Clientvo: 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 Clientvo 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?
No annotations are provided, so the description carries the full disclosure burden. It clearly states what the call returns, including provenance banner and row count, and for a zero-parameter introspection tool there are no hidden mutation or cost concerns needing disclosure.
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 focused sentence delivers the key output details and usage instruction without repetition or filler. The content is front-loaded and every part contributes 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 zero-parameter metadata tool with no output schema, the description gives enough information to invoke it confidently and interpret the result. It could be slightly richer by describing the output shape or naming related siblings, but it is functionally 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?
The tool has zero parameters, so parameter semantics are not applicable in a meaningful way. The description accurately describes what the no-argument call will return, meeting the baseline expected for a parameterless tool.
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 concrete outputs: columns, numeric indicators, row count, and provenance banner. It also states a clear role—'Call this first to learn the schema'—so an agent can understand exactly what this tool provides and how it fits into a workflow.
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 instruction 'Call this first to learn the schema' explicitly positions this tool as the initial schema-discovery step. It does not enumerate sibling alternatives or explicit when-not-to-use cases, but the ordering guidance is strong and actionable.
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 Clientvo 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 carries the behavioral disclosure burden. It does disclose row selection and order preservation, which are meaningful behaviors. But it does not explain output format, exact-match/case sensitivity, or what happens when a value has no matching rows.
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 compact sentence that includes the resource, filtering logic, ordering behavior, and intended use-case. The phrasing 'whose column is any of the given values' is slightly awkward, but every component earns its place and there is no redundant 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 simple two-parameter tool with low schema richness, the description covers the essential invocation semantics: what dataset, which column, what values, and the ordering behavior. No output schema exists, but the result type (rows for comparison) is reasonably implied by the title and description.
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 effectively communicates that 'column' is the field to match against and 'values' is the list of allowed values, and it clarifies the relationship between them. It does not explicitly name the parameters, but the relational semantics are clear.
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 identifies the resource (Clientvo dataset), the operation (select rows where a column matches any of the given values), and the ordering behavior (in the order given). It lacks an explicit imperative verb like 'returns' or 'retrieves', but the intent is unambiguous and distinct enough even without sibling names.
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' gives useful usage context, implying the tool is for comparing rows for two or more values. However, it does not state when not to use it, mention alternatives, or describe prerequisite conditions.
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 Clientvo 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, the description carries the full burden and it does convey the read-only nature through 'Read this' and the list of metadata fields it returns. It doesn't mention output formatting or error cases, but for a simple provenance lookup this is reasonably transparent.
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?
Two short sentences with no filler. The substantive content is front-loaded in the first sentence, and the usage instruction adds a concrete purpose in the second.
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 no-parameter provenance metadata tool, the description tells the agent what data is returned (source, date, licence, citation) and why to invoke it. There is no output schema, so the description's list of returned elements is sufficient.
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?
There are zero parameters, so the baseline is 4. The description correctly avoids inventing parameter details and instead focuses on what the tool returns.
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 identifies the tool as providing provenance metadata—source, computation date, licence, and citation—for a specific dataset. The phrase 'Read this to attribute a figure correctly' leaves no ambiguity about the tool's purpose, and none of the sibling tools (columns, row, stats, search, etc.) claim this responsibility.
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 tells the agent when to use it: 'Read this to attribute a figure correctly.' It does not name alternatives or negative conditions, but for a zero-parameter metadata tool alongside data-query siblings, the intended 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 Clientvo 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 burden and does disclose one genuine behavioral trait: case-insensitive exact matching. However, it says nothing about behavior on zero matches, whether a single row or many rows are returned, or any limits. It is a reasonable but incomplete disclosure.
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 with zero filler; every phrase ('where a column equals', 'value exactly', 'case-insensitive') carries meaning and the core matching semantic is front-loaded. This is appropriately sized for a simple two-parameter lookup tool.
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 two-parameter tool with no output schema and no annotations, the description covers the core lookup contract, and the plural 'rows' hints at a multi-row return. It omits error/no-match behavior and return format, but for a simple query tool the essential context is present.
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 maps both parameters to the operation: 'column' is the field being checked and 'value' is the exact value to match. This is enough to infer the parameters' roles, though no extra detail like type formats or edge cases is provided.
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 clear verb+resource: it looks up rows in the Clientvo dataset by matching a column against an exact value. The phrase 'exactly (case-insensitive)' sharpens the semantics beyond the tool name. It doesn't explicitly distinguish this from sibling dataset_search, but the 'exact key' framing in the title gives enough separation.
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 prefer this tool over siblings like dataset_search, dataset_columns, or dataset_compare. The exact-vs-fuzzy distinction from dataset_search is implied but never stated, so an agent must infer the selection criteria on its own.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Clientvo 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 burden. It discloses case-insensitive matching, the row-based return scope, and the 50-row cap, which is useful behavior. However, it does not mention what happens when the query matches no rows, whether pagination is supported, or any performance implications.
