get_workspaceSTART HERE. Returns everything that exists in the user's Quillm workspace in one compact overview: every view (slug, purpose, datasets it reads, URL, who last changed it), every dataset (columns, key, row count, freshness), workspace notes left by agents, and recent changes. Call this at the start of every task so you extend what exists instead of duplicating it.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behavioral context: the response is a 'compact overview' and enumerates the categories returned (views with slug/purpose/datasets/URL/last editor, datasets with columns/key/row count/freshness, notes, recent changes). It does not mention auth requirements or size/pagination limits.
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?
Front-loaded with 'START HERE', followed by one dense sentence enumerating contents and a closing call-to-action. Every clause earns its place and there is no filler.
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?
No output schema exists, so the description carries the full burden of describing return values — and it does so in detail across all four content categories. Combined with annotations covering safety, an agent has everything needed to call and interpret this tool.
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 takes zero parameters, so per the rubric the baseline is 4. There is nothing to disambiguate and the description correctly implies a no-argument call.
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?
States a specific verb+resource ('Returns everything that exists in the user's Quillm workspace') with an enumerated scope of what that includes (views, datasets, notes, recent changes). It is clearly distinguishable from siblings like get_view or check_view, which cover single entities.
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?
Explicitly prescribes when to call it ('START HERE', 'Call this at the start of every task') and states the rationale/alternative behavior (extend what exists instead of duplicating). The agent knows both the trigger condition and the consequence of skipping it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.