Abutly: 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 Abutly 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 behavior, and it does so adequately: it enumerates the returned contents and frames the tool as informational/schema-learning. It does not explicitly state 'read-only' or describe edge cases, but for a zero-parameter introspection tool this is sufficient.
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 with no filler. The first sentence lists the exact output contents, and the second gives the usage directive. Information is front-loaded and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless metadata tool with no output schema, the description covers the essential return values and when to call it. It is slightly incomplete in that it does not clarify the relationship to dataset_provenance (which may also expose provenance), but overall it is sufficient.
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 parameters, so the baseline is 4. The description adds useful context about the dataset scope and returned fields, but there are no parameter semantics to elaborate on.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies what the tool provides: columns, numeric column flags, row count, and provenance banner for the Abutly dataset. It also states the intended initial action ('Call this first to learn the schema'), but it does not explicitly differentiate itself from siblings like dataset_provenance 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?
It gives explicit usage timing ('Call this first') and purpose ('to learn the schema'), which is strong guidance. However, it does not mention when not to use this tool or point to alternatives among the sibling tools.
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 Abutly 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. It discloses useful behavior: matching is based on 'any of' the given values, and results appear 'in the order given.' However, it does not describe exact-match semantics, case sensitivity, response format, or whether the operation is read-only, leaving some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no filler. The primary filtering behavior is front-loaded, and the use-case note 'for X vs Y questions' is compact yet informative.
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 comparison tool, the description is reasonably close to adequate. However, the absence of annotations, output schema, and any mention of return format or edge cases like missing values or duplicates leaves noticeable gaps for an agent deciding how to use the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needs to compensate for the missing parameter documentation. It adds the key relationship that the column is matched against any of the values and that order matters, but it does not clarify valid column names, value matching rules, or handling of empty 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 clearly states that the tool returns rows from the Abutly dataset where the specified column matches any of the given values, and that the order of values is preserved. This is specific enough to distinguish it from generic search or stats tools, though it does not explicitly reference sibling tools to differentiate them.
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 some usage context, suggesting the tool is meant for comparing specific rows side by side. However, it does not explicitly mention alternatives like dataset_row or dataset_search, nor does it state when not to use this tool.
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 Abutly 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 present, so the description carries the burden. It frames the operation as a non-mutating read ('Read this') and specifies the exact metadata returned: source, computed date, licence, and citation. This is adequate for a simple provenance tool, though it stops short of describing the exact response format.
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 filler. It front-loads the specific metadata items and adds exactly one sentence about why to use the tool. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless provenance tool with no output schema, the description is complete: it states what information is returned, names the dataset, and explains when to use it. An agent can select and invoke this tool with confidence based on the text alone.
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 parameters and schema description coverage is 100%, so there is nothing for the description to add. The baseline for a parameterless tool is 4, and the description doesn't need to explain parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies what the tool provides: source, computed date, licence, and citation for the Abutly dataset. It also gives the operational purpose, 'Read this to attribute a figure correctly,' and is easily distinguished from sibling tools focused on columns, rows, stats, search, and comparison.
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 states when to use the tool: when attribution or citation of a figure is needed. It doesn't name alternatives or exclusions, but the context is clear and sufficient for a metadata/provenance lookup among the listed siblings.
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 Abutly 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 full burden. It does disclose one non-obvious trait: matching is case-insensitive despite 'exactly.' However, it leaves cardinality ambiguous (title says 'a row,' description says 'rows'), says nothing about no-match or multi-match behavior, and the 'exactly (case-insensitive)' phrasing is internally tension-laden. No annotation contradiction since no annotations exist.
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 with no filler: it names the resource, the filtering condition, and the case rule immediately. It earns conciseness points, though the 'exactly (case-insensitive)' qualifier muddies precision slightly, keeping it from a 5.
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 low-complexity tool (two simple string params, no enums, no output schema), the description conveys the core matching semantics but not the return behavior — no output schema exists and the description never says what a successful call returns or what happens on zero/multiple matches. The gaps are real but not crippling for such a simple lookup.
