Tieoutly: 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 Tieoutly 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 disclosure burden; it does convey that this is a read-only schema-discovery operation and lists the returned information categories. However, it does not explicitly state that no parameters are needed or describe how the listed items are represented, so some behavioral context is left 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 description is two short sentences, with the concrete outputs leading and the actionable call-instruction ('Call this first') immediately after. No filler or repetition 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 parameterless introspection tool with no output schema, the description covers what information will be revealed and when to invoke it. The only minor gap is that 'provenance banner' is a domain-specific term that is named but not explained, and the exact return shape is not described.
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 takes zero parameters and the schema description coverage is effectively complete, so there is little for the description to add. The baseline for a parameterless tool is 4, and the description naturally needs no parameter-specific detail.
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 enumerates the tool's exact outputs: columns, numeric-field indicators, row count, and provenance banner, and frames it as the first schema-introspection step. This clearly distinguishes it from siblings like dataset_compare or dataset_search, which operate on data rather than describe structure.
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 instructs the agent to call this first to learn the schema, giving a clear when-to-use signal. It does not name alternatives or state when not to use it, so it stops short of a full routing rule.
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 Tieoutly 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 present, so the description carries the full burden. It discloses the core behavior: rows are selected by matching column values and returned in the given order. It does not mention any side effects or limitations, but none are expected.
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, no filler, efficiently communicates the 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?
Given the simple query nature and lack of output schema, the description provides all necessary information for an agent to understand what the tool does and when to call it.
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 explains the semantics of 'column' and 'values' by stating that rows are those whose column matches any of the given values, adding meaning beyond the bare JSON schema types.
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 verb 'Compare rows side by side' and resource 'rows of the Tieoutly dataset' and clearly defines the filtering and ordering behavior, distinguishing it from siblings like dataset_row 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?
Provides a use case ('for X vs Y questions') but does not explicitly name alternative tools or conditions for when to use them instead.
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 Tieoutly 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 full burden. It only lists the data returned and does not mention side effects, authentication needs, rate limits, or whether it is read-only. This lack of behavioral disclosure leaves uncertainty about the tool's impact.
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 sentences, concise and to the point. Every word adds informational value without fluff 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?
The description fully explains the tool's purpose and the type of information returned. Since there is no output schema, no return-value explanation is needed. An agent has enough context to decide when to invoke it.
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 the schema is empty (100% coverage). The baseline for high schema coverage is 3, and the description adds no parameter-specific detail because there are none to describe.
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 provenance information (source, computed date, licence, citation) for the Tieoutly dataset. It lacks an explicit verb like 'retrieve' or 'get', but the intent is clear. It is distinct from sibling tools that handle columns, rows, stats, etc.
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 provides a clear use case: 'Read this to attribute a figure correctly.' This tells an agent when to use the tool. It does not explicitly contrast with sibling tools, but the attribution context is sufficient for basic 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 Tieoutly 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?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions case-insensitivity, which is useful, but does not state that the operation is read-only, what happens on no matches or multiple matches, or any limits or error behavior. This is a significant gap for a lookup tool.
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 that front-loads the core behavior: selecting rows by exact match. There is no filler or redundant phrasing, 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?
Given the tool's simplicity (two string parameters, no output schema, no annotations), the description covers the essential matching logic and case-insensitivity. However, it omits information about the return structure, potential multiple matches, and any constraints or limitations, which an agent might need for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains that 'column' identifies the column to match and 'value' is the exact value to compare, which directly maps to both parameters. However, it does not provide additional constraints, examples, or format details, leaving it minimally 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?
The description clearly states that the tool returns rows from the Tieoutly dataset where a column exactly equals a value (case-insensitive). The title adds 'Look a row up by an exact key', reinforcing the purpose. It distinguishes from siblings like dataset_search by emphasizing exact match, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus siblings such as dataset_search or dataset_compare. The description implies exact-match use but does not state conditions, exclusions, or alternatives, leaving the agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Tieoutly 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, the description carries the full burden. It usefully discloses case-insensitive cell containment and a 50-row cap, but it does not clarify the return shape, default limit behavior, ordering, or what happens when there are no matches. This is adequate 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?
