Look a row up by an exact key
dataset_rowThe rows of the Dispatchzo 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
A3.9/5.0
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. It does disclose a useful behavioral trait (case-insensitive exact match), but it does not clarify whether multiple matching rows are returned or only one, how unmatched queries behave, or whether the operation is read-only. This is a notable gap for an unannotated tool.
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 sentence with no filler. It conveys the core purpose, matching mode, and case-sensitivity in minimal words, and the title reinforces the action. Every word earns its place.
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 tool with no output schema and no annotations, important contextual details are missing. The title says 'row' (singular) while the description says 'rows' (plural), creating ambiguity about the expected result set. There is no mention of error behavior, performance implications, or what happens if no row matches. The tool is simple, so this is not a severe failure, but it is incomplete.
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 description coverage is 0%, so the description must compensate. It effectively explains both parameters: 'column' is the field to compare against, and 'value' is the exact string to match, with case-insensitive equality. This adds meaningful semantic context that the bare schema lacks.
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 states a specific operation: retrieve rows from a dataset by exact column match, with case-insensitivity. This clearly differentiates it from siblings like dataset_search, which implies broader/fuzzy matching, and dataset_top or dataset_stats, which serve different purposes.
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?
Usage guidance is only implied: the phrase 'exactly (case-insensitive)' suggests this tool is for precise key-based lookups rather than fuzzy searches. However, there is no explicit statement of when to prefer this tool over alternatives, nor any exclusion criteria.
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
A4/5.0
Disambiguation5/5
Each tool targets a distinct query need: schema, provenance, exact row lookup, substring search, multi-value ordered comparison, numeric stats, and top/bottom ranking. There is no meaningful overlap that would make an agent uncertain which tool to call.
Naming Consistency5/5
All tools follow a consistent `dataset_` prefix with concise snake_case names that clearly reflect their function. The naming convention is uniform and predictable across the entire set.
Tool Count5/5
Seven tools is a well-scoped set for a dataset-querying server. Each tool covers a distinct operation and none are redundant or superfluous.
Completeness4/5
The toolset covers schema discovery, provenance, exact and substring search, comparison, statistics, and ranking—strong coverage for a dataset Q&A server. Minor gaps exist such as no direct way to fetch all rows, list distinct values, or filter on multiple columns simultaneously, but these can usually be worked around with the existing tools.