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add_wallets_to_group

Organize cryptocurrency wallets by adding them to a specified group for easier management and categorization within the Armor Crypto MCP server.

Instructions

Add wallets to a specified group.

Expects the group name and a list of wallet names, returns a list of AddWalletToGroupResponse.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
add_wallet_to_group_requestsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler function for 'add_wallets_to_group'. Decorated with @mcp.tool(), validates input, calls the Armor client API, and handles errors.
    @mcp.tool()
    async def add_wallets_to_group(add_wallet_to_group_requests: AddWalletToGroupRequestContainer) -> List[AddWalletToGroupResponse]:
        """
        Add wallets to a specified group.
        
        Expects the group name and a list of wallet names, returns a list of AddWalletToGroupResponse.
        """
        if not armor_client:
            return [{"error": "Not logged in"}]
        try:
            result: List[AddWalletToGroupResponse] = await armor_client.add_wallets_to_group(add_wallet_to_group_requests)
            return result
        except Exception as e:
            return [{"error": str(e)}]
  • Client-side implementation that serializes the request and makes a POST API call to the Armor Wallet API endpoint '/wallets/add-wallet-to-group/'.
    async def add_wallets_to_group(self, data: AddWalletToGroupRequestContainer) -> List[AddWalletToGroupResponse]:
        """Add wallets to a specific group."""
        # payload = json.dumps([{"wallet": wallet_name, "group": data.group_name} for wallet_name in data.wallet_names])
        payload = data.model_dump(exclude_none=True)['add_wallet_to_group_requests']
        return await self._api_call("POST", "wallets/add-wallet-to-group/", payload)
  • Pydantic model defining the structure for a single add wallet to group request (group name and wallet name).
    class AddWalletToGroupRequest(BaseModel):
        group: str = Field(description="Name of the group to add wallets to")
        wallet: str = Field(description="Name of the wallet to add to the group")
  • Pydantic container model wrapping a list of AddWalletToGroupRequest for batch operations.
    class AddWalletToGroupRequestContainer(BaseModel):
        add_wallet_to_group_requests: List[AddWalletToGroupRequest]
  • Pydantic model for the response, including wallet_name, group_name, and success/error message.
    class AddWalletToGroupResponse(BaseModel):
        wallet_name: str = Field(description="name of the wallet to add to the group")
        group_name: str = Field(description="name of the group to add the wallet to")
        message: str = Field(description="message of the operation showing if wallet was added to the group")
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the expected input and return type but lacks details on permissions, side effects, error handling, or rate limits, which are crucial for a mutation tool.

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

Conciseness5/5

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

The description is front-loaded with the core purpose in the first sentence, followed by input and output details in the second. It uses minimal words effectively, with no wasted sentences.

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

Completeness3/5

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

Given the tool's mutation nature, no annotations, and an output schema (which covers return values), the description is minimally adequate. However, it lacks critical context like permissions or error cases, making it incomplete for safe agent 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?

Schema description coverage is 0%, so the description must compensate. It mentions 'group name' and 'list of wallet names', which adds some meaning beyond the schema's generic 'add_wallet_to_group_requests'. However, it doesn't fully explain the parameter structure or constraints, leaving gaps.

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 verb 'Add' and the resources 'wallets' and 'group', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'remove_wallets_from_group' or 'create_groups', which would require a 5.

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?

No guidance is provided on when to use this tool versus alternatives such as 'create_groups' or 'remove_wallets_from_group'. The description only states what it does, not the context or prerequisites for its use.

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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