Csatzo: 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 Csatzo 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 carries the burden of disclosing behavior. It clearly enumerates the expected return content (columns, numeric indicators, row count, provenance banner) and implies a read-only, schema-discovery operation. It does not mention side effects, but none are expected for a schema inspector.
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 tight sentences: first sentence lists the output fields, second sentence gives the usage directive. No filler, and the most important content 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 zero-parameter, no-output-schema tool, the description gives an agent enough to decide when to call it and what to expect. It could add a bit more detail about the exact output format, but that is not essential given the simplicity of the tool.
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 properties, so there are no parameters to document. The baseline score of 4 applies; the description adds no unnecessary parameter information.
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 specifies exactly what the tool returns: the columns, numeric flags, row count, and provenance banner of the Csatzo dataset. It adds a clear action ('Call this first to learn the schema') and distinguishes itself from sibling tools like dataset_provenance and dataset_stats by focusing on schema/shape information.
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 explicitly tells the agent to use this tool first when needing to learn the schema of the Csatzo dataset. However, it does not name alternative tools or state when not to use it, so it lacks explicit exclusion guidance.
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 Csatzo 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 full burden, and it does disclose a genuinely non-obvious trait: result rows come back in the order of the given values rather than the dataset's natural order or alphabetical order. It also implies read-only behavior by describing a data retrieval. However, it omits match semantics (exact equality vs substring, case sensitivity), empty-result behavior, and output shape.
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 no filler; the core behavior (filtering and ordering) is front-loaded and the usage purpose is appended at the end. Every clause earns its place. The only slight inefficiency is the em-dash usage clause, which adds color but could arguably be merged more cleanly.
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 required flat parameters, no nested objects — so the description covers the main call shape. Yet with zero annotations, zero schema descriptions, and no output schema, meaningful gaps remain for an agent: exact-match vs partial-match behavior, case sensitivity, and what happens when no rows match. The description is adequate for a straightforward retrieval tool but leans on the agent to infer several conventions.
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, and it partially does: it identifies 'column' as the filter field and 'values' as the match list, notably tying the output row order to the order of the values array — a semantic the bare schema cannot convey. But it leaves static the exact-match semantics and does not clarify how the two parameters interact beyond the filter/order relationship.
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 behavior: retrieve rows of the Csatzo dataset filtered by a column matching any of given values, preserving the values' order. This distinguishes it from siblings like dataset_top (ranking) and dataset_row (single record), and the 'X vs Y comparison' framing carves a distinct niche. However, it never explicitly names a sibling or states what it is not, and the title's 'compare' metaphor is less precise than the filtering behavior described.
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 trailing clause 'for "X vs Y" questions' gives implied usage context — use it when the goal is side-by-side comparison of a small set of specific values. But there is no explicit when-to-use vs alternatives, no exclusion criteria, and no mention of sibling tools like dataset_search for fuzzy or arbitrary queries. The guidance is suggestive rather than directive.
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 Csatzo 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 disclosure burden. It communicates that the tool returns provenance-related information and implies a read-only operation, but it does not explicitly state side-effect-free behavior or explain what the response format will look like.
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 concise sentences convey the content, purpose, and intended use without repetition or filler. The key information 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 zero-parameter provenance metadata tool, the description is sufficiently complete: it lists the key information fields and explains the practical use. The lack of an output schema is not a major gap here since the field list is already stated.
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 a concern. The description does not need to compensate for any schema gaps, and the baseline of 4 applies.
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 as provenance metadata for the Csatzo dataset and enumerates the specific attributes (source, computed date, licence, citation). It lacks an explicit verb like 'get' or 'retrieve,' but the intent is unambiguous and distinct from the 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?
The description states a clear use case: 'Read this to attribute a figure correctly.' It does not explicitly contrast with sibling tools, but the provenance-specific content makes the appropriate context 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 Csatzo 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 provided, the description carries the burden of behavioral disclosure. It does add a meaningful behavioral detail: the match is exact but case-insensitive. However, it does not state what happens on no match or multiple matches, nor any return format or error behavior, so coverage is only partial.
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. The title and description align, and every word contributes to explaining the tool's behavior. This is an appropriately sized definition for a simple 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?
The tool is simple and the description covers the core matching semantics, but with no output schema and no annotations, return shape and edge-case behavior are left implicit. Given the sibling set, an agent might still need to infer how results are presented and how this differs from dataset_search, so it is minimally complete but has 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?
