OrderPadLedger: 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 OrderPadLedger 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?
No annotations are provided, so the description carries the behavioral disclosure burden. It clearly enumerates what the tool returns: columns, numeric flags, row count, and provenance banner. As a read-only schema inspection tool, this is adequate despite not discussing side effects or permissions.
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 sentences, no filler, and the most actionable guidance ('Call this first') is included. Every word contributes 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 zero-parameter tool with no output schema, the description lists the key outputs and the intended first-use context. It could specify return formatting or data types, but the description is adequate for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters and schema coverage is 100%, so the description does not need to add parameter detail. The baseline of 4 for zero-parameter tools applies.
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 what the tool provides: columns, numeric indicators, row count, and the provenance banner for the OrderPadLedger dataset. It also frames the purpose as learning the schema. It does not explicitly contrast with sibling tools, but the resource and content are specific enough to distinguish it from the listed siblings.
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' gives explicit usage context and ordering. It does not mention alternatives or exclusions, but for a zero-parameter schema-introspection tool, the timing guidance is sufficient.
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 OrderPadLedger 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 full burden. It discloses the selection behavior and the order-preservation behavior, which is useful. However, it does not describe the output format, whether all columns are returned, or what happens when no rows match, leaving some ambiguity in the exact behavior.
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 includes the target dataset, the filtering logic, the ordering behavior, and the intended use case. Every part earns its place with no unnecessary 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 tool with no output schema, the description covers the core invocation details: which dataset, which column, which values, and the output order. It does not specify the exact return representation, but the phrase 'The rows' gives a reasonable expectation that full row data is 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?
With schema description coverage at 0%, the description compensates by explaining that 'column' is a column in the dataset and 'values' are the values to match. It adds the important semantic that the order of the values array determines the output row order, which is not apparent from the schema alone.
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 returns rows from the OrderPadLedger dataset filtered by a column matching any of the supplied values, in the order given. It distinguishes itself from sibling tools like dataset_row and dataset_search by framing the use case as 'X vs Y' comparisons. The missing explicit verb is a minor flaw, but the intent 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 provides clear context for when to use this tool — for 'X vs Y' questions where specific values of a column need to be compared side by side. It does not explicitly name alternatives or state when not to use it, but the differentiation from dataset_search, dataset_row, and dataset_stats is implied by the focused comparison use case.
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 OrderPadLedger 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, the description carries the full behavioral disclosure burden. It lists the returned content fields and implies a read-only, side-effect-free operation. It could be more explicit about output format, but the metadata-only nature is clearly conveyed.
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: the first names the returned fields, the second gives the usage context. It is front-loaded with the essential content and contains no 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 metadata retrieval tool with no output schema, the description is sufficient. It tells the agent what it will receive (source, date, licence, citation) and why to call it (attribution), so no critical information is missing for selection and 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 zero parameters, so there is nothing to document; the baseline of 4 applies. The description adds no parameter information, but none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies exactly what the tool exposes — source, computed date, licence, and citation — for the OrderPadLedger dataset, and explicitly frames it as a read action for attribution. This clearly distinguishes it from sibling data-access tools like dataset_search or dataset_row, which return dataset contents rather than provenance metadata.
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 'Read this to attribute a figure correctly' provides a clear use case: when the agent needs citation or provenance information. It does not explicitly name alternatives or state when not to use it, but the context is strong enough that an agent can infer this tool is for metadata, not data operations.
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 OrderPadLedger 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 behavioral burden. It usefully discloses exact matching and case-insensitivity, and 'rows' implies all matches are returned. But it does not mention possible empty results, whether the dataset has unique keys, or any other operational caveats.
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 states the dataset, the selector, and the matching behavior. No filler or redundant content appears.
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, the description covers the core lookup semantics and even adds case-insensitivity. It is less complete regarding the return shape, duplicate matches, and explicit differentiation from sibling tools, though the absence of an output schema makes some of this harder to assess.
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 clarify that 'column' is the field to match on and 'value' is what to match, plus the exact/case-insensitive rule. However, it provides no additional detail on valid column names, value formatting, or edge cases.
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 conveys a concrete verb and resource ('Look a row up'), and the description clarifies the matching rule: rows whose column equals a value exactly, case-insensitively. It is specific enough to distinguish from a fuzzy search sibling, though the description's noun-phrase phrasing ('The rows...') is slightly awkward and does not explicitly say 'returns'.
