Clauselane: 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 Clauselane 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 behavioral burden, and it does disclose what the tool produces: columns, numeric indicators, row count, and provenance banner. The read-only nature is strongly implied by 'learn the schema' and the zero-parameter design, though it never explicitly states that no data is modified.
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 output contents are front-loaded, and the usage instruction is placed at the end. Every part of the description 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 zero-parameter tool with no output schema, the description is largely complete: it names the dataset, the returned information, and the intended call order. It doesn't describe the exact representation of non-numeric columns or the provenance banner, but that is a minor gap given the tool's simple introspection purpose.
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 is empty, so there are no parameter semantics to explain. The 0-parameter case earns the baseline of 4, and the description adds useful context about what the returned schema information will contain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (the Clauselane dataset) and the key outputs: columns, numeric flags, row count, and provenance banner. It lacks an explicit verb like 'returns' or 'lists,' and it doesn't name sibling alternatives, but 'Call this first to learn the schema' positions the tool distinctly as the schema entry point.
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' gives explicit timing guidance, telling the agent to invoke this tool before working with the dataset. It does not mention alternatives or when not to use it, but for a zero-parameter schema-introspection tool, this is clear 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 Clauselane 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 burden. It usefully states that rows are filtered by 'any' matching value and preserve the given order. However, it does not disclose whether matching is exact or case-sensitive, how the rows are formatted or presented, or what happens when no rows match.
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 compact sentence conveys the core behavior and intended use case without repetition or filler. The essential matching and ordering semantics are front-loaded, and the 'X vs Y' tag adds relevant context in a few words.
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 read operation, the description plus title gives a decent overall picture: matched rows, ordering, and comparison purpose. However, with no annotations and no output schema, it leaves edge cases, output layout, and sibling-tool routing unspecified, so 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?
The schema has 0% description coverage, so the description must explain the parameters. It effectively maps 'column' to the matching field and 'values' to the candidate values, and adds important ordering semantics. It doesn't mention min/max constraints, but those are already present in 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 specifies that the tool returns rows from the Clauselane dataset whose column matches any of the given values, in the order of those values. This is more concrete than the title and suggests a comparison use case, though it does not explicitly distinguish itself from the sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for "X vs Y" questions' gives an implied usage context—comparing rows by specific values—but there is no explicit guidance on when to choose this tool over dataset_row, dataset_search, or dataset_top, or when it should not be used.
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 Clauselane 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?
There are no annotations, so the description carries the behavioral burden. It clearly indicates a read-only informational operation ('Read this') and specifies exactly what information will be exposed: source, date computed, licence, and citation. For a zero-parameter metadata tool, this is sufficient transparency.
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, immediately front-loads the substantive content, and avoids repeating the title or schema. Every word contributes to the agent's understanding of what the tool returns and when to use it.
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 no-parameter, no-output-schema provenance tool, the description is complete: it lists all the fields the agent will receive and explains the practical purpose (correct attribution). 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?
The tool has zero parameters and 100% schema description coverage, so there is nothing for the description to clarify about inputs. The baseline of 4 applies because no parameter guidance is necessary.
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 explicitly identifies the tool's resource (Clauselane dataset) and the specific provenance details it covers: source, computation date, licence, and citation. The title reinforces the purpose ('Where this data comes from, and how to cite it'), and the content clearly distinguishes it 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 gives a clear use case: 'Read this to attribute a figure correctly.' This tells the agent when the tool is appropriate. It does not name alternatives, but none of the sibling tools serve a provenance purpose, so no exclusion is strictly needed.
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 Clauselane 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 key matching behavior (exact, case-insensitive) and that it returns rows. However, it does not specify whether one or many rows are returned, what happens on no match, or whether there are any limits—minor but relevant gaps 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, compact sentence that front-loads the resource and core behavior without fluff. It is appropriately concise for a simple lookup tool, though it could include a tiny bit more behavioral detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter, no-output-schema tool, the description is mostly adequate. It names the dataset, matching logic, and case sensitivity. But it leaves ambiguity about return cardinality, empty results, and error behavior, which an agent might need when invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implicitly maps 'column' and 'value' by using those exact words in the sentence, and adds the case-insensitive matching nuance. Still, it does not explicitly define parameter purposes or constraints beyond what the schema already provides.
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 a specific action: retrieving rows from a named dataset where a column exactly equals a value, with case-insensitivity. The title reinforces the exact-key lookup. It does not explicitly contrast with sibling tools, but the phrase 'equals a value exactly' differentiates it from fuzzy search-like alternatives such as 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 description implies when to use the tool: when you need exact, case-insensitive row matches by column value. However, it does not explicitly state when not to use it or mention alternative tools, leaving the agent to infer routing from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Clauselane 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. It discloses case-insensitive matching and the 50-row cap, which is useful. However, it does not mention output format, ordering, which rows are returned when more than 50 match, or whether the operation is strictly read-only, leaving moderate gaps.
