Soapvo: 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 Soapvo 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 provided, the description must carry the full behavioral disclosure burden. It lists the returned elements (columns, numeric flags, row count, provenance banner), which is useful, but it does not explicitly state that the tool is read-only or describe any side effects, limitations, or response format. This is a moderate gap for a tool that is clearly a schema inspection.
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 two sentences with no wasted words. The first sentence front-loads the core content (what it returns) and the second gives a clear usage directive. It is concise, structured, and every part earns its place.
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 tool with no output schema, the description sufficiently conveys what the agent can expect: a set of columns, numeric flags, row count, and provenance banner. It lacks explicit format details (e.g., whether the response is an object or array), but for a schema discovery tool, this is adequate and covers the essential information needed to call it 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 tool has zero parameters and the schema description coverage is 100% (empty schema), so the description does not need to add parameter-level meaning. According to the rubric, a zero-parameter tool gets a baseline score of 4, and the description provides no extraneous parameter info.
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 what the tool returns: the columns, their numeric status, row count, and provenance banner. It also identifies the specific dataset (Soapvo) and instructs to call it first to learn the schema, which distinguishes it from sibling tools that perform other operations.
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 phrase 'Call this first to learn the schema' provides explicit when-to-use guidance, positioning it as the entry point for schema discovery. It does not explicitly list alternatives, but the context strongly implies it is the initial step before using other tools like dataset_stats or dataset_row.
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 Soapvo 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?
With no annotations, the description carries the full burden. It discloses the filtering behavior (column must match any of the given values) and ordering behavior (rows appear in the order of the provided values). However, it does not explain the output format beyond the title's 'side by side' hint, nor address edge cases like missing values or invalid columns.
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 front-loads the core behavior (filtered rows in a specified order) and appends the use case ('X vs Y' questions). Every phrase earns its place, and the title complements without 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 simple two-parameter tool with no output schema or annotations, the description covers what rows are returned, how they are ordered, and when to use it. The main gap is that the return shape is only hinted at by the title 'side by side' rather than described, but this is minor given the tool's simplicity.
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 explains both parameters: 'column' is the field to filter on, and 'values' are the set of values to match. It also adds important semantics by stating that row order follows the order of the given values, which is not visible from the schema alone.
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 states a specific operation: it returns rows from the Soapvo dataset filtered by a column matching any of the supplied values, in the given order. This clearly distinguishes it from siblings like dataset_row (single row) and dataset_search (broader search) through the explicit 'X vs Y' use case.
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 phrase 'for "X vs Y" questions' provides an explicit context for when this tool is the right choice, implying comparison of two or more specific values side by side. It does not explicitly name alternatives or state exclusions, but the use case is clear enough to route an agent.
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 Soapvo 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?
With no annotations provided, the description carries the burden of behavioral disclosure. It implies a read operation via 'Read this', but does not state whether it is side-effect-free, the format of the returned data, or any potential variability (e.g., if the dataset is updated over time). It discloses what is returned but not much else.
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 two short sentences, with the core content list front-loaded and the use case immediately after. Every word earns its place; there is no fluff 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 tool with no parameters, no output schema, and a simple metadata retrieval purpose, the description fully covers what the tool does and when to use it. An agent can correctly decide to invoke this tool for attribution without needing additional information.
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 the schema is empty. The baseline for 0 parameters is 4. The description adds meaningful context about the tool's output (source, date, licence, citation), which is valuable beyond the empty schema. It does not need to explain any parameters because there are none.
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 provides provenance metadata (source, computation date, licence, citation) for the Soapvo dataset, and explicitly ties it to the task of correctly attributing a figure. This distinguishes it from sibling tools like dataset_row or dataset_stats, which deal with data content rather than metadata.
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 includes a direct use-case directive ('Read this to attribute a figure correctly'), which tells an agent when to invoke the tool. It does not explicitly state when not to use it or mention alternatives, but given the narrow scope and the sibling names (dataset_columns, dataset_search, etc.), the context is clear enough.
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 Soapvo 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 behavioral burden. It usefully discloses exact-match and case-insensitive behavior, but it does not state whether multiple rows can be returned, whether results are ordered, or what happens when no row matches.
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?
One compact sentence conveys the operation and its matching rules with no filler. The title also reinforces the key behavior immediately.
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?
This is adequate for a simple two-param lookup and the wording implies the return value is the matching rows. Still, the description does not cover selection guidance versus dataset_search or edge cases like no match or multiple matches, which would help an agent invoke it reliably.
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 add meaning by tying column and value to an equality comparison, but it does not elaborate on value formatting, column name semantics, or the scope of case-insensitivity.
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 title states an exact-key lookup and the description explains that it returns rows where a column equals a value exactly, case-insensitively. This distinguishes it from dataset_search by emphasizing exact matching, though it does not name that sibling tool explicitly.
