dataset_compareThe rows of the HostingByStack 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 carries the behavioral burden. It discloses the selection criteria ('whose column is any of the given values') and ordering ('in the order given'), but it does not mention what happens when a value has no matching row, how output is structured, or whether the comparison is truly displayed side-by-side.
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 conveys the core behavior, the dataset, the filtering logic, the output ordering, and the intended use case. There is no redundant or filler content.
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 covers the essential invocation details—dataset, column, values, and order—but with no output schema and no annotations, it does not specify the result format or edge-case handling (e.g., missing values, duplicates). This leaves moderate ambiguity for an agent deciding whether the output will be truly comparable side-by-side.
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%, so the description must explain the parameters. 'Column' is clearly the field to match against, and 'values' are the list of values to include, with the output order tied to their given order. This adds functional meaning beyond the bare schema definitions.
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 a specific operation: it returns rows from the HostingByStack dataset matching any of the given values, in the order provided. The phrase 'for X vs Y questions' clearly distinguishes this from siblings like dataset_row or dataset_top, which serve different query patterns.
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 gives a clear context for use: 'for "X vs Y" questions,' implying a side-by-side comparison of specific values. It does not explicitly name alternatives or state when not to use the tool, but the intended scenario is evident.
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.
The dataset_* tools are mostly distinct, but dataset_row and dataset_compare overlap: both retrieve rows by matching a column value, and compare can effectively do row's job with a single value. The enquiry_* tools are clearly separated by behavior, fields, and submission.
Tool names consistently use lowercase snake_case with clear domain prefixes: dataset_* and enquiry_*. However, some names are nouns (dataset_columns, dataset_row, dataset_stats) while others are verbs or adjectives (dataset_compare, dataset_search, dataset_top), so the pattern is not perfectly uniform.
Ten tools is well-scoped for a server covering two clear areas: read-only dataset exploration and enquiry submission. Each tool has a practical role, and the count is comfortably within the ideal range.
The dataset side covers schema, provenance, exact lookup, substring search, comparisons, statistics, and top/bottom rows, which is strong for a read-only dataset server. The enquiry side covers describing the process, listing fields, and submitting with a two-step confirmation, though there is no way to check submission status or cancel an enquiry.