Look a row up by an exact key
dataset_rowThe rows of the PerDiemDesk dataset where a column equals a value exactly (case-insensitive).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
site
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.1/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the matching behavior, including exactness and case-insensitivity, but does not state whether the operation is read-only or mention edge cases like no matches, multiple matches, or errors. With no annotations provided, the description carries the burden of transparency and only partially satisfies it.
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 title and description are very short and directly convey the core purpose without unnecessary detail. The one-sentence description is front-loaded and easy to scan.
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 provides enough information for a basic exact-match row lookup, but with no output schema it omits return format, result multiplicity, sorting, pagination, and no-result behavior. This leaves some context missing for an agent planning to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameter descriptions and the prose only says 'a column equals a value exactly.' It does not clarify which columns are valid, what value format is expected, or provide examples, so the parameters are only minimally understood.
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 description clearly states that the tool returns rows from the PerDiemDesk dataset where a column exactly matches a value, case-insensitively. This distinguishes it from the sibling dataset_search tool, though it does not explicitly name that alternative.
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 does not explicitly say when to use this tool versus dataset_search or other sibling tools. It implies exact-match lookup through the wording, but provides no direct guidance on selection criteria or alternatives.
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
A3.7/5.0
Disambiguation4/5
Each tool targets a distinct query pattern—schema, provenance, exact rows, substring search, stats, top values, and multi-value comparisons—so an agent can generally choose correctly. Some overlap exists between dataset_row and dataset_compare (both filter rows by column values), and dataset_search overlaps with dataset_row on substring matches, but the descriptions are clear enough to resolve the ambiguity.
Naming Consistency5/5
All tool names follow the same dataset_<operation> pattern, with clear nouns like columns, row, search, stats, top, compare, and provenance. The naming is uniform and predictable, with no mixed conventions.
Tool Count5/5
Seven tools is a well-scoped count for a single-dataset exploration server. Each tool covers a distinct query need without redundancy or unnecessary bloat.
Completeness5/5
The tool surface fully covers the read-only dataset workflow: schema inspection, provenance, exact lookup, substring search, statistical summaries, extreme-value ranking, and side-by-side comparisons. No obvious gaps would prevent an agent from answering typical questions about this dataset.