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 an exact, case-insensitive column match is needed. However, it never explicitly contrasts this with alternatives such as dataset_search or states when not to use it, leaving the agent to infer the intended boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the OrderPadLedger 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 present, so the description carries the behavioral disclosure burden. It discloses the matching scope (any cell), case-insensitive behavior, and the 50-row limit, which are useful traits beyond the schema. It does not specify ordering or the default limit when omitted, but the core behavior is adequately 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?
The description is a single, well-structured sentence with no wasted words. It front-loads the key facts: dataset, matching behavior, case-insensitivity, and result cap.
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, the description covers the essential behavior and result scope. However, there is no output schema and no annotation, so details like return row structure, default limit, ordering, and relationship to sibling tools are left unstated.
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 query as 'text to look for in any cell,' and the description adds case-insensitive matching, which is helpful. The limit parameter has no schema description, and the description's 'up to 50' partially compensates but does not clarify the default behavior or the exact role of the limit parameter beyond the schema maximum.
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 what the tool returns: rows of OrderPadLedger whose cells contain the query, and it adds case-insensitivity and a 50-row cap. It is distinct from siblings like dataset_row or dataset_columns, but it does not explicitly call out how it differs from 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?
No explicit guidance is given about when to use this search tool versus alternatives such as dataset_row or dataset_stats. The intended use is implied from the word 'search,' but there are no when-to-use or 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 OrderPadLedger 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 does well by explaining that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not detail output shape or error behavior, but the disclosed quirks 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 compact sentence that lists the computed statistics first and then adds relevant caveats. Every part carries information and there is no 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 simple one-parameter tool with no output schema, the description is largely complete: it names the dataset, the statistics, and important data-cleaning behavior. It could mention the return format or edge cases, but the core calling context is sufficiently covered.
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 documents a required string column with no description, and schema coverage is 0%. The description adds that the column must be numeric and belong to the OrderPadLedger dataset, which is useful, but it stops short of explaining exact column-name expectations or what happens for invalid columns.
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 operation: computing count, min, max, mean, median, and sum for a numeric column of the OrderPadLedger dataset. This makes the tool's job concrete and distinguishes it from siblings like dataset_row or dataset_search, 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 should be used for numeric summary statistics on the OrderPadLedger dataset, but it does not explicitly say when to prefer this over siblings or when not to use it. There is no mention of alternatives or exclusions.
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 OrderPadLedger 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 and does disclose the core behavior: selecting rows by numeric ordering, either highest or lowest. However, it does not state whether the operation is read-only, how ties or null values are handled, what the default limit is, or what the response structure looks like, leaving some behavioral uncertainty.
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 helpful illustrative phrase. It conveys the essential meaning without filler, though some of it overlaps with the title. Overall, it is concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, so the description must explain what the caller gets back. It does not mention the default limit when omitted, the return format, or edge-case behavior such as ties or missing values. For a simple query tool this is still a meaningful completeness gap.
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 low at 33%, with only "ascending" documented in the schema. The description partially compensates by clarifying that "column" must be numeric and that ascending/descending relates to highest/lowest, but it does not explain the "limit" parameter at all or explicitly map parameter names to behaviors.
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 the highest or lowest rows of a named dataset based on a numeric column, which directly expresses the ranking purpose. It is easy to distinguish from siblings like dataset_search or dataset_stats because it targets top/bottom row selection rather than filtering or aggregation.
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 "which is the most/least X" gives a clear intended use case for ranking questions, but the description does not mention when to prefer this over siblings such as dataset_stats or dataset_row, nor does it state exclusions. The usage is implied rather than explicitly contrasted with alternatives.
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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ReceivableLedger: the site's own MCP server — dataset; every answer cites the site.
BurdenRateLedger: the site's own MCP server — dataset; every answer cites the site.
ProbeLedger: the site's own MCP server — dataset; every answer cites the site.
Netsheetly: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Each tool has a distinct role: schema, provenance, exact-match row lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. The only mild overlap is dataset_row versus dataset_compare and dataset_search, but their descriptions clarify exact equality, multi-value filtering, and cell containment.
All tools share the dataset_ prefix, making the family immediately recognizable. The suffixes mix nouns (columns, row, provenance, stats, top) and verbs (compare, search), so there is no strict verb_noun convention, but the pattern is predictable and readable.
Seven tools is a well-scoped count for a single-dataset exploration server. Each tool covers a distinct query need without redundancy or unnecessary surface area.
The server covers the full read-only lifecycle of interacting with this dataset: schema discovery, provenance, row lookup, search, comparison, aggregation, and ranking. There are no obvious missing operations for its stated purpose.