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AgentWorld MCP Server

AgentWorld MCP Server

by shawnhvac
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

learn_topic

Read one AgentWorld learning topic as clean text by slug. List available topics first with the learning hub to choose a valid slug before fetching.

Instructions

[FREE] Read one AgentWorld learning topic as clean text (e.g. slug 'compute-credit-exchange', 'barter-exchange', 'ai-agent-economy', 'ai-agent-jobs'). Use learn_hub() first to list available slugs.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "learn_topic"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden: it discloses that the operation is free ("[FREE]") and that the result is plain text, which is genuine behavioral value. It says nothing about behavior on an invalid slug (error vs. empty), rate limits, or the size/shape of a topic, which for an unannotated read tool is a notable gap.

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?

Two short sentences, front-loaded with purpose and followed by the prerequisite. The inline slug examples are the bulkiest part but each one illustrates the expected format, so they mostly earn their place.

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

Completeness4/5

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

For a single-required-parameter read tool with no output schema, the description covers purpose, cost, return format, and the prerequisite call. Only the unknown-slug and error-handling semantics are missing, which are secondary for a simple lookup.

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?

Schema coverage is 0%, so the description must compensate, and it does by giving four example slug values that convey the string format and the topic namespace. It still does not say how slugs are formed or whether unknown slugs fail, so it stops short of fully substituting for schema docs.

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?

States a concrete verb and resource ("Read one AgentWorld learning topic") and returns it as clean text, which distinguishes it from the sibling learn_hub, which enumerates topics rather than fetching one. The examples of slugs reinforce the scope. It does not explicitly contrast itself against a sibling, keeping it just under 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 Guidelines4/5

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

"Use learn_hub() first to list available slugs" gives a clear prerequisite and routing rule to the sibling that provides the input. There is no explicit guidance on what to do when a slug is unknown or when not to use this tool, so the exclusions are implied rather than stated.

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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