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 exact-match and case-insensitive wording implies this is for precise lookups rather than fuzzy or partial searches, especially alongside dataset_search. However, there is no explicit guidance about when to prefer this tool over a sibling or what prerequisites exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Soapvo 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?
With no annotations, the description carries the behavioral disclosure burden. It usefully discloses case-insensitive matching, substring containment semantics, and a maximum of 50 results. However, it does not mention ordering, pagination, empty-result behavior, or whether the query must match whole cells versus substrings beyond the word 'contain.'
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?
One compact sentence conveys the core behavior, the matching rule, and the result cap with no filler. The most important detail—what the tool returns—is front-loaded.
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 two-parameter search tool with no output schema and no annotations, the description is largely complete: it names the dataset, defines the query semantics, and states the result limit. It could add return-format or ordering details, but nothing essential is missing for invoking the 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?
Schema description coverage is 50%: only 'query' has a schema description. The tool description adds meaning by explaining that the query is matched case-insensitively in any cell, and the 'up to 50' phrase hints at the limit parameter's cap. However, it does not explicitly state that 'limit' controls the number of returned rows, leaving some inference needed.
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 states a specific operation: returning rows of the Soapvo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes search from the sibling tools like dataset_columns, dataset_row, and dataset_stats, which have different purposes.
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 does not say when to use this tool versus the sibling tools, nor does it mention any exclusions or prerequisites. An agent must infer that this is for cell-level substring search rather than, say, fetching a specific row or computing statistics.
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 Soapvo 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 burden. It discloses parsing behavior (grouping commas and currency) and how non-numeric rows are handled (excluded and counted), which is valuable beyond the schema. It stops short of stating output shape or side effects explicitly, but the operation is read-only by nature.
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 lists the exact statistics first and then packs useful parsing details into a parenthetical. 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 aggregation tool with no output schema, the description covers the return stats and the important exclusion/formatting behaviors. It is sufficient for an agent to call the tool, though an explicit note about output shape would make it fully 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 coverage is 0%, so the description must compensate. It adds meaning to the column parameter by specifying it must be a numeric column in the Soapvo dataset. It does not explicitly state that it is a required column name, though that is present in the schema.
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 identifies the operation (computing count, min, max, mean, median, sum) and the target (a numeric column of the Soapvo dataset), which is clear. However, it does not explicitly contrast itself with sibling tools such as dataset_top or dataset_search.
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 context for using this tool is implied: when summary statistics for a numeric column are needed. There are no explicit when-not-to-use conditions, prerequisites, or references to alternative sibling tools.
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 Soapvo 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?
With no annotations, the description carries the full burden of behavioral disclosure. It explains the ranking intent but does not mention that the tool just reads data, that results are limited, that ascending defaults to false, or what fields are returned. Several important behaviors are left to the schema or to inference.
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 front-loaded sentence with no filler, and the quote makes the intent immediately relatable. It is short, though the brevity contributes to the semantic gaps in other dimensions.
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 essential ranking semantics are present, but with no output schema and no annotations, an agent is left guessing about return shape, default limit, and edge-case behavior. For a simple top-N tool this is borderline adequate but not 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 coverage is only 33% (just ascending), so the description should compensate for column and limit. It adds that column must be numeric and that ascending/descending corresponds to lowest/highest, but it never explains the limit parameter or the default/behavior of ascending beyond the schema's one-line note.
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 uses 'highest (or lowest) rows... by a numeric column' with a concrete user question ('which is the most/least X'), clearly conveying a ranking operation. It distinguishes dataset_top from statistical/summary tools such as dataset_stats and from arbitrary row lookup in dataset_row, though it doesn't explicitly name a sibling.
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 'which is the most/least X' phrasing gives a clear context for ranking questions, but the description provides no explicit when-to-use/where-not-to guidance or alternatives among the listed sibling tools. It implies usage rather than stating it.
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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Eposvo: the site's own MCP server — dataset; every answer cites the site.
Opexvo: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Each tool targets a distinct operation type (schema, provenance, exact lookup, substring search, multi-value compare, stats, top/bottom), so boundaries are mostly clear. dataset_compare is slightly vague by name but its description distinguishes it from dataset_row and dataset_search.
All tools follow a predictable `dataset_<topic>` snake_case pattern. Even though some suffixes are nouns and some are verbs, the uniform prefix and lowercase underscore style make the set feel consistent.
Seven tools is a well-scoped size for a single-dataset exploration server. Each tool serves a clear querying or metadata need without redundancy.
The toolkit covers schema, provenance, exact match, search, multi-value comparison, numeric stats, and top/bottom rows—a broad and practical surface. Obvious missing pieces are distinct-value enumeration and group-by aggregates, but most common questions can be answered with the existing tools.