Turndownly: 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 Turndownly 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 explaining what the tool returns. It lists the output components but does not state whether the call is read-only, cheap, or how the data is presented, leaving some behavior implicit.
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 that front-loads the key outputs and ends with a clear call-to-action. 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 0-parameter schema-introspection tool, the description tells the agent what information will be returned and when to call it. It does not describe the exact response shape, but that is not necessary to invoke the tool successfully.
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 coverage is effectively complete, 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?
The description names a specific, concrete result set: columns, their numeric status, row count, and provenance banner. It also marks the tool as the first schema-learning call, which clearly separates it from siblings like dataset_row and 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?
'Call this first to learn the schema' is an explicit usage directive. It lacks when-not-to-use guidance or alternatives, but for a lightweight 0-parameter introspection 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 Turndownly 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 reveal the key selection and ordering behavior, but it leaves the actual output format, exact-match semantics, duplicate handling, and empty-result behavior implicit.
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 entire description is one dense sentence that front-loads the operation and ordering, then ends with the use case. There is no filler, no repetition of schema information, and every part contributes to understanding.
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 small two-parameter tool the core invocation logic is covered, but with no output schema and no annotations, the description omits the actual side-by-side return format and edge-case behavior. An agent can invoke it, but may not know exactly what response shape to expect.
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%, and the description compensates by mapping both parameters: 'column' is the field whose values are matched, and 'values' are the row-selector values that also determine output order. It does not provide exhaustive detail, but it adds real meaning beyond the bare JSON 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: return rows of 'the Turndownly dataset' using a filter column and a set of values, preserving the given value order. This clearly distinguishes it from siblings like dataset_row, dataset_search, or dataset_top by its value-based, order-preserving comparison behavior.
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 an explicit, recognizable use case for when to invoke this tool. It does not enumerate alternatives or state when not to use it, so it stops short of the full 5.
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 Turndownly dataset. Read this to attribute a figure correctly.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that the tool returns provenance metadata and is meant to be read, implying a read-only operation, but it does not clarify output format, whether the citation is ready-to-use text, or any other behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no redundant wording. The most important information—what provenance fields are provided—is front-loaded, and the usage directive is concise and actionable.
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's simplicity (zero parameters, no annotations, no output schema), the description adequately covers what the agent needs to invoke it and interpret the result: the source, computed date, licence, and citation. It could be more explicit about the return format, but this is a minor gap for such a low-complexity 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 tool has zero parameters and 100% schema description coverage, so the baseline is 4. There are no parameter semantics to explain, and the description appropriately focuses on what the tool returns rather than on inputs.
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 specific resource (the Turndownly dataset) and the exact provenance fields it provides: source, computed date, licence, and citation. This clearly distinguishes it from sibling tools like dataset_columns or dataset_search, which are about data structure and lookup rather than attribution.
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 states the intended use case: 'Read this to attribute a figure correctly.' This gives the agent a clear cue for when to invoke the tool, though it does not explicitly mention when not to use it or contrast it with alternatives.
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 Turndownly 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 must carry the full behavioral burden. It discloses the case-insensitive matching behavior, which is a key trait, but leaves other behaviors ambiguous: the title says 'a row' (singular) while the description says 'rows' (plural), so it's unclear whether all matches are returned or just one. It also does not mention handling of no matches, ordering, or any limits. The single behavioral detail provided is useful but the description is not 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?
The description is a single, focused sentence that immediately states the core function. It front-loads the action ('rows ... where a column equals a value') and adds the critical case-insensitive qualifier. There is no redundant wording or irrelevant details, making it highly concise and well-structured.
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's simplicity (two parameters, no output schema), the description covers the essential lookup logic but has notable gaps: it does not specify whether multiple matching rows are returned or just one, does not mention behavior for no matches, and does not clarify if column names are case-sensitive. For a simple tool these omissions are acceptable but not ideal, so a mid-range score is appropriate.
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 0% description coverage, so the description must clarify parameter meanings. It does relate 'column' and 'value' by stating 'where a column equals a value,' giving basic semantic context. However, it does not explain that 'column' refers to a specific column name in the dataset or whether the value can be non-string types, nor does it mention case-sensitivity of the column name. This adds some meaning but not enough to fully compensate for the absent schema descriptions.
