Look a row up by an exact key
dataset_rowThe rows of the Reconzy 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.reconzy
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden; it discloses case-insensitive equality and plural row results, but omits return format, empty-result behavior, and error handling.
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 filler, and the title reinforces the core action immediately.
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?
For a simple two-parameter lookup, the description covers the essential behavior; a minor gap is that it does not clarify whether multiple matching rows are returned or what happens when no match exists.
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?
Parameter names are self-explanatory and the description links column and value, but there is no parameter-level detail about what column identifier format is expected or how values are interpreted.
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?
Title and description clearly state an exact row lookup by column/value equality, with case-insensitive matching, and this is distinct from the broader search/aggregation tools in the sibling list.
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?
The phrase 'exact key' and 'case-insensitive' give clear context for exact-match lookups, but there is no explicit comparison with dataset_search or other sibling tools.
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
A3.6/5.0
Disambiguation4/5
Tools are mostly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping row-filtering purposes. Descriptions clarify exact match vs. contains vs. any-of, so confusion is unlikely but possible.
Naming Consistency3/5
All tools share the dataset_ prefix, but the suffix pattern is inconsistent: some are nouns (columns, provenance, row, stats, top) and some are verbs (compare, search). A more uniform verb_noun or noun_only convention would improve predictability.
Tool Count5/5
Seven tools is a well-scoped set for dataset exploration, covering schema, provenance, lookup, search, comparison, statistics, and top-values queries without redundancy or bloat.
Completeness4/5
The toolset covers the main dataset exploration operations well, but it lacks explicit distinct-value or grouped-aggregation tools, which could be useful for fuller ad-hoc analysis. It is not severely incomplete.