Compare rows side by side
dataset_compareThe rows of the Coshhvo dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
dataset_compareThe rows of the Coshhvo dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | 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?
With no annotations present, the description carries the burden of disclosure. It clearly explains the selection and ordering behavior, which is the core trait. However, it does not describe the output format, matching semantics (e.g., exact match/case sensitivity), or behavior when no rows match, leaving moderate gaps.
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 a single concise sentence that front-loads the behavior and ends with the intended use case. Every word contributes meaning, with no redundancy or 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?
For a two-parameter read-oriented tool with no output schema, the description explains the core mechanism well enough for invocation. But the title promises 'side by side' comparison while the description only says rows are returned in order, leaving the actual return layout ambiguous. Edge cases such as no matches or duplicate values are also unaddressed.
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%, so the description must compensate. It does so by relating `column` to the field being matched and `values` to the accepted values that determine the row order. This adds real semantic meaning beyond the raw schema, even though it does not restate constraints like minItems or maxItems.
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 exactly what the tool does: it returns rows from the Coshhvo dataset whose column matches any of the given values, in a specified order. It also ties this to the concrete use case of 'X vs Y' questions, making its purpose distinct from generic row retrieval or search.
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 phrase 'for "X vs Y" questions' provides an explicit use case, and the 'order given' clause implies this is the tool when preserving a requested comparison order matters. It does not explicitly name sibling tools or state when not to use it, so it falls short of full guidance.
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.
Each tool targets a distinct dataset operation: schema, provenance, exact lookup, multi-value comparison, substring search, numeric stats, and ranking. The only near-overlap is dataset_row and dataset_compare, but the multi-value/ordered behavior of compare makes its purpose clearly different.
All tools follow a consistent dataset_<noun> snake_case pattern. The convention makes the tool surface predictable and easy to navigate.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct querying need without the set feeling bloated or sparse.
The tool set covers the full lifecycle of exploring a read-only dataset: schema discovery, provenance, exact filtering, fuzzy search, comparison, statistics, and top/bottom ordering. There are no obvious dead ends for common dataset questions.