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ask_orbit

Get answers about Arbitrum Orbit chain deployment, configuration, validators, AnyTrust, custom gas tokens, and governance for blockchain development.

Instructions

Answer questions about Arbitrum Orbit chain deployment, configuration, validators, AnyTrust, custom gas tokens, and governance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesQuestion about Orbit chain deployment or management
question_typeNoType of question for optimized responsegeneral

Implementation Reference

  • The `execute` method is the core handler for the `ask_orbit` tool, matching user questions against a knowledge base and optional RAG retrieval to provide answers about Orbit chain deployment and management.
    def execute(self, **kwargs) -> dict[str, Any]:
        """Answer an Orbit chain question."""
        question = kwargs.get("question", "").strip()
        _question_type = kwargs.get("question_type", "general")  # reserved for future use
    
        if not question:
            return {"error": "Question is required and cannot be empty"}
    
        q_lower = question.lower()
    
        # Match against knowledge base topics
        answer_parts = []
        relevant_topics = []
    
        # Chain config questions
        if any(kw in q_lower for kw in [
            "chain config", "prepare config", "configure chain",
            "chain id", "chain owner", "chainconfig",
        ]):
            relevant_topics.append("chain_config")
    
        # Deployment questions
        if any(kw in q_lower for kw in [
            "deploy", "create rollup", "launch", "setup chain",
            "deployment", "createrollup",
        ]):
            relevant_topics.append("deployment")
    
        # Validator questions
        if any(kw in q_lower for kw in [
            "validator", "batch poster", "sequencer", "assertion",
        ]):
            relevant_topics.append("validators")
    
        # Gas token questions
        if any(kw in q_lower for kw in [
            "gas token", "native token", "custom token", "erc20 gas",
            "custom gas",
        ]):
            relevant_topics.append("gas_tokens")
    
        # AnyTrust questions
        if any(kw in q_lower for kw in [
            "anytrust", "dac", "keyset", "data availability",
            "committee", "any trust",
        ]):
            relevant_topics.append("anytrust")
    
        # Node setup questions
        if any(kw in q_lower for kw in [
            "node", "nitro", "node config", "run node", "start node",
            "devnode", "docker",
        ]):
            relevant_topics.append("node_setup")
    
        # Node troubleshooting questions
        if any(kw in q_lower for kw in [
            "error", "fail", "not working", "troubleshoot",
            "can't start", "won't start", "permission denied", "crash",
        ]) or ("node" in q_lower and any(kw in q_lower for kw in [
            "issue", "problem", "fix",
        ])):
            relevant_topics.append("node_troubleshooting")
    
        # Governance questions
        if any(kw in q_lower for kw in [
            "governance", "upgrade", "executor", "admin", "role",
            "permission",
        ]):
            relevant_topics.append("governance")
    
        # Code standards questions (include when question involves code examples)
        if any(kw in q_lower for kw in [
            "code", "script", "example", "how to",
            "register", "keyset", "deploy",
        ]):
            relevant_topics.append("code_standards")
    
        # Token bridge questions
        if any(kw in q_lower for kw in [
            "token bridge", "bridge", "gateway", "create bridge",
            "createtokenbridge",
        ]):
            relevant_topics.append("token_bridge")
    
        # Build answer from matched topics
        if relevant_topics:
            for topic in dict.fromkeys(relevant_topics):  # preserve order, deduplicate
                if topic in ORBIT_KNOWLEDGE:
                    info = ORBIT_KNOWLEDGE[topic]
                    answer_parts.append(
                        f"## {topic.replace('_', ' ').title()}"
                    )
                    for key, value in info.items():
                        if isinstance(value, list):
                            answer_parts.append(
                                f"**{key.replace('_', ' ').title()}:**"
                            )
                            for item in value:
                                answer_parts.append(f"  - {item}")
                        elif isinstance(value, dict):
                            answer_parts.append(
                                f"**{key.replace('_', ' ').title()}:**"
                            )
                            for sub_key, sub_value in value.items():
                                answer_parts.append(
                                    f"  - **{sub_key}:** {sub_value}"
                                )
                        else:
                            answer_parts.append(
                                f"**{key.replace('_', ' ').title()}:** {value}"
                            )
                    answer_parts.append("")
    
        # Try RAG context if available
        rag_context = ""
        if self.context_tool:
            try:
                ctx_result = self.context_tool.execute(
                    query=question,
                    n_results=3,
                    rerank=True,
                    category_boosts={
                        "orbit_sdk": 1.5,
                        "arbitrum_docs": 1.3,
                        "arbitrum_sdk": 1.0,
                        "stylus": 0.5,
                    },
                )
                if ctx_result.get("contexts"):
                    rag_context = "\n\n".join(
                        c.get("content", "")
                        for c in ctx_result["contexts"][:2]
                    )
            except Exception:
                pass  # RAG is optional
    
        # Build final answer
        if answer_parts:
            answer = "\n".join(answer_parts)
        else:
            answer = self._get_generic_answer(question)
    
        result = {
            "answer": answer,
            "topics": list(dict.fromkeys(relevant_topics)) if relevant_topics else ["general"],
            "references": self._get_references(relevant_topics),
        }
    
        if rag_context:
            result["additional_context"] = rag_context[:1000]
    
        return result
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'answers questions', implying it's a read-only operation, but doesn't cover critical aspects like response format, limitations (e.g., accuracy, depth), rate limits, or authentication needs. For a Q&A tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that lists key topics upfront. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., by grouping topics). Overall, it's appropriately concise for its purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a Q&A tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of answers to expect (e.g., text responses, links, code snippets), potential limitations, or how it integrates with sibling tools. For a tool that likely returns varied outputs, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with both parameters ('question' and 'question_type') well-documented in the schema. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'question_type' affects responses or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: answering questions about Arbitrum Orbit chain topics. It specifies the scope with concrete topics (deployment, configuration, validators, etc.), which is more specific than just restating the name. However, it doesn't explicitly differentiate from sibling tools like 'ask_bridging' or 'ask_stylus', which appear to be similar Q&A tools for different topics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It lists topics but doesn't specify prerequisites, exclusions, or compare to sibling tools like 'ask_bridging' or 'generate_orbit_config'. Without such context, an agent might struggle to choose between this and other tools for Orbit-related queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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