Rapportvo: 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 Rapportvo 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 carries the burden of explaining behavior. It states what information is returned, which is useful, but it does not explicitly state the operation is read-only or side-effect-free. The phrasing strongly implies introspection, but leaves room for ambiguity.
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 concise and front-loaded, leading with the returned contents and adding the usage instruction. Every sentence earns its place, and there is no redundant or filler language.
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 schema-discovery tool with no parameters and no output schema, the description fully covers what is returned, the dataset it applies to, and when to invoke it. An agent has enough information to call it correctly as a first step.
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 documentation is not needed. The description adds no parameter-specific semantics, but the schema already covers this completely with an empty parameter object.
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 what the tool returns (columns, numeric flags, row count, provenance banner) for the Rapportvo dataset, and is positioned as the schema-learning entry point. It does not explicitly contrast with siblings like dataset_stats or dataset_provenance, though 'call this first' implies a distinct role.
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' provides clear timing guidance relative to other tools. It does not state when not to use it or name alternatives, but for a schema-discovery tool this is sufficient context.
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 Rapportvo 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 provided, the description carries the behavioral burden and does disclose key behavior: rows are filtered by exact value matching on a column, and the output follows the order of the provided values. It does not discuss edge cases like missing values or the exact output format, but for a simple read-style lookup tool the core behavior is 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?
The description is a single, information-dense sentence with no filler. It front-loads the result, then adds the key ordering detail and the practical use case, making every word earn 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 tool with only two simple required parameters, no output schema, and no annotations, the description covers the essential context: what is returned, how filtering works, how ordering works, and when to use it. It leaves minor gaps around edge cases and return representation, but these are not critical 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 0%, so the description must compensate. It does by explaining that 'column' is the field to match against and 'values' are the set of values to match, with their order determining output order. This adds real semantic meaning beyond the schema, which only declares types and constraints.
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 operation: return rows from the Rapportvo dataset where the specified column matches any of the given values, preserving the given order. The title 'Compare rows side by side' and the 'X vs Y' qualifier help distinguish it from generic search or row-lookup siblings, though it does not explicitly name a sibling to differentiate from.
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 gives a clear usage context: this is for 'X vs Y' comparison questions, meaning the agent should use it when it has specific column values to compare side by side. It does not mention alternative tools or exclusion criteria, but the intended use case is reasonably clear.
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 Rapportvo 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does describe what the tool returns (source, date, licence, citation) and implies a read-only nature through 'Read this', but it never explicitly states the operation is safe or non-mutating. The risk is low given zero parameters and an informational purpose, but a direct statement of non-destructiveness would strengthen it.
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 zero filler: the first lists the delivered content types, the second gives the use case. The information is front-loaded and 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?
For a zero-parameter metadata tool with no output schema, the description covers the essentials: which dataset, what information is returned (source, date, licence, citation), and when to use it. Minor gaps remain around the exact format or shape of the citation, but the content list is sufficient for an agent to invoke this tool 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 tool has zero parameters, so the baseline is 4 and there is no semantic gap for the description to fill. The description correctly spends its budget on return content and usage rather than parameters, which is the appropriate allocation for a no-input 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 the exact content delivered — source, computation date, licence, and citation — tied to the Rapportvo dataset, so an agent knows precisely what this tool returns. The verb is implicit ('Read this' rather than 'Returns...'), which keeps it a step below a fully explicit verb+resource statement, but the content list and title make the purpose unambiguous and distinguish it from all sibling tools.
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?
'Read this to attribute a figure correctly' gives a clear, actionable condition for when to invoke this tool. It does not explicitly name alternatives or exclusions, but none are needed — the siblings (columns, compare, row, search, stats, top) are functionally distinct, so the correct choice between them is evident.
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 Rapportvo 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 does reveal important behavior: the match is exact and case-insensitive. It does not disclose whether multiple rows can be returned, how column names are matched, or any pagination or error behavior, but the core lookup behavior is adequately conveyed.
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 one compact sentence with no filler; it names the dataset, the matching condition, and the case-sensitivity rule. The title adds a helpful framing as a key lookup. The only minor issue is that the description is a noun phrase rather than a clearer directive, but it is still efficiently written.
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 lookup with no output schema and no annotations, the description covers the essential calling context: dataset, column, value, and exact case-insensitive matching. However, it does not describe the return shape, the possibility of multiple matches, or how this tool relates to the search sibling, making it 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?
The schema provides only the parameter names and string types with zero description coverage, so the description is the main semantic source. 'Where a column equals a value exactly' gives meaning to both parameters and adds the case-insensitive detail. It does not clarify whether the column name itself is case-insensitive or how special characters should be handled, but for two simple string parameters this is reasonable.
