Profiles an authorized local dataset and returns a Data Context, with optional local model interpretation for AI assistants.
Profile an authorized local dataset and return Data Context as text.
Optional local model interpretation depends on server configuration. Statistics describe the first configured rows, not necessarily the full dataset.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| data_path | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it discloses useful behavioral traits: output is text, optional local model interpretation depends on server configuration, and statistics are sample-based rather than full-dataset. These are non-obvious behaviors an agent needs to know.
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?
Three sentences, no fluff, and important caveats are stated without burying the main purpose. It earns its place, though it could arguably be tightened further.
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?
Given no output schema, no annotations, and zero parameter documentation, the description is incomplete for safe invocation. It explains the output format broadly but omits parameter semantics and any return-structure details, leaving an agent to guess how to call the tool 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?
Schema description coverage is 0%, and the description adds no meaning for either parameter ('task' and 'data_path'). The agent is left to guess what 'task' means and what format 'data_path' should take, which is a critical gap for a tool with two required 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 names a specific action ('profile'), a resource ('authorized local dataset'), and a deliverable ('Data Context as text'), so an agent can infer the tool's core function. The term 'Data Context' is jargon and not defined, but the overall purpose is clear enough.
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 implies when to use the tool (when profiling an authorized local dataset) and adds a caveat that statistics come from the first configured rows, not the full dataset. However, there is no explicit statement of when not to use it, and no alternatives are referenced since no siblings exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.