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Algorand MCP

Algorand MCP

by GoPlausible
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

wallet_get_assets

Retrieve all asset holdings for the active Algorand wallet account, supporting mainnet, testnet, and localnet networks with paginated responses.

Instructions

Get all asset holdings for the active wallet account.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "wallet_get_assets"
  }
}

Input Schema

NameRequiredDescriptionDefault
networkNoAlgorand network to use (default: mainnet)
itemsPerPageNoNumber of items per page for paginated responses (default: 10)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.1.1

TDQS

C2.9/5.0
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. It states it's a read operation ('Get'), but doesn't disclose behavioral traits like pagination details (implied by 'itemsPerPage' but not explained), rate limits, authentication requirements, or what 'active wallet account' means in context. This leaves significant gaps for a tool with parameters.

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 a single, efficient sentence with zero waste—it directly states the tool's purpose without fluff. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 (a read operation with parameters, no annotations, and no output schema), the description is incomplete. It lacks details on behavior (e.g., pagination, auth), output format, and differentiation from siblings, making it inadequate for confident tool selection and invocation.

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 100%, so the schema fully documents the parameters ('network' and 'itemsPerPage'). The description adds no additional meaning beyond implying asset retrieval, which the schema already covers through parameter descriptions. Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('Get') and resource ('all asset holdings for the active wallet account'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'api_indexer_lookup_account_assets' or 'api_algod_get_account_asset_info', which appear to offer similar asset-related functionality, preventing a perfect score.

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. With many sibling tools that might retrieve asset information (e.g., 'api_indexer_lookup_account_assets', 'api_algod_get_account_asset_info'), the description lacks context about prerequisites, differences, or specific use cases, leaving the agent to guess.

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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