Look a row up by an exact key
dataset_rowThe rows of the Yearendo 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.yearendo
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations describing read-only behavior or side effects. The description implies a query operation but does not mention whether it modifies data, nor does it describe output format, pagination, or limits.
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 with no redundant wording. It directly states the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks an output schema and does not specify return structure, error handling, or edge cases. It provides minimal context beyond the basic filtering behavior, leaving important operational details unspecified.
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?
The description references 'column' and 'value' in context, clarifying that column is the field name and value is the exact match value. However, it lacks details on data types, possible values, or behavior when the column does not exist.
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 Yearendo dataset where a column matches a value exactly, which is distinct from the sibling dataset_search tool. It conveys the core filtering action effectively.
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?
No explicit guidance is provided on when to use this tool versus alternatives like dataset_search or dataset_top. The description implies exact-match use but does not state conditions or prerequisites.
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
A3.7/5.0
Disambiguation4/5
Most tools have clear, distinct purposes: schema, search, stats, provenance, and top/bottom comparisons are unambiguous. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by exact column values, but descriptions clarify that compare handles multiple values in a specific order.
Naming Consistency5/5
All tools follow a consistent dataset_ prefix with clear, lowercase snake_case names. The naming pattern is predictable and easy to scan, with no mixing of styles or vague generic verbs.
Tool Count5/5
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common operation—schema, lookup, search, comparison, stats, top values, and provenance—without unnecessary bloat.
Completeness4/5
The toolkit covers the core read-only operations needed for exploring and querying the Yearendo dataset: schema discovery, exact match, substring search, ordered comparison, numeric stats, ranking, and attribution. Minor gaps like grouped aggregations or combined filters exist, but agents can usually work around them with existing tools.