Reviewvo: the site's own MCP server — dataset; every answer cites the site.
The columns, which of them are numeric, the row count and the provenance banner of the Reviewvo dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It discloses the specific information returned (columns, numeric flags, row count, provenance banner), which is helpful. However, it doesn't explicitly state that the tool is read-only or has no side effects, nor does it describe any other behavioral traits. This is a minor gap for a simple inspection tool.
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, well-structured sentence that front-loads the main purpose (columns, numeric flags, row count, provenance) and ends with a clear usage directive. Every word earns its place; there is no redundancy or filler.
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's simplicity (no parameters, no output schema), the description adequately explains what is returned and when to use it. It covers the essential information an agent needs to call it correctly. It could optionally mention that it is a read-only schema inspection, but that is not critical for invocation.
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?
The tool has zero parameters, so there is nothing for the description to explain beyond what is already evident. The baseline for 0 parameters is 4, and the description adds no confusion. It does not need to describe parameters since none exist.
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 the tool's purpose: to retrieve the columns, numeric flags, row count, and provenance banner of the Reviewvo dataset. It also includes a specific directive to 'Call this first to learn the schema,' which distinguishes it from siblings by establishing it as the entry point for understanding the dataset structure.
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?
The description explicitly instructs to call this tool first, providing clear context for when it should be used. It doesn't name alternatives or exclusions, but the 'first' directive is a strong usage guideline that implies it precedes other dataset tools. This meets the 'clear context, no exclusions' benchmark.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The rows of the Reviewvo dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral disclosure. It does disclose that rows are returned in the order of the given values, which is a useful trait. However, it does not specify what columns are returned, whether it is a read-only operation (though obvious), or how missing values are handled, leaving some ambiguity for an agent without an output schema.
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 sentence that conveys the core behavior without redundancy. It front-loads the main action and includes the order detail, which is essential. While the phrasing could be more polished, it is efficient and free of extraneous content.
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?
The tool is simple (two parameters, no output schema), so the description must be self-sufficient. It explains the filtering and ordering but does not describe the return format (e.g., whether it returns full rows or a subset of columns). It also omits edge-case behavior such as what happens when no rows match. This is a moderate gap for an agent expected to call the tool correctly without additional context.
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?
The schema has zero description coverage, so the description must explain the parameters. It does: 'column' is the field to match, 'values' are the list of acceptable values, and the order of the values determines the order of returned rows. This effectively compensates for the lack of schema descriptions, providing clear semantics for both parameters.
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 the tool retrieves rows from the Reviewvo dataset filtered by a column matching any of the provided values, preserving the given order. The phrase 'for "X vs Y" questions' signals its comparative intent, distinguishing it from generic search or single-row retrieval tools. While the phrasing is slightly awkward, the purpose is unambiguous and differentiates it from siblings like dataset_search and dataset_row.
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?
The description offers a contextual hint ('for "X vs Y" questions') that implies when to use it, but it does not explicitly name alternative tools or state conditions for when not to use it. There is no mention of how it relates to dataset_row, dataset_search, or dataset_top, leaving the agent to infer the intended usage scenario. This is adequate but not explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The source, the date it was computed, the licence and the citation for the Reviewvo dataset. Read this to attribute a figure correctly.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the behavioral burden, and it does disclose the exact metadata fields returned: source, computed date, licence, and citation. It omits output formatting details, but for a static, side-effect-free metadata lookup this is adequate.
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 definition is two short sentences with the metadata categories front-loaded and the actionable purpose at the end. No filler or redundancy.
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?
For a zero-parameter metadata tool with no output schema, the description covers both the content and the reason to call it. An agent has everything it needs to invoke and interpret the result.
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?
The input schema has zero parameters, so there is no parameter semantic gap to compensate for. The description appropriately explains what metadata is returned instead of describing arguments.
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?
States a clear resource—the Reviewvo dataset provenance—and a specific purpose: correctly attributing a figure. The listed contents (source, computed date, licence, citation) make it semantically distinct from sibling tools that handle columns, rows, search, stats, and top-N data.
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?
Gives a clear trigger: read this when a figure needs attribution. It does not explicitly name alternatives or exclusions, but the sibling tools' purposes are sufficiently distinct that the intended use is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The rows of the Reviewvo dataset where a column equals a value exactly (case-insensitive).
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals case-insensitivity and exact matching, but omits crucial behaviors such as whether the lookup is read-only, what happens if no rows match, whether multiple rows are returned, ordering, pagination, or error handling. The description is minimal and insufficient.
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?
A single sentence that is direct and efficient. It states the core behavior without any redundant words. The structure is appropriately front-loaded with the primary action.
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?
For a simple 2-parameter lookup tool, the description conveys the core functionality but misses usage differentiation and return behavior. It does not mention whether the result is a single row or multiple, nor does it address error scenarios. Given the absence of an output schema and annotations, the description should have provided more context to make the tool fully usable.
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?
The description clarifies that 'column' is the field to match and 'value' is the target value, which adds meaning beyond the bare schema types. However, it does not specify allowed column names, value formats, or any constraints. Given the 0% schema coverage, the description partially compensates but not fully.
