Search the dataset
dataset_searchRows of the Taxyearvo dataset whose cells contain the query (case-insensitive), up to 50.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | text to look for in any cell |
site
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses two behaviors beyond the schema: case-insensitive matching and a result cap of 50. However, with no annotations, it does not mention ordering, default limit when omitted, empty-result behavior, or output format, leaving the operational profile partly unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single 16-word sentence that front-loads the dataset and action, with no filler. Every phrase adds operational detail (case-insensitive, any cell, cap).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description communicates the core contract (match criterion, case handling, result cap) and states that rows are returned, which is sufficient given the tool's simplicity. But with no output schema and no annotations, it omits default-limit behavior and row shape; these are notable but non-critical gaps for a simple search.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 50% of parameters: query has a description, limit does not. The description adds meaning by stating 'up to 50', clarifying that limit bounds the number of returned rows, and reinforces query semantics with 'any cell'. This compensates for the undocumented limit parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description states a specific search operation: returning rows from the Taxyearvo dataset where any cell contains the query, case-insensitively, capped at 50. This clearly separates it from sibling tools like dataset_row (single row), dataset_columns (column list), or dataset_stats (aggregates).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is provided, and no sibling alternatives are named. The intended use is only implied by the phrasing 'Rows ... whose cells contain the query,' which suggests full-text search, but the agent is left to infer when this is appropriate versus dataset_top or dataset_row.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.7/5.0
Disambiguation4/5
Each tool targets a distinct data-access operation: schema intro, provenance, exact lookup, substring search, value comparison, statistics, and extreme rows. dataset_compare and dataset_row are somewhat similar in that both filter on column values, but their descriptions clarify the multi-value ordered behavior versus exact equality.
Naming Consistency5/5
All tools share a consistent dataset_ prefix and lowercase snake_case naming style, making the set predictable. Although the names are nouns rather than verbs, the convention is uniform and easy to infer.
Tool Count5/5
Seven tools is a well-scoped size for a dataset-focused MCP server. Each tool addresses a distinct query or metadata need without excessive overlap or unnecessary bloat.
Completeness4/5
The surface covers the main dataset operations: schema, provenance, row lookup, text search, multi-value comparison, numeric stats, and top/bottom rows. Minor gaps like range filtering, grouping, or paginated full scans exist but most typical questions about this dataset can be answered.