The schema provides only string type and minLength for column and value, with no descriptions. The description adds semantics by explaining that a column is compared to a value for exact, case-insensitive matching. This gives the parameters clear roles, but it lacks examples or constraints like whether column must be an existing column name, so it does not fully compensate 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 clearly states an action (returns rows) and a resource (Csatzo dataset), qualified by an exact match on a column/value pair with case-insensitivity. It is not a tautology and is more specific than a generic search, but it does not explicitly contrast itself with sibling tools such as dataset_search, so it misses the full differentiation needed for a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives like dataset_search or dataset_compare. The title hints at exact-key lookup, but the description does not state prerequisites, exclusions, or which sibling tool to use for fuzzy or non-exact lookups, leaving the choice 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 Csatzo 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 behavioral burden. It discloses case-insensitive matching and the 50-row limit, but does not mention return format, default limit behavior, what happens with no matches, or whether all columns are searched.
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, information-dense sentence covers the resource, matching rule, case sensitivity, and result cap. No filler or repetition.
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 search tool with no output schema, the description covers the essential semantics: row-level search, query matching, case-insensitivity, and result cap. Minor missing details like default limit or sorting keep it from a 5.
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 50%; the query parameter is described in the schema, and the description adds case-insensitivity. The limit parameter is not described in the schema, but its maximum of 50 is mirrored in the description. The description adds only modest value beyond 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 states a specific operation: returning rows of the Csatzo dataset whose cells contain the query, with case-insensitivity and a 50-row cap. This clearly distinguishes it from sibling tools like dataset_row, dataset_stats, or dataset_columns.
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 a clear use case: search for rows matching text in any cell. It does not explicitly mention alternatives or when not to use it, but the context is clear enough for an agent to select it for row-level text matching.
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 Csatzo 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?
No annotations are provided, so the description carries the disclosure burden. It usefully reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. It does not specify exact output structure or edge-case behavior, but it goes beyond a simple restatement.
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 statistic list and then adds edge-case handling, making it easy to scan and understand quickly.
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 single-parameter read tool with no output schema, the description is mostly complete: it names all computed values and key data-handling behavior. It could be more explicit about the response format and behavior for an all-non-numeric or empty column, but these are minor 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?
The input schema only defines column with minLength 1, so schema coverage is minimal. The description adds important meaning: the column is numeric and non-numeric values are handled in a specific way. It does not explain column name syntax, but the single-parameter context makes this adequate.
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?
Title and description explicitly state that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Csatzo dataset. This clearly distinguishes it from siblings like dataset_top or 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?
Usage context is implied: use when summary statistics for a numeric column are needed. However, the description does not explicitly mention alternatives or when not to use it, leaving some differentiation 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 Csatzo 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, and it does convey the core behavior: selecting rows by top/bottom numeric ranking. However, it leaves key behavioral details unstated, such as whether full rows are returned, how ties or nulls are handled, and whether the operation is strictly read-only.
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 front-loads the core ranking purpose and adds a clarifying 'which is the most/least X' gloss. There is no filler or redundant restatement of the tool name.
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 ranked-rows tool, the description plus schema provides the minimum needed to make a call: the numeric column, optional limit, and ordering flag are present. But there is no output schema, no default-limit information, and no discussion of return shape or edge cases, so the agent is left with assumptions.
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 adds useful meaning by specifying that the column must be numeric and that highest/lowest ordering is the operation, complementing the sparse schema (only 'ascending' has a description). It does not explain the 'limit' parameter beyond its name, and overall schema coverage is only 33%, so compensation is partial.
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 the highest or lowest rows of the Csatzo dataset based on a numeric column, and the title adds the 'rank rows' framing. It does not explicitly differentiate itself from sibling tools like dataset_stats or dataset_search, so it misses the top criterion for a 5.
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 a use case — finding the most/least X — but gives no explicit guidance on when to choose this over dataset_row, dataset_search, or dataset_stats. There are no exclusions, prerequisites, or alternative tool mentions, so an agent must infer the boundary from sibling names.
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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Mandatzo: the site's own MCP server — dataset; every answer cites the site.
Reputzo: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Most tools are clearly distinct: schema, provenance, stats, and top each have a unique purpose. The row-returning tools (dataset_row, dataset_search, dataset_compare) could potentially be confused, but their exact-match, contains-search, and multi-value-ordering semantics are described clearly enough to prevent serious misselection.
All seven tools follow the predictable dataset_<operation> pattern, making the set easy to scan and understand. There is no mixing of naming conventions or styles.
Seven tools is a well-scoped size for a dataset exploration server. Each tool covers a distinct useful operation without redundancy or bloat.
The tool set covers the full read-only lifecycle of working with the Csatzo dataset: schema discovery, provenance, exact lookup, free-text search, comparisons, summary statistics, and top/bottom rows. No obvious missing operation would prevent an agent from answering typical questions about the data.