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 the tool returns rows where a column equals a value exactly, with a specific verb ('look up') and resource ('rows of the Reviewvo dataset'). It does not explicitly contrast with the sibling dataset_search, which might perform fuzzy or broader matching, so it lacks explicit differentiation but the purpose is unambiguous.
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?
No guidance is given on when to use this tool instead of alternatives like dataset_search. The description does not mention exclusions, prerequisites, or scenarios where this exact-match lookup is preferred over other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Reviewvo dataset whose cells contain the query (case-insensitive), up to 50.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | text to look for in any cell |
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 discloses case-insensitivity and the 50-row cap, but does not explicitly state the operation is read-only or describe behavior with no results. As a search tool, safety is inferred but not stated.
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?
A single, front-loaded sentence that states the core behavior and constraints with zero waste. Every word adds value.
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?
For a simple search tool, the description covers purpose, matching behavior, and limit. Minor gap: no mention of default limit when omitted or ordering of results, but given the output is rows of a known dataset, it is largely complete.
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 covers query with a description and limit has none (50% coverage). The description adds meaning to both: case-insensitive search across all cells for query, and the up-to-50 cap for limit. It compensates for the schema gap effectively.
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?
States a specific action (search rows) on a specific resource (Reviewvo dataset), with explicit matching behavior (case-insensitive, any cell) and a limit. Clearly distinguishable from sibling tools like dataset_columns or dataset_stats.
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?
The description implies when to use (when you need rows matching text) but provides no explicit comparison to siblings or exclusions. No guidance on when not to use it or alternatives, leaving some inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
count, min, max, mean, median and sum of a numeric column of the Reviewvo dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and does well: it discloses that grouping commas and currency symbols are handled during parsing, and that non-numeric rows are excluded and counted. This reveals edge-case behavior an agent would otherwise only discover by calling the tool. It stops short of covering error behavior for unknown columns or exact return shape, so a 5 is not warranted.
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?
A single efficiently packed sentence that front-loads the computed statistics and then adds the two preprocessing caveats. Every element earns its place; no filler.
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?
For a one-parameter tool with no output schema and no annotations, the description covers what is computed and how dirty data is handled. The main gap is the absent return format, but the enumerated statistics strongly imply the output structure, making the description adequate if not exhaustive.
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 compensate. It does: 'numeric column of the Reviewvo dataset' clarifies that the sole `column` parameter refers to a column name and should be numeric for meaningful results. It could add expectations about what happens if a non-numeric column is passed, but for a single simple parameter this is solid coverage.
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 names a specific verb-like function ('count, min, max, mean, median and sum') applied to a specific resource ('a numeric column of the Reviewvo dataset'). This clearly differentiates it from siblings like dataset_columns (listing columns) and dataset_search (searching rows).
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?
The description clearly implies the tool is for aggregate summaries of a numeric column, which indirectly separates it from the sibling tools. However, it does not explicitly name alternatives or state when-not-to-use conditions, leaving routing mostly to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The highest (or lowest) rows of the Reviewvo dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the burden. It does not mention whether the operation is read-only, whether it modifies data, or any side effects. Since it is likely a query tool, this omission reduces transparency.
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 extremely concise, using one clear sentence with an illustrative phrase. There is no redundancy or extraneous content; it efficiently conveys the core functionality.
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?
The description is adequate for a simple top-N query tool, but lacks usage context, parameter details, and any mention of output format. Given the siblings and the low schema coverage, more context would be needed for an agent to reliably select and invoke this tool correctly.
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?
The schema has a description only for the 'ascending' parameter; 'limit' and 'column' have no descriptions. The tool description does not add any meaning to these parameters, leaving the agent to guess their roles (though names are somewhat self-explanatory). Coverage is low and description does not compensate.
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 indicates the tool returns the highest or lowest rows based on a numeric column, with a helpful example phrase. It does not explicitly state 'returns' but the intent is unambiguous. It differentiates from search/stats by focusing on ranking, though not explicitly naming siblings.
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?
No guidance is provided on when to use this tool versus the alternatives like dataset_search or dataset_stats. The description gives a sense of 'which is most/least' but does not contrast with sibling tools or state conditions for selection, leaving the agent to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
dataset_columnsdataset_comparedataset_provenancedataset_rowdataset_searchdataset_statsdataset_topClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool targets a distinct operation: schema, provenance, exact lookup, substring search, multi-value comparison, aggregate stats, and ranking. Minor overlap exists between dataset_row and dataset_compare (both match column values) and between dataset_columns and dataset_provenance (both expose provenance details), but the descriptions make the intended use clear.
All tools share the dataset_ prefix and use lowercase snake_case, giving a clear family identity. The suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), which is a minor inconsistency, but the overall pattern is predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool has a distinct role and none feels redundant or unnecessary for the core task of exploring and analyzing the Reviewvo dataset.
The surface covers schema discovery, provenance, exact/string lookup, multi-value comparison, numeric summaries, and ranking, which covers the main ways an agent would explore a dataset. Minor gaps include no pagination for large result sets and no multi-column filter, but these are workable given the available tools.