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 no filler. The most important facts (what is searched, matching behavior, result cap) are front-loaded. 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 two-parameter search tool with no output schema, the description is adequate but bare. It lacks guidance on choosing among the six sibling tools, default limit behavior, row ordering, and return shape. The core semantics are present, but an agent has several open questions when invoking 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?
Schema coverage is 50%: only 'query' has a description. The description adds the case-insensitive behavior for query and reinforces the limit cap, but it does not explicitly map 'up to 50' to the 'limit' parameter or explain default behavior. It adds some value beyond the schema but does not fully compensate for the undocumented limit.
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 explicitly states the resource (Clauselane dataset), the action (find rows whose cells contain the query), and key modifiers (case-insensitive, up to 50). This is a specific, self-contained purpose that clearly distinguishes it from sibling tools like dataset_columns or dataset_row.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says what the tool does but not when to use it over alternatives. It never mentions any sibling tool, exclusion condition, or scenario where another dataset_* tool would be more appropriate. The intended use case is inferable but not explicit.
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 Clauselane 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 provided, the description carries the burden of behavior disclosure. It reveals key parsing behaviors: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This gives meaningful insight into how messy data is processed, though it stops short of describing error cases or exact response structure.
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 that front-loads the computed metrics before adding parenthetical parsing details. Every element earns its place; there is no filler, repetition, or irrelevant context.
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 tool with one parameter, no annotations, and no output schema, the description covers the essential inputs, the expected column type, parsing behavior, and the list of results. The metrics list effectively serves as a return contract, though the exact JSON response format is not 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 input schema defines 'column' only as a non-empty string with no property description, so schema description coverage is 0%. The description compensates by specifying that the parameter refers to a numeric column in the Clauselane dataset and by explaining how the column values are parsed, adding critical meaning the schema lacks.
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 title and description clearly state the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Clauselane dataset. The specific list of metrics and the explicit resource distinguish it clearly from sibling tools like dataset_columns or dataset_top, 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 description implies the tool is for analyzing numeric columns by mentioning 'numeric column' and explaining how non-numeric rows are handled. However, it does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or prerequisites, leaving routing mostly to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The highest (or lowest) rows of the Clauselane 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 must carry the behavioral burden. It does disclose the core behavior (ranking by a numeric column, returning highest or lowest), but it does not mention default limit behavior, tie handling, invalid column handling, or that the operation is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with a useful clarifying clause, but it partially restates the title ('Rank rows by a numeric column' vs. 'highest rows ... by a numeric column'). Still, it is compact and readable.
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 ranking tool with only 3 parameters, this is minimally sufficient: an agent can infer the main call pattern. Yet without annotations or an output schema, it lacks details on returned rows, default limit, and error behavior, so it is 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 low (only ascending has a description), and the description compensates by explaining that column is numeric and linking ascending to 'most/least'. However, the limit parameter's meaning is never clarified beyond the schema's min/max, so the compensation is incomplete.
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 verb and resource: it returns the highest or lowest rows of the Clauselane dataset ranked by a numeric column, which clearly distinguishes it from siblings like dataset_row, dataset_search, and dataset_stats. The 'which is the most/least X' phrasing makes the intent immediately actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied through 'which is the most/least X' — it is not stated explicitly when to choose dataset_top over dataset_stats or dataset_search, and no exclusions or alternatives are named. This is adequate but leaves the routing decision 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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Cafmlane: the site's own MCP server — dataset; every answer cites the site.
Sudslane: the site's own MCP server — dataset; every answer cites the site.
Subbielane: the site's own MCP server — dataset; every answer cites the site.
Limslane: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Most tools are clearly separated by operation: schema, provenance, exact lookup, search, stats, top, and comparison. Dataset_row and dataset_compare overlap somewhat for exact-value lookups, but their intended use cases are mostly distinguishable.
All tools share the dataset_ prefix and snake_case convention, which creates a strong pattern. However, the suffix mixes nouns (columns, row, stats) and verbs (compare, search), so it is not a fully consistent verb_noun scheme.
Seven tools is well-scoped for a dataset exploration server. Each tool serves a distinct read/query need without unnecessary bloat or overlap.
The set covers schema discovery, provenance, exact lookup, fuzzy search, numeric statistics, top/bottom values, and category comparisons. For a read-only dataset querying purpose, there are no obvious missing operations.