Deep-dive any Lab entity by reference—dataset slug, repo name, domain slug, source ID, or analysis slug—and return its card, children, and routing hints.
Deep-dive any Lab entity by ref: dataset slug, repo name, domain slug, source id, or analysis slug. Returns card + children + routing hints.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.3.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it partially meets it by describing a composite return ('card + children + routing hints'), which signals a read-only traversal. It says nothing about invalid/missing refs, permissions, rate limits, or the cost of deep traversal, so an agent calling with an unknown ref gets no expectation-setting.
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 sentences, no filler, and the accepted-input list plus return shape are front-loaded before any elaboration. Every clause conveys information an agent can act on.
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 tool with an output schema that already documents return values, the description covers both what to pass in and the shape of the payload. The remaining gap is routing relative to sibling discovery tools, which keeps it from being fully self-sufficient.
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?
Schema coverage is 0% for the single 'ref' parameter, so the description must compensate, and it largely does by listing the five accepted identifier formats. It stops short of giving format examples, prefix syntax, or whether a slug vs an id resolves differently.
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 ('deep-dive') plus the resource ('any Lab entity') and enumerates the five accepted ref kinds (dataset slug, repo name, domain slug, source id, analysis slug), which is unusually concrete. It does not contrast itself with discovery siblings like find or lab_map, so an agent must still infer its place in the suite.
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?
Usage is only implied: you call it when you already have a 'ref' to an entity. There is no explicit when-to-use or when-not-to-use, and no mention of the obvious alternatives in the sibling set (find, lab_map, workspace_triage) or how choosing explore over those changes the outcome.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.