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. It does tie the two params to behavior — column is the field being matched and value is the lookup value — which is more than the bare schema provides. But it omits what values column accepts (name vs index) and any value formatting requirements, 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 states the operation — retrieve rows from the Abutly dataset filtered by an exact column=value equality — which is a specific verb+resource pairing. The title adds the exact-key lookup framing, and 'exactly' hints at differentiation from the sibling search tool, though the description doesn't name that distinction 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 description gives no guidance on when to use this tool instead of dataset_search or the other siblings — no scenario conditions, prerequisites, or exclusions. The word 'exactly' in the title implies an exact-match use case, but the description itself never states routing rules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Abutly 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 disclosure burden. It usefully discloses that matching is case-insensitive, applies to any cell, and returns at most 50 rows. It does not discuss pagination, ordering, or error behavior, but these are less critical for a read-style search 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 efficient sentence with no filler. It front-loads the core behavior, then adds the case-insensitive detail and the result cap. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search tool with no output schema, the description adequately explains what is returned (matching rows) and the upper bound on results. It could mention ordering or default limit, but an agent can invoke the tool correctly with the information provided.
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 but not the limit parameter, giving 50% coverage. The description adds partial meaning by saying results are capped at 50 and that the query is searched across cells, relating to both parameters. However, it does not clarify the default limit or the exact behavior when limit is omitted.
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 of the Abutly dataset whose cells contain the query, and adds useful specifics: case-insensitive matching and a limit of 50. It does not explicitly contrast with sibling tools, but the function is distinct and clear enough from the wording.
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 usage context is implied by the description: use this when you need to find dataset rows containing a given text fragment. However, there is no explicit guidance about when not to use it or which sibling tools (e.g., dataset_row, dataset_top) to prefer instead.
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 Abutly 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 disclosure burden, and it does more than repeat the title: it discloses that grouping commas and currency symbols are parsed, and that non-numeric rows are excluded and counted. This is meaningful behavioral context. The phrase 'excluded and counted' is slightly ambiguous about whether the returned count excludes those rows, so it is not a perfect 5.
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 compact sentence lists the exact statistics, then adds two important caveats in a parenthetical. There is no filler; the most identifying information (statistics and dataset) comes first, and the edge-case behavior is placed where it cannot distract.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description supplies the return statistics, the target dataset, and data-cleaning semantics. It does not specify the exact response shape or define the behavior when the column contains no numeric rows, but those are minor omissions for this simple operation.
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 semantics. It does: 'column' is specified as a numeric column of the Abutly dataset rather than just any string. The mention of grouping commas, currency, and non-numeric handling also informs what kind of values the column may contain, which goes beyond the bare 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 identifies the operation: it computes count, min, max, mean, median, and sum for a numeric column. The title reinforces this, and the scope is narrowed to 'the Abutly dataset,' distinguishing it from sibling tools like dataset_search or dataset_top, which serve different lookup/ranking 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 description makes the usage context clear: use this when summary statistics for a numeric column are needed. It does not explicitly name alternatives or exclusion conditions, so it falls short of a 5, but the numeric-column and dataset qualifiers are enough for an agent to select it over the listed siblings.
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 Abutly 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 provided, the description carries the full burden of behavioral disclosure. It only states the basic ranking behavior and does not mention edge cases such as non-numeric column values, missing data, tie handling, or whether full rows are returned.
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 short and front-loaded, carrying its core meaning in the first clause. The quoted phrase adds helpful intuition but is slightly redundant with the opening statement.
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 three-parameter tool, the description covers the main ranking concept but omits important calling context: the meaning of the limit parameter is left only to schema constraints, and there is no indication of return shape or behavior when the column is not numeric.
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%, so the description must compensate. It does clarify that 'column' must be numeric and implies the effect of 'ascending', but it leaves 'limit' without any real semantic guidance beyond the schema's min/max constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: returning the highest or lowest rows by a numeric column, framed as 'which is the most/least X.' This distinguishes it from broad dataset operations like search or stats, though it does not explicitly name sibling 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?
The description implies when to use the tool: whenever an agent needs ranking by a numeric column. However, it offers no explicit guidance on when not to use it or which sibling tool might be more appropriate for similar needs.
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 role: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. Even though dataset_row and dataset_compare both retrieve rows by column value, their descriptions make the single-value vs multi-value distinction clear.
All tools share a consistent dataset_ prefix and use clear snake_case names. The suffixes are either nouns or verbs that accurately reflect the operation, making the naming predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query need without redundancy or bloat.
The toolset covers the full range of dataset querying: schema inspection, provenance, exact match, substring search, multi-value comparison, numeric aggregation, and ranking. Since this is a read-only dataset server, no update/create/delete tools are needed.