dataset_compareThe rows of the RollCallWorks 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?
No annotations are provided, and the description does not explicitly state whether the operation is read-only, idempotent, or has any side effects. While the nature of the action (returning rows) implies a safe read, the lack of explicit transparency about permissions or effects leaves some ambiguity.
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 concise, consisting of a single sentence that packs all essential information without redundancy. It follows a clear structure: subject, action, and purpose, making it easy to parse and understand.
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 tool with only two parameters, the description is largely complete. It explains the selection logic and output ordering. However, it assumes prior knowledge of the 'RollCallWorks' dataset name and does not specify the return format (e.g., full row objects vs. specific fields), which could be relevant for an agent.
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 schema only defines types and constraints for 'column' and 'values'. The description adds crucial meaning: it explains that 'column' refers to a field in the dataset, 'values' are the set of values to match with an OR condition, and the 'order given' determines the output row order. This goes beyond the raw schema.
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 clearly states the tool's function: it retrieves rows from a specified dataset where a given column matches any of the provided values, and arranges them in the order given. The phrase 'for X vs Y questions' explicitly identifies the intended use case, making the purpose unambiguous.
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 offers a general hint about when to use this tool ('for X vs Y questions') but does not explicitly contrast it with sibling tools such as dataset_row or dataset_search. An agent would need to infer that this tool is for comparing multiple rows rather than fetching a single row or searching based on arbitrary conditions.
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 operation (schema, provenance, exact lookup, fuzzy search, comparison, stats, top-N), but dataset_row, dataset_search, and dataset_compare all return matching rows and could be confused without careful reading of their filter semantics.
All tools share the dataset_ prefix and use clear lowercase snake_case names. The second part is sometimes a noun (columns, provenance, row) and sometimes a verb/search-style word, but the pattern is uniform and predictable.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct query mode without bloat or redundancy.
The set covers schema discovery, provenance/citation, exact value lookup, substring search, comparisons, numeric statistics, and top/lowest ranking. For a read-only dataset MCP server this is a complete lifecycle with no obvious dead ends.