Subbielane: 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 Subbielane 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 full burden of behavioral disclosure. It states the specific information returned (columns, numeric flags, row count, provenance banner), which is a clear behavioral specification. It implies a read-only operation by describing it as learning the schema, though it doesn't explicitly state that no side effects occur. This is adequate given the tool's simplicity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences that front-load the key content (columns, numeric flags, row count, provenance banner) and immediately give the usage directive. There is zero wasted wording, 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?
Given the tool has no parameters, no output schema, and a simple introspective purpose, the description is nearly complete. It states exactly what information will be returned and when to call it. A slight gap is that it doesn't describe the format of the provenance banner or how the numeric flags are represented, but for a schema-introspection tool this is minor and not blocking.
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 for this dimension is 4 per the rubric. The empty schema already indicates no inputs are required, and the description adds no parameter-related information. Since there are no parameters to document, a 4 is appropriate.
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: columns, numeric flags, row count, and provenance banner. It names the specific dataset (Subbielane) and indicates it's a schema-learning tool, which distinguishes it from the sibling tools that compare, search, or compute stats. However, it does not explicitly contrast with siblings like dataset_provenance or dataset_stats, so it's clear but not fully differentiating.
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 explicit usage guidance: 'Call this first to learn the schema.' This tells the agent when to invoke it, establishing it as the initial step. It does not mention when not to use it or name alternative tools, but the instruction is clear and contextually relevant.
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 Subbielane 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 must carry the full burden of behavioral disclosure. It reveals that output order follows the given values and that matching is 'any of' (inclusive), but it does not mention what happens on no matches, whether matching is case-sensitive, or the structure of returned rows. These gaps are significant for an agent deciding whether this tool fits.
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 behavior and includes a usage hint. There is no fluff; every word contributes to understanding the tool's function. It is appropriately sized for the tool's simplicity.
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 the essential behavior (filter rows by column values, preserve order) and hints at the intended use case. However, it lacks details on edge cases (empty results, invalid column) and does not explicitly distinguish from sibling tools, leaving some ambiguity about when to use it. It is adequate but not 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 is the only source of parameter meaning. It explains that 'column' is the field to filter on and 'values' are the list of accepted values, and that output order follows the values order. This adds meaning beyond the schema, but it doesn't clarify constraints like min/max items or exact-match semantics. Adequate but not exhaustive.
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 action: returning rows of the Subbielane dataset where a column matches any of given values, preserving order. It clearly indicates the resource and behavior, and the phrase 'for "X vs Y" questions' hints at its comparison use case. It doesn't explicitly differentiate from siblings like dataset_search, but the core purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear context (comparison questions) but does not explicitly state when to prefer this over alternatives or provide exclusions. It implies usage for side-by-side comparisons but doesn't name sibling tools or conditions when they should be used instead. This is adequate but not explicit.
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 Subbielane 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 disclosure burden. It conveys that this is a read-only provenance lookup by listing returned metadata and instructing the agent to read it. It does not explicitly state 'no side effects', but the content and instruction make that sufficiently clear.
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 content list is front-loaded and the usage instruction is placed second, making each sentence 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 parameterless provenance tool with no output schema, the description fully covers what the agent needs: the fields returned and the reason to call it. Nothing essential is missing.
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 and the schema is empty, so the baseline is 4. The description adds no parameter details, but none are 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?
States a specific resource (the Subbielane dataset) and the exact content delivered: source, computed date, licence, and citation. This differentiates it clearly from sibling tools like dataset_columns or dataset_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent when to use it: 'Read this to attribute a figure correctly.' It does not list exclusions or name alternatives, but the context is clear and self-sufficient.
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 Subbielane 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. It discloses case-insensitivity but omits other behaviors: whether it returns all matching rows or just one, what happens on no match, read-only nature, pagination, or output format. For a data retrieval tool, this is insufficient 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?
The description is a single sentence, concise and to the point. It front-loads the core behavior (matching rows) and adds the case-insensitive detail. No wasted words, though it could be more structured with clearer phrasing.
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 output schema and no annotations, the description is incomplete for a tool that returns data. It lacks any indication of return shape, handling of multiple matches, error cases, or read-only safety. An agent would be uncertain about the exact output and edge cases, making the tool risky to invoke blindly.
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 add meaning. It does clarify that 'column' refers to a dataset column and 'value' is the comparison target, but it does not specify column-name syntax, case-sensitivity of column names, or any constraints. It adds modest value beyond the raw 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 clearly states the tool returns rows from a dataset that match a column-value pair exactly, with case-insensitivity. It distinguishes itself from dataset_search by implying exact matching, though it doesn't name the sibling. The verb 'look up' and resource 'rows' are specific enough.
