[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).
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It says 'Returns categorized topics' and prefixes '[FREE]', implying a read-only, no-cost operation. However, it does not explicitly state side-effect-free behavior, rate limits, or other operational traits; it is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single scannable sentence that front-loads the action and '[FREE]' tag, briefly explains the content, states the return value, and routes to the sibling tool. No word is wasted and the structure is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool with an output schema and clear return semantics, the description is complete. It explains what the index contains, what the response is (categorized topics), and what the agent should do next (read one via learn_topic), leaving no critical gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema coverage, so there is no parameter burden for the description to carry. The description appropriately focuses on the output and follow-up use of learn_topic, which is more valuable than documenting nonexistent parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get'), resource ('AgentWorld Learning Hub index'), and the content scope of the guides. It also differentiates itself from sibling learn_topic by explicitly saying 'read one with learn_topic(slug)', making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear usage context: call this to get the index of categorized topics, then use learn_topic(slug) to read an individual topic. It doesn't enumerate when this tool is NOT appropriate versus data_* siblings, but for a zero-parameter index tool the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.