dataset_compareThe rows of the HeadcountDesk 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, the description must carry behavioral disclosure itself. It does reveal meaningful behavior: rows are returned 'in the order given' and matched if the column is 'any of the given values'. However, it does not disclose output format, handling of missing values, case sensitivity, or whether the operation is read-only; these gaps matter without annotation support.
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?
A single sentence that is front-loaded with the core operation and closes with a usage context. No filler words; every clause adds meaningful information.
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?
The description is adequate for invoking the tool given the schema constraints, but it omits the return format implied by the title 'Compare rows side by side'. Since there is no output schema, an agent cannot know whether results are presented side-by-side, as a simple list, or in some other structure. This is a notable completeness gap for a tool with no annotations.
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 fully compensates for the two parameters. 'whose column is any of the given values' maps `column` to the dataset field and `values` to the list of match values, and 'in the order given' clarifies ordering semantics for the values array. It does not mention exact-match behavior, but the core roles are clear.
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 title supplies the verb 'Compare' and the description specifies the exact resource and behavior: rows of the HeadcountDesk dataset filtered by column matching any of the given values, in the order given. The 'for X vs Y questions' phrase clearly differentiates it from sibling tools like dataset_search or dataset_top.
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 scopes usage to 'X vs Y' questions, which tells an agent when to select this tool. It does not mention alternative tools or describe when not to use it, but the stated purpose provides clear contextual 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 maps to a distinct query type: schema discovery, provenance, exact matches, substring search, value comparison, numeric stats, and top/bottom rows. The potential overlap between dataset_row and dataset_compare is mitigated by the explicit X-vs-Y ordering purpose of compare.
All tools share the consistent dataset_ prefix and snake_case, making the family immediately recognizable. However, suffixes mix nouns (columns, row, stats) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped set for interacting with a single read-only dataset. Each tool covers a distinct user need without redundancy or overwhelming breadth.
The dataset surface is complete for the apparent purpose: schema, provenance, exact lookup, full-text search, comparison, summary statistics, and ranked extremes are all available. No obvious query type relevant to this domain is missing.