Look a row up by an exact key
dataset_rowThe rows of the Attestroom 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
A3.5/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses key matching behavior (exact, case-insensitive) and that rows are returned, but it does not mention whether multiple rows can be returned, no-match behavior, or any read-only/error characteristics.
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 front-loads the dataset and match criterion. There is no filler or redundant restatement of the schema.
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 simple two-parameter lookup tool, the core semantics are present, but the description lacks an output description (no output schema), match-multiplicity expectations, and explicit relation to sibling tools. It is adequate but not fully complete.
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 coverage is 0%, so the description must clarify the two parameters. It does connect 'column' and 'value' to the match operation, indicating column is the field and value is the lookup value, but it offers no additional constraints, formats, or examples.
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 identifies the operation: returning rows of the Attestroom dataset matching a column/value pair exactly, with case-insensitivity. The title's 'Look a row up by an exact key' reinforces the verb and resource, but it does not explicitly distinguish itself from the sibling dataset_search beyond the word 'exactly'.
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 implies use cases: exact, case-insensitive column-value lookup. However, it provides no explicit when-to-use/when-not-to-use guidance or alternatives such as dataset_search for fuzzy matching, leaving the agent to infer usage from the phrase 'exactly'.
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 clearly distinct purposes: schema, provenance, exact lookup, substring search, stats, and top/bottom queries. dataset_row and dataset_compare overlap somewhat since both filter on column values, but the descriptions clarify exact single-value matching versus ordered multi-value comparison.
Naming Consistency4/5
All tools consistently share the dataset_ prefix and use lowercase snake_case, which makes the set feel unified. However, suffixes are a mix of nouns (columns, provenance, row, stats) and verbs (compare, search), so the pattern is not perfectly uniform.
Tool Count5/5
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a distinct common operation without feeling padded or redundant.
Completeness4/5
The set covers the essential dataset operations: schema discovery, provenance, exact match, text search, numeric statistics, ranking, and multi-value comparison. Minor gaps exist such as pagination for search results and range-based numeric filters, but agents can generally answer common questions without dead ends.