A single sentence that front-loads the dataset and matching condition, with no filler or redundancy. Every word contributes meaning, and the case-insensitivity plus cap are efficiently included.
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, the basics are covered, but the lack of an output schema and annotations means the description should say more about return format, default limit, and empty-result behavior. It is functional but leaves important edge cases to be discovered.
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 documents the query parameter and gives a range for limit. The description adds case-insensitive behavior and an upper bound of 50, but it does not explain the default for the optional limit or how the limit parameter maps to the returned row count.
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 indicates that the tool returns rows of the Tieoutly dataset that match a query in any cell, with case-insensitive matching and a 50-row cap. It does not explicitly name sibling tools, but the search-oriented behavior is distinct and easy to recognize.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: use this tool when you need rows whose cells contain a given search string. However, there is no explicit guidance about when to prefer this over siblings like dataset_row or dataset_top, nor any when-not-to-use instructions.
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 Tieoutly 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?
The description reveals important behavioral details: it handles grouping commas and currency, and excludes non-numeric rows while counting them. This gives insight into edge-case handling. Since there are no annotations, the description carries the burden and does so reasonably well, though it does not explicitly mention side effects or read-only nature.
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, well-structured sentence that efficiently lists the statistics and key handling behaviors. It avoids unnecessary words and presents all essential information in a compact form.
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 description lists the statistics returned (count, min, max, mean, median, sum) and mentions handling of commas/currency and non-numeric rows. While no output schema is provided, this gives a good understanding of the tool's output. It does not specify the exact return format or error handling, but it is sufficient for typical use.
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 only parameter 'column' has no schema description, so the description must compensate. It does by indicating that the column should be numeric, implying the parameter is the column name. However, it does not provide further details like allowed formats or examples, relying on common sense.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column. It distinguishes its function from sibling tools like dataset_row or dataset_search by focusing on statistical aggregation. The mention of handling commas and currency further clarifies its purpose.
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 does not explicitly state when to use this tool versus alternatives. However, the name and functionality imply it should be used when column-level statistics are needed. It lacks explicit contrast with sibling tools such as dataset_columns or dataset_top.
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 Tieoutly 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 burden of explaining behavior. It states that rows are returned in highest/lowest order by a numeric column, but it does not disclose the default limit, whether the result is a sorted subset, how ties are handled, or that this is a read-only operation. These are meaningful gaps for a tool with no annotation safety hints.
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 concise sentence that conveys the core ranking concept and a practical interpretation. It is front-loaded with the key behavior and contains no filler, though it is slightly informal.
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, but with no output schema and only 33% schema coverage, the description leaves important details unstated, especially the limit parameter and return shape. It is adequate for an experienced agent to infer the main purpose, but not complete enough to guarantee correct usage.
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%, and the description compensates only partially. It adds that 'column' must be numeric and ties 'ascending' to highest/lowest ordering, but 'limit' is entirely undocumented in both the schema and the description. The description does not clarify default values or how the limit interacts with ranking.
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 together clearly identify the tool as ranking rows by a numeric column (top or bottom). The 'which is the most/least X' phrasing gives a concrete use case and distinguishes it from search, stats, and row-fetching siblings. However, it does not explicitly name any sibling or contrast its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: use this when you want the most/least values of a numeric column. There is no explicit statement about when not to use it or which sibling alternative to choose, so the guidance is only implied rather than stated.
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 clearly distinct purpose: schema introspection, exact-match lookup, substring search, multi-value comparison, statistical aggregation, ranking, and provenance metadata. No two tools overlap in functionality, making misselection unlikely.
All tools follow a consistent 'dataset_<descriptor>' pattern, where the descriptor is a noun or verb indicating the operation (columns, compare, provenance, row, search, stats, top). This uniformity aids predictability and discoverability.
Seven tools is well-scoped for a dataset-querying server. Each tool covers a distinct query type or metadata aspect, and none are redundant or unnecessary.
The tool surface covers the primary ways to interact with the dataset: retrieving schema, accessing rows via exact match, substring search, multi-value comparison, computing statistics, finding top/bottom values, and citing provenance. This covers the full lifecycle of typical dataset questions without obvious gaps.