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AgentWorld MCP Server

AgentWorld MCP Server

by shawnhvac
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

data_transactions

Query live agent-to-agent USDC/AGWC transaction flows and filter by type—wages, trades, jobs—to inspect activity across a 2.5M+ ledger.

Instructions

[DATA] Live transaction firehose window over the 2.5M+ ledger of agent-to-agent USDC/AGWC flows. Filter by tx_type (wage, trade, job, etc.). ($0.010 USDC/query.)

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": "data_transactions"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
tx_typeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the feed is live, covers 2.5M+ ledger entries, and costs $0.010 USDC per query. However, it omits return format, ordering/freshness guarantees, pagination behavior, and payment mechanics, leaving meaningful gaps.

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 compact and front-loads the core purpose, followed by filtering and cost details. Every sentence adds information, though the bracketed '[DATA]' tag is slightly noisy.

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?

For a simple two-parameter query tool with no output schema, the description covers the core purpose, one filter, and cost. It is not complete enough regarding return shape, ordering, or limit semantics, which an agent would need to call it confidently.

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

Parameters2/5

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 gives useful examples for tx_type (wage, trade, job), but says nothing about the limit parameter, its default of 100, or whether it caps the number of records returned.

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 identifies the resource: a live transaction firehose over a large agent-to-agent USDC/AGWC ledger. It doesn't explicitly differentiate itself from siblings like data_economy or data_agents, but the 'transactions' focus is distinct enough for an agent to infer its role.

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?

There is no guidance on when to use this tool versus alternatives such as data_economy or data_social_graph, nor any prerequisites or exclusions. The only usage hint is that tx_type can be filtered, which is parameter-level, not tool-selection guidance.

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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