FMlane: 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 FMlane dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly lists what the call returns (columns, numeric flags, row count, provenance banner) and frames the tool as an introspective schema-learning step. It does not state side effects or error behavior, but for a zero-parameter schema-shape read, the behavior is sufficiently clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the output contents and ends with actionable usage guidance. Every word earns its place, with no wasted filler.
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, no-output-schema tool, the description provides sufficient context: it names the resource, enumerates the data returned, and tells the agent this is the first call to make. A minor gap is that it does not clarify how its provenance banner relates to the sibling dataset_provenance tool, but this does not prevent 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?
The tool has no parameters, so there is no schema ambiguity to clarify. The baseline of 4 is appropriate because the description does not need to compensate for undocumented parameters.
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 FMLane dataset and enumerates the specific information returned: columns, numeric flags, row count, and provenance banner. The phrase 'learn the schema' reinforces the tool's purpose. It does not explicitly distinguish itself from siblings, though 'Call this first' implies its role as an initial schema-discovery tool.
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 instruction 'Call this first to learn the schema' provides clear guidance on when to use the tool. However, it does not mention alternatives or conditions where another sibling like dataset_provenance or dataset_stats would be more appropriate, so it falls short of fully explicit usage routing.
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 FMlane dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It usefully discloses row selection, equality filtering, and value-order output ordering. However, it leaves some behavior implicit, such as exact-match semantics, case sensitivity, handling of duplicates, and the exact shape of the returned rows.
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 delivers the resource, filtering mechanism, ordering, and intended use case with no filler. The dataset scope is front-loaded and every part of the sentence adds value.
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 tool with no output schema, the description is mostly adequate, but it leaves out the output shape and does not explicitly contrast with dataset_search or dataset_row. With no annotations to provide context, these are moderate gaps.
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 does convey the key relationship between `column` and `values`: rows are selected when the column equals one of the values, and output order follows the values array. It adds ordering semantics beyond the schema, though it could more explicitly define the parameters.
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: return rows from the FMlane dataset filtered by a column matching any of the supplied values, in the provided order. The 'X vs Y' framing and the sibling-tool names make it clearly distinct from dataset_row, dataset_search, and dataset_top.
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 clear context for when to use this tool ('X vs Y' questions), which helps an agent choose it among the dataset_* siblings. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 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 FMlane 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 behavioral disclosure burden. It discloses the type of information returned and implies a read-only, metadata-retrieval behavior through 'Read this.' It does not describe side effects, caching, or computation, but for a simple zero-parameter provenance tool the disclosure is adequate.
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, no filler. The content is front-loaded in the first sentence, and the second sentence adds a practical instruction for use. 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 zero-parameter metadata lookup, the description is complete: it lists the returned fields (source, computed date, licence, citation), identifies the dataset, and explains when to consult it. No output schema exists, but the description sufficiently explains what the agent can expect to learn.
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 the description needs to add about parameter meaning. The baseline of 4 applies because no parameter gaps exist for the description to compensate for.
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 FMlane dataset's provenance) and enumerates the exact content returned: source, computed date, licence, and citation. It lacks an explicit action verb like 'returns' or 'provides', and it does not explicitly contrast itself with sibling tools, but the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear use case: 'Read this to attribute a figure correctly.' This tells the agent when the tool is relevant. It does not name alternatives or explicitly state when not to use it, but the provenance/citation focus makes the appropriate context clear.
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 FMlane 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 burden of behavioral disclosure. It usefully reveals that matching is exact and case-insensitive, but it leaves ambiguity about singular versus plural results — the title says 'a row' while the description says 'rows' — and does not describe behavior for no matches or multiple matches.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single focused sentence that front-loads the resource and the core matching rule. Every word adds meaning, and the case-insensitivity detail is placed where it is easy to notice.
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 lookup with no output schema, the description provides enough context to select and invoke the tool: the resource, the matching semantics, and the parameter roles. It is slightly incomplete regarding whether one row or multiple rows are returned and what an empty result looks like.
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 clarify the parameters. It does this by tying 'column' and 'value' into the matching predicate: a row is returned when a named column equals the supplied value. This is sufficient for the two simple string parameters, though it could be slightly more explicit about value formatting.
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 states a specific action ('Look a row up by an exact key') and the description names the resource ('the FMlane dataset') and the exact matching condition. This distinguishes it from siblings like dataset_search by emphasizing exact, case-insensitive equality.
