[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.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 carries the burden. It discloses that the operation is a read, that it is free, and that the output is clean text. It could add error behavior for invalid slugs, but for a simple free read operation this 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences front-load the most important facts ([FREE], read, clean text, examples) and put the prerequisite last. Every word earns its place.
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 one-parameter read tool with an output schema, the description is nearly complete: it explains how to obtain the slug and what the call returns. A minor gap is that it does not state what happens for an unknown slug, but the output schema covers the return shape.
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 schema only defines slug as a string with 0% coverage, but the description adds substantial meaning: the slug is an AgentWorld learning-topic identifier, with four examples and an instruction to get valid values from learn_hub(). This fully compensates for the empty schema.
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 uses a specific verb ('Read'), identifies the exact resource ('one AgentWorld learning topic'), and gives concrete slug examples. The phrase 'Use learn_hub() first to list available slugs' also distinguishes this per-topic reader from the catalog-listing sibling.
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?
It gives a clear prerequisite: call learn_hub() first to discover valid slugs. It does not explicitly discuss when not to use the tool or name alternatives, so it misses the top band, but the intended workflow is obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.