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's purpose: retrieve rows from the Turndownly dataset where a specified column equals a given value, with the exact-match condition and case-insensitive behavior explicitly noted. This distinguishes it from sibling tools like dataset_search (likely fuzzy or broader) and dataset_top (which returns top rows). The verb 'look up' plus the resource and criteria make the function 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 no guidance on when to prefer this tool over alternatives. It does not mention scenarios such as exact-match lookups versus fuzzy searches, nor does it reference sibling tools like dataset_search or dataset_top. The agent is left to infer usage context from the tool name and description alone, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Turndownly 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 carries the full burden and does disclose key behavior: case-insensitive substring matching across all cells and an output limit of 50 rows. It does not mention ordering, default limit behavior, or empty-result handling, but the core selection-relevant behaviors are explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that contains no filler. Every word earns its place and the most important scoping information appears 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?
Given the low complexity of a two-parameter search tool, the description covers the required query, optional limit, match semantics, and maximum result size. The lack of an output schema and a few minor unspecified behaviors (ordering, default limit) remain, but they are not critical for selecting or invoking this 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 schema already documents the query parameter ('text to look for in any cell'), while the limit parameter has no schema description. The description adds 'case-insensitive' and 'up to 50' — useful context for query semantics and the result cap — but it does not clarify the limit parameter's default or behavior beyond the schema's min/max.
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 does: returns rows of the Turndownly dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This is specific and clearly distinguishes it from sibling tools like 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 wording implies a text-search use case, but no explicit guidance is given about when to choose this over siblings like dataset_row or dataset_provenance. There are no exclusion conditions or alternative tool mentions, so an agent must infer usage from the description alone.
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 Turndownly 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 discloses important data-cleaning behavior: grouping commas and currency symbols are parsed, and non-numeric rows are excluded and counted. This goes beyond a generic 'computes statistics' statement and gives the agent useful expectations about edge cases.
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 that front-loads the returned statistics and adds edge-case handling in a parenthetical. No words are wasted and every clause contributes useful information.
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 read-only tool, the description covers the input semantics and the list of computed values. However, with no output schema, it does not state the exact return shape (e.g., whether the result is an object with those keys), and it omits failure behavior such as what happens when the column does not exist or contains no numeric rows.
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?
Since schema description coverage is 0%, the description must compensate for the bare 'column' parameter. It adds semantics by explaining that the column must be numeric, that formatting like commas and currency is normalized, and that non-numeric values are excluded. This is meaningful interpretation 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 names a concrete operation—computing summary statistics for a numeric column—and lists exactly which statistics are returned: count, min, max, mean, median, and sum. This uniquely distinguishes it from siblings like dataset_row, dataset_search, and dataset_compare, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied clearly: when an agent needs numeric summary statistics for a column in the Turndownly dataset. However, it does not explicitly state when NOT to use this tool or point to an alternative for other types of queries, so the routing guidance is implicit rather than explicit.
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 Turndownly 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?
No annotations are provided, so the description carries the full behavioral burden. It discloses only that ranking is involved; it does not state whether the operation is read-only, what the output shape is, how limit defaults work, or how ties/edge cases are handled.
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?
Single sentence with a useful user-question framing; every phrase earns its place and the core behavior 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?
Adequate for a simple ranking tool with an informative schema, but the absence of annotations and output schema means the description alone leaves return format and limit behavior under-specified.
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 the useful constraint that `column` must be numeric, which the schema does not say. However, with schema coverage at only 33%, it does not compensate for the undocumented `limit` parameter and only indirectly maps `ascending` to lowest-first.
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 specific operation—returning the highest or lowest rows ranked by a numeric column—and frames it as answering 'which is the most/least X'. This clearly differentiates it from siblings like dataset_stats or dataset_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'which is the most/least X' phrasing implies when to use the tool, but there is no explicit guidance on when to prefer it over dataset_stats, dataset_search, or dataset_row, and no exclusion conditions.
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 has a clear primary purpose: schema, provenance, exact lookup, substring search, comparison, stats, and top/bottom ranking. There is minor overlap between dataset_row and dataset_compare for single-value lookups, but the descriptions steer usage toward distinct cases.
All tools share the dataset_ prefix and use a consistent noun or verb suffix after it. The pattern is predictable and makes it easy to infer what each tool does.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool addresses a different query type without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, substring search, comparisons, numeric statistics, and ranking. Minor gaps like listing all unique values or arbitrary numeric filtering are absent, but they can be worked around with the existing tools.
