vela.corpus.list_topicsList the coordinate systems the corpus can be queried through — each with its thesis, axes, and controlled vocabularies. Call this before vela.corpus.query to learn what axes and themes exist. FREE and read-only.
[capability: topic-coordinate-systems · mirrors: GET /api/corpus/topics]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| topics | Yes |
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?
Annotations already declare readOnlyHint and destructiveHint, and the description adds the useful details that the tool is FREE and read-only. It also notes the REST mirror, which provides some extra context, but it does not go beyond annotations in explaining broader behavioral traits such as data freshness, rate limits, or scope limitations.
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 compact and front-loaded: the core action and output are stated in the first sentence, followed by the key usage instruction and cost/safety note. The metadata tags add value without bloating the text.
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 zero-parameter, read-only listing tool with an output schema and annotations covering safety, the description supplies the essential context: what is returned, why to call it, and the relationship to the main query tool. Nothing material is missing for an agent to invoke it correctly.
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 input schema has zero parameters, so the baseline is 4. The description correctly indicates that no inputs are required and focuses on what the tool returns, which is appropriate for a parameterless discovery endpoint.
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 ('List'), a concrete resource ('coordinate systems the corpus can be queried through'), and the key contents (thesis, axes, controlled vocabularies). It also positions itself relative to vela.corpus.query, making its purpose unmistakable even among several corpus siblings.
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 explicitly advises calling this tool before vela.corpus.query to discover available axes and themes, which is clear usage guidance. It does not explicitly mention when not to use it or name all alternative tools, but it gives enough context for correct invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.