach them to variations

• Verify: The agent fetches the config to confirm attachment

• Complete the Full Workflow: Listing existing tools is a discovery step, not the end goal. After listing, always proceed to create the requested tool, attach it, and verify. Do not stop after exploration.

Workflow

Step 1: Identify Needed Capabilities

What should the AI be able to do?

• Query databases, call APIs, perform calculations, send notifications

• Check what exists in the codebase (API clients, functions)

• Consider framework: LangGraph/LangChain auto-generate schemas; direct SDK needs manual schemas

If the user asks to check existing tools first, or you have no codebase context about what tools exist, follow this exact order:

• list-ai-tools -- explore what exists

• create-ai-tool -- create the new tool (with a key different from existing ones)

• update-ai-config-variation -- attach it

• get-ai-config -- verify

Call list-ai-tools as your first tool call before any creation. Never stop after listing alone -- always proceed through all four steps.

Step 2: Create Tools

Use create-ai-tool with:

• key -- unique identifier for the tool

• description -- clear description (the LLM uses this to decide when to call the tool)

• schema -- raw JSON Schema (do NOT use the OpenAI function calling wrapper):

{ "type": "object", "properties": { "query": {"type": "string", "description": "Search query"}, "limit": {"type": "integer", "default": 10} }, "required": ["query"] }

Step 3: Attach to Variation

Use update-ai-config-variation to attach tools. Pass only the tools field. Do not bundle instructions , messages , model , or parameters into this PATCH unless the user has explicitly asked you to also update those fields. Those fields may have been edited in the LaunchDarkly UI since the variation was created, and including them in a tool-attachment PATCH will silently clobber the UI edits.

{ "projectKey": "my-project", "configKey": "support-chatbot", "variationKey": "default", "tools": [ {"key": "search-knowledge-base", "version": 1} ] }

If you observe a UI-clear bug where attaching tools wipes other fields, do not work around it by re-sending those fields from the previous get-ai-config response — that masks the bug and can resurrect stale values that the user has since edited. Report the bug instead.

Step 4: Verify

• Use get-ai-tool to confirm the tool exists with a valid schema

• Use get-ai-config to confirm the tool is attached to the variation (check tools in the variation's output)

Report results:

• Tool created with valid schema

• Tool attached to variation

• Flag any issues

Per-provider schema at the call site

LaunchDarkly stores the tool schema once — the flat {type, name, description, parameters} shape you passed to create-ai-tool . Your application reads it back via config.model.parameters.tools (completion mode) or agent_config.model.parameters.tools (agent mode), then converts to the shape the provider SDK expects. LaunchDarkly never makes the provider call; your code does. The handlers that implement each tool also stay in application code — LaunchDarkly stores the schema, your application owns the behavior.

Provider / framework Target shape Where it goes on the call

OpenAI Chat Completions (direct SDK) {type: "function", function: {name, description, parameters}}

top-level tools=[...]

Anthropic direct SDK {name, description, input_schema} — rename parameters → input_schema

top-level tools=[...]

Bedrock Converse {toolSpec: {name, description, inputSchema: {json: parameters}}}

inside toolConfig.tools=[...]

Gemini (google-genai ) {function_declarations: [{name, description, parameters}]} (Python) / {functionDeclarations: [...]} (Node) GenerateContentConfig.tools=[...]

OpenAI Responses API LaunchDarkly's flat shape passes through unchanged top-level tools=[...]

LangChain / LangGraph LangChainProvider.createLangChainModel(config) and pass ai_config.tools (or your own StructuredTool list) into bind_tools(...) / create_react_agent(tools=[...])

framework-native; no per-call conversion

Strands Agents LaunchDarkly's flat shape; drop parameters.tools before passing params to the Strands model class (AnthropicModel , OpenAIModel ) — Python @tool -decorated callables stay in code Agent(tools=[...]) constructor; no per-call conversion

Minimal conversion snippets (Python):

ld_tools = (ai_config.model.to_dict().get("parameters") or {}).get("tools", []) or []