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 and description clearly state that the tool retrieves rows from the Rapportvo dataset by matching a column to a value exactly. The phrase 'equals a value exactly (case-insensitive)' gives a specific matching mechanism and helps distinguish it from the likely fuzzy dataset_search sibling. The wording is slightly awkward as a noun phrase rather than an imperative, but the intent 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?
The description implies the use case: when you need rows where a column exactly equals a value. However, it does not explicitly say when not to use it or mention alternatives such as dataset_search for partial or approximate matches, so the routing decision is left mostly to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Rapportvo 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 provided, the description properly carries the behavioral burden. It discloses case-insensitive matching, that matching applies to any cell, and that results are capped at 50. Minor gaps remain, such as result ordering and behavior when no cells match, but the key invocation-relevant behavior is 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?
One sentence with no filler; the resource, matching behavior, case sensitivity, and row cap are all front-loaded and useful.
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, the description plus schema is nearly sufficient: the agent knows what to pass, how matching works, and the result limit. It does not describe the output shape or ordering, but no output schema exists and the return concept of 'rows' is implied.
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 50% because limit has no description, but the description compensates by explaining the search is case-insensitive and that results are 'up to 50'. This adds meaning beyond the schema for both query and limit.
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 exactly what the tool returns: rows of the Rapportvo dataset whose cells contain a query, with case-insensitive matching and a cap of 50. This makes the tool's function clear, though it does not explicitly contrast it with sibling tools such as dataset_row or dataset_top.
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 wording establishes a clear use case: search for dataset rows by content across all cells. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to prefer this over row/stats/column tools.
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 Rapportvo 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 and does useful work: it discloses that grouping commas and currency symbols are parsed and that non-numeric rows are excluded and counted. It does not describe the exact return shape or behavior on an empty/absent column, but the most important parsing and filtering behaviors are 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 sentence with a parenthetical packs all essential information without wordiness, and the returned statistics are listed first.
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, the description is nearly complete: it defines the parameter, the computed values, and edge-case handling. It could briefly state the result object structure, but the listed statistics make the return value largely inferable.
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 only defines 'column' as a non-empty string, so the description adds crucial meaning: the column must be numeric, and its values undergo comma/currency normalization. This compensates for the 0% schema description 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 concrete resource (numeric column of the Rapportvo dataset) and the exact computed outputs (count, min, max, mean, median, sum), making the tool's function unambiguous. This is enough to distinguish it from siblings such as dataset_top and dataset_compare, which serve different analytical 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?
It clearly implies this is the tool to call when summary statistics for a numeric column are needed and includes parsing caveats. It does not explicitly name excluded use cases or alternatives, but the context is clear.
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 Rapportvo 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, and it only states that the tool returns the highest or lowest rows. It does not mention default limits, handling of non-numeric columns, tie-breaking, or what fields the returned rows contain. The 'numeric column' hint is useful but incomplete.
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 efficient sentence that front-loads the core purpose and includes a user-oriented framing. It is not bloated, though it could integrate a bit more detail about defaults without becoming verbose.
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 ranking tool, the description covers the essential idea and names the key column parameter type. However, with no output schema and no annotations, it should ideally clarify the default limit, the meaning of 'ascending' defaults, and what the returned rows include. It is adequate but has noticeable gaps.
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 only 33% (only 'ascending' is described), so the description must compensate. It does add meaning by specifying that the column must be numeric and explains highest/lowest conceptually, but it leaves 'limit' semantics and the default ordering behavior to the schema, which is only partially documented.
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 a specific action ('rank rows') and a resource ('by a numeric column') and even frames it as a natural-language question ('which is the most/least X'). This distinguishes it from sibling tools like dataset_search or dataset_stats, though it does not name an alternative 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 question format 'which is the most/least X' suggests when the tool is appropriate, giving clear context for ranking use cases. However, it does not explicitly say when not to use it or mention alternatives such as dataset_stats or dataset_search, 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.
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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Most tools are clearly distinct query modes, but dataset_columns includes a provenance banner that slightly overlaps with dataset_provenance, and dataset_row/dataset_compare both support exact-value filtering on a column. These boundaries are manageable but not perfectly crisp.
All tools share the dataset_ prefix and snake_case style, which is predictable and consistent. However, the second part mixes verbs (compare, search), nouns (columns, provenance, row), and adjectives/nouns (top, stats), so there is no uniform verb_noun pattern.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct type of data access or metadata need without feeling padded or redundant.
The surface covers schema, provenance, exact lookup, substring search, multi-value comparison, statistical summaries, and top/bottom ranking for the dataset. It lacks generalized arbitrary filtering or full row pagination, but the core read-only dataset workflow is well covered.