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 clearly implies use when an exact, case-insensitive match on a column value is needed, which contrasts with the fuzzy search implied by dataset_search. It does not explicitly name alternatives or state when not to use it, but the exact-match condition is enough to guide 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 FMlane 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 full behavioral burden. It discloses case-insensitive substring matching and a 50-row cap, which are useful. It doesn't state ordering, empty-result behavior, or confirm read-only semantics, but the core operation is transparent.
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 resource and action while packing two key constraints (case-insensitive, max 50). 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 low-complexity search tool with no annotations or output schema, the description covers the main invocation semantics and result scope. It doesn't describe output fields or pagination, but the operation is simple enough that an agent can call it correctly from this text.
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 only 50%; the query parameter is described in the schema, while limit has no description. The tool description adds the case-insensitive matching detail and behaviors for limit ('up to 50'), partially compensating for the schema gap, though it doesn't fully document the limit parameter's role.
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 uses a specific verb ('search'), names the resource ('FMlane dataset'), and defines the matching semantics ('cells contain the query, case-insensitive, up to 50'). This clearly distinguishes it from siblings like dataset_stats or dataset_row, which imply different operations.
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 it: when you need rows matching a text query in any cell. However, it does not explicitly state when not to use it or name alternatives like dataset_row for exact-row retrieval or dataset_columns for schema exploration.
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 FMlane 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 behavioral disclosure burden. It usefully states that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not describe edge cases like an all-non-numeric column, but the provided behaviors are meaningful and non-obvious.
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 operation and output metrics, then adds relevant parsing and exclusion behavior. Every clause adds information, and there is no redundancy or filler.
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 and no output schema, the description is complete enough for correct selection and invocation. It states what input is needed, what values are accepted, how data quirks are handled, and what statistics will be returned.
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 only defines 'column' as a string with minLength 1, so description-level semantics are important. The description clarifies that column refers to a numeric column of the FMlane dataset and that values may include formatting like commas and currency, adding real meaning 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 clear verb and resource: it computes summary statistics for a numeric column of the FMlane dataset. It lists the exact metrics (count, min, max, mean, median, sum), which distinguishes it from siblings like dataset_row, dataset_search, or dataset_top.
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 clear context for when to use the tool: when summary statistics for a numeric column are needed. It does not explicitly mention alternatives or exclusions, but the unique statistical purpose is conveyed well enough that an agent can select it appropriately.
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 FMlane 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 behavioral disclosure. It does state that rows are ordered highest/lowest by a numeric column and that the result is a set of rows. However, it does not disclose default limit behavior, handling of non-numeric columns, ties, or output formatting, leaving meaningful 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 efficient sentence with a useful illustrative example and no filler. It front-loads the core ranking behavior and is appropriately sized for a tool of this complexity.
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 with no output schema, the description covers the essential return value ('rows'), the ordering direction, and the column type requirement. It is nearly complete, though limit semantics and edge-case behavior are left implicit.
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 should compensate. It adds the important 'numeric column' constraint for the column parameter, but gives no explanation of the limit parameter's role as the number of rows returned, and only the schema covers ascending's behavior. Partial but not sufficient.
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 name a specific operation: returning the highest or lowest rows of the dataset by a numeric column, with a concrete query example ('which is the most/least X'). This clearly differentiates it from siblings like dataset_search (searching) and dataset_stats (aggregate statistics).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear context for use — answering 'most/least X' questions by ranking rows — but does not explicitly name when to prefer it over sibling tools or list exclusions. This is clear context without alternative guidance, so it earns a 4 rather than a 5.
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 targets a distinct query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric aggregates, and top/bottom rows. Although dataset_row and dataset_compare both filter on column equality, their descriptions clearly separate single-value from multi-value ordered use.
All tools share a consistent dataset_ prefix and snake_case, making the family obvious. The suffix is not uniformly verb_noun, mixing nouns (columns, provenance, stats) with verbs (compare, search), so it is predictable but not perfectly consistent.
Seven tools is well-scoped for a single-dataset query server. Each tool corresponds to a common question type about the FMlane dataset, and none feel redundant or superfluous.
The set covers the core data-exploration surface: schema, provenance, exact/contains lookup, comparisons, numeric summaries, and extremes. Minor gaps exist, such as no distinct-value enumeration or grouped counts, but they can usually be worked around with dataset_compare and dataset_search.