OpenAI Chat Completions

openai_tools = [ { "type": "function", "function": { "name": t["name"], "description": t.get("description", ""), "parameters": t.get("parameters", {"type": "object", "properties": {}}), }, } for t in ld_tools ]

Anthropic

anthropic_tools = [ { "name": t["name"], "description": t.get("description", ""), "input_schema": t.get("parameters", {"type": "object", "properties": {}}), } for t in ld_tools ]

Bedrock Converse

bedrock_tool_config = { "tools": [ { "toolSpec": { "name": t["name"], "description": t.get("description", ""), "inputSchema": {"json": t.get("parameters", {"type": "object", "properties": {}})}, } } for t in ld_tools ] }

Gemini

gemini_tools = [ { "function_declarations": [ { "name": t["name"], "description": t.get("description", ""), "parameters": t.get("parameters", {"type": "object", "properties": {}}), } for t in ld_tools ] } ] if ld_tools else []

Agent loop with tool calls

An agent that uses tools runs a short loop: call the provider, dispatch any tool calls, loop again, stop when the provider returns a final answer. Three rules apply regardless of provider:

• Bound the loop. MAX_STEPS = 5 is a safe default. A runaway tool loop is almost always a prompt or schema bug, not a case that needs 50 iterations.

• Track every tool invocation. Call tracker.track_tool_call(tool_name) / tracker.trackToolCall(toolName) for each tool the agent actually executes. This is what the Monitoring tab counts as tool usage.

• Break on the provider's "no more tool calls" signal. The exact signal differs per provider: OpenAI Chat Completions → choice.finish_reason != "tool_calls" ; Anthropic → response.stop_reason != "tool_use" ; Bedrock Converse → response["stopReason"] != "tool_use" ; Gemini → response.function_calls empty; OpenAI Responses API → no function_call items in response.output .

Skeleton (Python, Anthropic — the other providers follow the same shape with their own stop-reason check and tool-result formatting):

messages = [{"role": "user", "content": initial_input}] MAX_STEPS = 5 for _ in range(MAX_STEPS): response = tracker.track_metrics_of( lambda: anthropic_client.messages.create( model=agent.model.name, system=agent.instructions, messages=messages, tools=anthropic_tools, **params, ), anthropic_metrics, ) if response.stop_reason != "tool_use": break

messages.append({"role": "assistant", "content": response.content})

tool_results = []
for block in response.content:
    if block.type != "tool_use":
        continue
    if block.name not in tool_handlers:
        raise ValueError(f"Unknown tool: {block.name}")
    result = tool_handlers[block.name](**block.input)
    tracker.track_tool_call(block.name)
    tool_results.append({
        "type": "tool_result",
        "tool_use_id": block.id,
        "content": result,
    })
messages.append({"role": "user", "content": tool_results})

Per-provider tool-call payload shapes live in the aiconfig-ai-metrics references:

• openai-tracking.md — Chat Completions + Responses API

• anthropic-tracking.md — tool_use blocks and tool_result payloads

• bedrock-tracking.md — toolUse / toolResult Converse format

• gemini-tracking.md — functionCalls / functionResponse parts

• langchain-tracking.md — LangGraph tool loop inherits from create_react_agent

Orchestrator Note

LangGraph, CrewAI, and AutoGen often generate schemas from function definitions. You still need to create tools in LaunchDarkly and attach keys to variations so the SDK knows what's available.

Edge Cases

Situation Action

Tool already exists (409) Use existing or create with different key

Schema invalid Use raw JSON Schema format (type: object, properties, required)

Wrong endpoint assumed The tools use /ai-tools , not /ai-configs/tools

What NOT to Do

• Don't try to attach tools during config creation -- update the variation afterward

• Don't skip clear tool descriptions (LLM needs them to decide when to call)

• Don't forget to verify attachment after updating the variation

• Don't bundle instructions , messages , model , or parameters into the tool-attachment PATCH. Send tools alone unless the user explicitly asked for a multi-field update — bundled PATCHes silently clobber UI edits to the other fields.

Related Skills

• aiconfig-create -- Create config before attaching tools

• aiconfig-variations -- Manage variations with different tool sets