Look a row up by an exact key
dataset_rowThe rows of the Netsheetly 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
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The verb 'look up' suggests a read-only operation, but it does not explicitly state that no modifications occur or mention any permissions or side effects.
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 that effectively conveys the core functionality without unnecessary detail.
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 tool has low complexity with two parameters and no output schema. The description sufficiently explains the purpose and parameters, with only a minor gap regarding the column identifier format.
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 description clarifies that 'column' is a column identifier and 'value' is the value to match, and adds the important matching behavior (exact, case-insensitive). It does not specify the format of the column identifier (e.g., name vs. index), but the overall semantics are clear.
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?
States a specific verb 'look up' and resource 'row', and clearly defines the matching condition (exact, case-insensitive). This distinguishes it from siblings like dataset_search which likely performs fuzzy or partial matching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies the use case for exact-match queries through the description, though it does not explicitly mention alternatives or when not to use it. The clarity of the matching condition provides sufficient guidance.
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 operation: schema, provenance, exact row lookup, substring search, ordered comparison, statistics, and top/bottom extremes. dataset_row and dataset_compare are somewhat similar, but their descriptions make the single-value vs ordered-multi-value distinction clear.
Naming Consistency4/5
All tools share the consistent dataset_ prefix and snake_case style, making the family recognizable. However, the second segment mixes noun, verb, and adjective forms (columns, compare, top), so the naming is not a strict verb_noun pattern.
Tool Count5/5
Seven tools is well-scoped for a dataset querying server. Each tool covers a different common question type without redundancy, and the count feels neither thin nor bloated.
Completeness5/5
The tool set covers schema discovery, provenance, exact lookup, substring search, comparisons, summary statistics, and top/bottom ordering. For a read-only dataset QA server, there are no obvious missing operations or dead ends.