Search the dataset
dataset_searchRows of the Longtailo 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
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description implies a read-only search operation but does not explicitly state that it has no side effects. This is a minor gap because search operations are typically expected to be non-mutating.
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?
The description is a single, concise sentence with no unnecessary details. It communicates the essential functionality and constraints efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides enough context to understand what the tool does, including the dataset name, search behavior, case-insensitivity, and limit. It does not specify the output format, but that is not essential for basic usage.
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?
The 'query' parameter is described in the schema as 'text to look for in any cell', and the tool description reinforces it. The 'limit' parameter is described via 'up to 50' in the description, though the schema lacks a description for it. Overall, parameters are adequately explained.
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?
The description clearly states that the tool returns rows from the Longtailo dataset matching a case-insensitive query, with an optional limit. This is specific and distinguishes it from sibling tools like dataset_row or dataset_columns.
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?
The description does not explicitly say when to use this tool over alternatives, but the search-centric purpose is self-evident. It lacks explicit guidance on choosing between dataset_search and dataset_top or dataset_row, but it is not misleading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A3.9/5.0
Disambiguation4/5
Each tool targets a distinct query mode—schema, provenance, exact match, search, comparison, stats, and top-N—so there is little real ambiguity. The main possible confusion is between dataset_row and dataset_compare, since both filter by column values, but the descriptions clarify that compare is for ordered multi-value lookups while row handles single exact-value matches.
Naming Consistency5/5
All tool names follow a consistent dataset_<noun> pattern with lowercase snake_case, making the family easy to recognize and predict. Though the names use nouns rather than verbs, the convention is uniform and clear.
Tool Count5/5
Seven tools is a well-scoped set for querying and analyzing a single dataset. Each tool covers a distinct data-access need without redundancy or unnecessary bloat.
Completeness4/5
The surface covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, numeric stats, and top/bottom selection, which is comprehensive for typical dataset questions. A minor gap is the lack of categorical frequency counts or a way to retrieve all rows without a filter, but agents can work around those by combining existing tools.