Look a row up by an exact key
dataset_rowThe rows of the Keysvo dataset where a column equals a value exactly (case-insensitive).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
dataset_rowThe rows of the Keysvo dataset where a column equals a value exactly (case-insensitive).
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It only discloses that matching is case-insensitive, but omits critical details like whether multiple rows can be returned, how column names are handled, or any performance or error behavior. This is insufficient for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that front-loads the core behavior. It is not verbose and avoids redundancy, earning a high score for conciseness. However, its brevity limits the amount of useful information conveyed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only two parameters, no output schema, and no annotations, the description should provide sufficient context for an agent to call it correctly. It lacks usage guidance, return format details, and edge-case handling. The ambiguity between 'row' (singular) in the title and 'rows' (plural) in the description further reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must clarify parameter meaning. It states that 'column' is a column in the dataset and 'value' is the exact value to match, but adds little beyond the parameter names. It doesn't specify formats, whether column names are case-sensitive, or how values are represented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 Keysvo dataset where a specified column equals a value exactly, with case-insensitivity. It identifies the resource and operation distinctly, though it doesn't explicitly differentiate from sibling tools like dataset_search. The title reinforces the 'exact key' purpose, making it understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. No mention of when exact matching is preferred over fuzzy search (dataset_search) or when to use other sibling tools. The agent receives no context for selecting this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct query mode: schema, metadata, exact match, substring search, multi-value comparison, statistics, and top/bottom ordering. There is minor overlap between dataset_compare and dataset_row since both filter on column values, but their descriptions clearly separate multi-value ordered lookups from single exact matches.
All tools share the consistent dataset_ prefix and snake_case format, which makes the family easy to recognize. However, the suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is consistent in prefix but not perfectly uniform in part of speech.
Seven tools is well-scoped for a dataset query server. Each tool covers a distinct useful operation without redundancy or bloat, and the count feels appropriate for the stated purpose.
The toolset covers the full read-only dataset lifecycle: schema discovery, provenance, exact lookup, fuzzy search, value comparison, numeric statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions, and no missing operations seem necessary for the domain.