dataset_compareThe rows of the WallChartBook 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 burden of behavioral disclosure. It discloses the matching semantics ('any of the given values') and ordering behavior ('in the order given'), which adds real value beyond the schema. However, it does not mention edge cases, case sensitivity, or whether the returned rows include all columns, so transparency is partial.
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 entire description is one compact sentence that front-loads the action and resource, then adds the matching criterion, ordering behavior, and usage context. Every clause earns its place and there is no redundancy.
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 simple two-parameter read-only comparison tool with no output schema and no annotations, the description covers the core semantics, ordering, and usage context. It does not describe the return format or error behavior, but the simplicity of the tool and clear rules make it adequately complete for an agent to call 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%, so the description must compensate. It clearly explains both parameters: 'column' is the field to match on, and 'values' is the list of values to match with OR semantics ('any of'). The order-given note also clarifies how the values array is used. This is strong compensation for a two-parameter 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 names the specific resource (WallChartBook dataset), the action (returns rows), and the selection criteria (column matches any given values). The 'X vs Y' phrasing and 'side by side' title clearly differentiate it from single-row lookup (dataset_row) and free-form search (dataset_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 description explicitly ties the tool to 'X vs Y' questions, giving a clear when-to-use signal. It does not explicitly name sibling alternatives or state when not to use it, but the context is unambiguous enough for an agent to select it over row lookup or search.
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.
Most tools have clearly distinct purposes, but dataset_row and dataset_compare overlap for single-value equality checks, and dataset_search's substring match can also return exact matches. The descriptions largely resolve this, so the ambiguity is minor.
All tool names use the same dataset_ prefix and lowercase snake_case, which is predictable and clean. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search) and the adjective-like 'top', so it is not a fully consistent verb_noun pattern.
Seven tools is an appropriate size for a dataset-query server; each tool covers a distinct query pattern (schema, exact lookup, search, comparison, stats, ranking, provenance) without redundancy or bloat.
The server covers the core read-only workflows for the dataset: schema discovery, row retrieval, text search, comparisons, numeric summaries, top/bottom rankings, and citation metadata. It lacks a way to list all rows or get distinct column values, but these are workable gaps rather than dead ends.