Look a row up by an exact key
dataset_rowThe rows of the Stagenix dataset where a column equals a value exactly (case-insensitive).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
site
by com.stagenix
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full behavioral burden. It usefully discloses that matching is exact and case-insensitive and implies multiple rows may be returned, but it does not describe the output shape, ordering, or behavior when no rows match.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. It conveys the core matching semantics efficiently, though the phrasing is slightly nominal rather than action-oriented.
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?
For a two-parameter lookup the description covers the essential selection semantics. However, the title says 'a row' while the description says 'the rows', leaving ambiguity about result cardinality, and there is no mention of no-match behavior or output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 maps 'column' and 'value' to the two parameters and clarifies that equality is exact and case-insensitive, which is meaningful for such simple string parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The title 'Look a row up by an exact key' and the description clearly express an exact, case-insensitive equality lookup against a dataset column. It is distinguishable from sibling tools like dataset_search or dataset_top, though the description lacks a strong imperative verb.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for exact match lookups but provides no explicit guidance on when to choose this tool over dataset_search or dataset_stats. There are no stated exclusions, prerequisites, or alternatives.
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
A3.8/5.0
Disambiguation4/5
Most tools have clear, distinct purposes—schema, provenance, exact-match lookup, substring search, comparisons, statistics, and ranking. dataset_compare and dataset_row overlap somewhat since both filter by column values, but compare is explicitly for ordered multi-value 'X vs Y' queries while row is for a single exact match.
Naming Consistency5/5
All tools share a consistent dataset_ prefix and use clear, descriptive lowercase names. Even though some are nouns (columns, row, provenance) and others are verbs (compare, search, stats, top), the pattern is uniform and predictable.
Tool Count5/5
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct mode of interaction—schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking—without unnecessary redundancy.
Completeness4/5
The tool surface covers the core read-only dataset exploration lifecycle well: understand schema, get provenance, retrieve rows, search, compare, compute statistics, and rank. A minor gap is the absence of a tool for fetching distinct values or arbitrary aggregations, but this is not a critical dead end for the stated purpose.