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 explicit guidance on when to use this tool versus alternatives like dataset_search. The description implies exact-match behavior, but it does not state conditions or exclusions, nor does it reference sibling tools. An agent must infer that this is for exact lookups rather than broader search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Subbielane 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 must carry the behavioral disclosure burden. It adds useful detail—case-insensitive matching and a 50-row cap—but omits other behavioral traits such as default limit behavior, zero-result handling, or whether matching is substring-based. It is partial rather than comprehensive.
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, 13 words, front-loaded with the result type. No filler or redundant phrasing; every word contributes to the tool's purpose and key behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with no output schema and no annotations, the description conveys the essential behavior: what is searched, how matching works, and the result cap. It does not mention the limit parameter explicitly, but the input schema covers that; overall the definition is close to complete for the tool's complexity.
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 already describes query as 'text to look for in any cell' and includes limit with maximum 50. The description's 'cells contain the query' and 'up to 50' mostly restate that existing schema information, adding no meaningful new meaning about the limit parameter or how it controls results.
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 (search rows) on a specific resource (the Subbielane dataset) with precise behavioral constraints (case-insensitive, up to 50 rows). This clearly differentiates it from siblings like dataset_row (which fetches a specific row) and dataset_top (which summarizes data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus its alternatives. It does not mention dataset_row, dataset_stats, or any other sibling, nor does it state conditions that would make search preferable over other operations.
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 Subbielane 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 full burden. It does disclose useful behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it does not mention the return format, whether 'count' refers to total rows or valid numeric rows, or error handling for missing/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, focused sentence that front-loads the computed statistics and then adds edge-case handling with no wasted words. It is appropriately sized for a one-parameter 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?
With no output schema and no annotations, the description should fully define what the agent gets back. It lists the statistics but leaves ambiguity about the output structure and the exact meaning of 'count' relative to excluded non-numeric rows, so the agent still has some uncertainty before calling.
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 for the undocumented 'column' parameter. It clarifies that the column should be numeric and part of the Subbielane dataset, but it does not explain exact column naming, case sensitivity, or how to discover available column names (e.g., via dataset_columns).
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 exact statistics computed (count, min, max, mean, median, sum) for a numeric column of the Subbielane dataset, and the title reinforces this. This distinguishes it from siblings like dataset_row, dataset_top, and dataset_compare.
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 about when to use this tool versus its siblings, and no exclusions or alternatives are mentioned. The only usage cue is the generic 'numeric column' context, which does not help an agent route among dataset_columns, dataset_row, dataset_top, etc.
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 Subbielane 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 disclosure. It conveys the core behavior (sort by a numeric column and return top/bottom rows) but omits important details such as the default limit, default ascending direction, tie-handling, output shape, and behavior on non-numeric 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 focused sentence with a useful usage example. It is front-loaded with the core operation and contains no filler, making it efficient and easy to parse.
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 tool with no output schema and no annotations, the description should state what the result contains and the default behavior. It only says 'rows' without specifying output format, default limit, or ordering defaults, so an agent could call it with incomplete expectations.
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?
Only one of three parameters has a schema description, so the description must compensate. It adds meaning for 'column' as numeric and for 'ascending' via highest/lowest, but it does not clarify the 'limit' parameter or its default, leaving a notable 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 states a specific verb and resource: returning 'the highest (or lowest) rows' of the dataset by a numeric column, reinforced by the title 'Rank rows by a numeric column'. This clearly differentiates it from siblings like dataset_search or dataset_stats, though it does not name them 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 phrase 'which is the most/least X' provides a practical query template, implying when the tool is useful. However, it offers no explicit guidance on when to prefer this over sibling tools or any exclusions, leaving the decision partly to inference.
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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Sudslane: 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 on the Subbielane dataset: schema, provenance, exact row match, fuzzy search, statistics, top/bottom, and value-list comparison. Some mild overlap exists between dataset_search, dataset_row, and dataset_compare for lookups, but their matching semantics are clearly differentiated.
All tools follow the consistent 'dataset_' prefix followed by a short, descriptive noun or verb. The naming convention is uniform and predictable, making it easy to infer each tool's purpose.
Seven tools is a well-scoped number for a dataset-querying server. Each tool covers a distinct query pattern without redundancy or bloat.
The tool surface covers the core dataset exploration needs: schema, provenance, exact lookup, search, statistics, extremes, and comparisons. A minor gap is the lack of a tool to list all rows or paginate through the dataset without a filter, but most practical questions can be answered.