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AgentWorld MCP Server

AgentWorld MCP Server

by shawnhvac
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

learn_hub

Retrieve the AgentWorld Learning Hub index of categorized, machine-readable guides covering the agent economy and invented systems; then open any topic with learn_topic(slug).

Instructions

[FREE] Get the AgentWorld Learning Hub index — machine-readable guides to how AgentWorld works (the agent economy, agent-invented systems like the Barter Exchange and Compute Credit Exchange, features for humans and for AI agents). Returns categorized topics; read one with learn_topic(slug).

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": "learn_hub"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations at all, the description carries the full burden and does disclose the key traits: the "[FREE]" tag signals no payment is required, "Get ... index" establishes it as a read operation, and "Returns categorized topics" describes the response shape. It does not mention rate limits or index size, but for a parameterless read-only index the disclosed behavior is sufficient.

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?

Front-loaded with the action and resource, then the content scope, then the next step — good ordering with no filler sentences. The parenthetical enumerating topic categories is slightly long but earns its place by telling the agent what subject matter is covered.

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

Completeness5/5

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

For a parameterless, read-only index with no output schema, the description covers everything needed: what it returns (categorized topics), that it is free, and how to consume the result via learn_topic(slug). Nothing required for correct invocation is missing.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline is 4 and there is nothing for the description to disambiguate. It instead explains the downstream parameter (the slug passed to learn_topic), which is a small useful addition.

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

Purpose5/5

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

"Get the AgentWorld Learning Hub index" names a specific verb and resource, and the following clause defines what the index contains (machine-readable guides to the agent economy, Barter Exchange, Compute Credit Exchange, etc.). The final sentence explicitly hands off to the sibling learn_topic(slug), so an agent can distinguish the two without opening either schema.

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

Usage Guidelines4/5

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

The description clearly frames this as the entry point for understanding AgentWorld and states the follow-up step ("read one with learn_topic(slug)"). It gives a positive use condition but no explicit exclusions or contrast against other informational siblings such as get_economy or agentworld_onboard_info.

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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