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 sentence, front-loaded with the action and scope, and includes the key constraint (case-insensitive, up to 50). Every word 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?
Given 2 parameters and no output schema, the description covers the core behavior but misses details like default limit behavior, empty result handling, and sorting. It is adequate for a simple search tool but leaves some practical questions unanswered.
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%; the 'query' parameter is documented in schema as 'text to look for in any cell', and the description adds the case-insensitive and row-context detail. The 'limit' parameter is not described in the schema, and the description only implies a cap of 50, not how the limit parameter behaves or its interaction with the cap. This is a partial gap.
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 verb 'search' and the resource (Clientvo dataset rows), and specifies the case-insensitive matching behavior. However, it does not explicitly differentiate from sibling tools beyond the scope of this dataset, though the name 'dataset_search' helps.
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 usage: search within a dataset when you need rows matching a query. It does not explicitly state when to use alternatives like dataset_columns or dataset_stats, nor any exclusions. The 'up to 50' limit provides some context but not explicit usage guidance.
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 Clientvo 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 behavioral disclosure burden. It reveals important handling behavior: grouping commas and currency are normalized, and non-numeric rows are excluded and counted. It also enumerates the exact computed values, which substitutes for the missing output schema. It does not mention error cases or assumptions about missing data, but the stated behavior is useful and non-obvious.
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, information-dense sentence with no filler. The main statistics list is front-loaded, and the parsing caveats are neatly placed in parentheses. Every part 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 one-parameter tool with no annotations and no output schema, this description covers the core information: what the tool computes, for which dataset, and how edge-case rows are treated. It could be slightly more complete by explicitly naming the 'column' parameter and describing the response shape, but the description already conveys most of what an agent needs.
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 provides only the parameter name 'column' with a minLength constraint and no description, so the description must add meaning. It does clarify that the column is expected to be numeric and that non-numeric rows are handled specially. However, it does not explicitly connect this meaning to the 'column' parameter or describe required formats, leaving some ambiguity about how the column is referenced.
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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Clientvo dataset. This is a specific verb-plus-resource description that distinguishes it from sibling tools like dataset_top, dataset_row, and dataset_search, though it does not explicitly contrast with them.
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 intended use is implied: use this tool when you need numeric summary statistics for a column in the Clientvo dataset. However, there is no explicit guidance on when to prefer this over sibling tools such as dataset_top or dataset_compare, and no exclusions or alternative routing.
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 Clientvo 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, but it only states the basic ordering behavior. It does not disclose default sort order, how the limit is applied, tie-handling, whether the column must be strictly numeric, or what the return format looks like. This is a significant gap for a read tool 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, front-loaded sentence with a helpful explanatory quote. There is no filler, and it communicates the core operation efficiently, though the trailing dash and quote add slight stylistic complexity without harming clarity.
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 no annotations, no output schema, and only 33% schema description coverage, the description is too minimal. It omits key operational details like the default sort order, the meaning of the limit parameter, and what kind of result is returned, making it incomplete for confident 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?
Schema coverage is only 33% (only 'ascending' has a description). The description adds meaning for the 'column' parameter by indicating it must be numeric, but it says nothing about 'limit' or its maximum value, and it does not clarify the default ordering beyond what the schema already states. It does not sufficiently compensate for the low schema 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 clearly states that the tool ranks rows of the Clientvo dataset by a numeric column and identifies the use case as 'which is the most/least X'. It is easy to distinguish from siblings like dataset_search or dataset_stats, though it does not explicitly name alternatives.
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 it (when you need the highest or lowest rows by a numeric column) but provides no explicit guidance on when not to use it or how it compares to sibling tools like dataset_compare or dataset_row. It is usable context but leaves exclusion criteria unstated.
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 maps to a clear query type (schema, metadata, exact lookup, substring search, multi-value compare, stats, top-N) with only minor overlap between dataset_row and dataset_compare for single-value filtering. The examples in the descriptions help an agent choose correctly.
All seven tools share the dataset_ prefix and use consistent snake_case with a noun/operation suffix (columns, compare, provenance, row, search, stats, top). This creates a predictable pattern that is easy to extend and select from.
Seven tools is appropriate for a dataset query server: enough to cover common exploration tasks without being overwhelming. Each tool has a distinct role, so the count feels well-scoped rather than padded.
The set covers schema discovery, provenance, exact and fuzzy lookup, comparisons, numeric aggregates, and top/bottom queries — the core operations for a single dataset Q&A server. It lacks a direct 'count rows matching a filter' or distinct-values tool, but agents can work around those with existing tools.