WalkthroughDesk: 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 WalkthroughDesk 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 behavioral disclosure. It does disclose the returned content and implies a read-only metadata lookup via 'learn the schema,' but it does not explicitly state that the call is non-mutating, nor does it mention any access requirements or limits.
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 front-loads the output details and ends with a clear usage directive. There is no filler or redundancy.
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 schema-overview tool, the description covers the key outputs and tells the agent to call it first. 'Provenance banner' is slightly under-specified, but the overall context is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the input schema is empty, so the baseline is 4. The description has no parameter burden and accurately implies that no arguments are 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 names a specific resource (WalkthroughDesk dataset) and the exact output: columns, numeric flags, row count, and provenance banner. It clearly distinguishes this schema-overview tool from the sibling tools, which handle compare, search, stats, row access, and provenance separately.
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 'Call this first to learn the schema' gives explicit when-to-use guidance—before other dataset operations. It does not enumerate exclusions or name alternatives, but the directive is strong enough to route an agent correctly.
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 WalkthroughDesk 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 full behavioral burden. It usefully discloses retrieval semantics: rows are matched when the column equals any of the given values, and the ordering follows the values as provided. However, it does not describe the output shape, read-only nature, limits, or edge cases such as missing values.
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 dense sentence that front-loads the dataset and filter behavior, then adds the ordering guarantee and the intended use case. There is no filler or repetition of schema constraints.
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 tool with no output schema and no annotations, the description covers what is returned, the filtering key, accepted values, ordering, and when to use it. It could include an example or explicitly route around sibling tools, but the essential information for correct invocation is present.
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 descriptions are 0%, so the description provides essential parameter meaning: 'column' is the field to filter on and 'values' are the accepted alternatives, with their order driving the output order. This is significant added meaning beyond the bare schema, though details like value format and exact-match semantics are left implicit.
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 WalkthroughDesk dataset filtered by a column matching any of the given values, in the provided order. It implies a comparison use case via 'X vs Y', but it does not explicitly differentiate itself from sibling tools by name.
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 a clear intended use case: comparing a small set of specified values side by side. It does not explicitly mention alternatives or exclusions, but the usage context is strong and distinctive.
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 WalkthroughDesk 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 burden of conveying that this is informational. The explicit 'Read this' phrasing signals a non-mutating lookup, and listing the exact data fields provides transparency about what will be returned. It doesn't mention side effects, but none are plausible for a provenance metadata 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 two sentences with no filler. The title reinforces the purpose, and the description front-loads the key contents (source, date, licence, citation) before the usage instruction.
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 lookup, the description fully covers what the tool returns and why to use it. No output schema exists, but the listed fields are enough for the agent to know what to expect. There are no missing prerequisites or edge cases to disclose.
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 the schema is fully covered. The description does not need to explain parameters; it meaningfully describes the dataset that the metadata applies to, which is sufficient for an agent to understand the scope of the call.
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 provides: source, computed date, licence, and citation for the WalkthroughDesk dataset. It clearly differentiates this from sibling tools like dataset_columns or dataset_stats, which handle data content rather than provenance.
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 explicitly instructs the agent to use this tool when attribution is needed: 'Read this to attribute a figure correctly.' It doesn't explicitly name alternatives, but the use case is clear and distinguishes it from the other dataset 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 WalkthroughDesk 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?
The description discloses one key behavioral trait—case-insensitive matching—which is beyond the schema. However, no annotations exist, and it does not mention whether all matching rows are returned, how missing columns are handled, or any error behavior, leaving several behavioral aspects untold.
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 concise sentence with the essential information. The title is clear and the description is front-loaded with the core action and dataset name. No filler or unnecessary detail.
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 exact-match lookup, the description covers the primary functionality and even notes the plural 'rows' to indicate all matches are returned. With no output schema and no annotations, it is sufficiently complete for the tool's simplicity, though edge cases are not addressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description clarifies the roles of both parameters: 'column' is the field to compare and 'value' is the exact string to match, with case-insensitivity applied. This adds meaning beyond the bare schema types, though it does not specify any additional constraints or format expectations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb, resource and scope. The description explicitly says it returns rows from the WalkthroughDesk dataset where a column equals a value, and the title reinforces the exact-key lookup. This clearly differentiates it from siblings like 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?
No guidance is provided on when to use this tool versus alternatives. The description does not reference any of the listed sibling tools (e.g., dataset_search for fuzzy or partial matching), leaving the agent to infer the appropriate use case without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the WalkthroughDesk 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 burden of explaining behavior. It accurately states that rows are returned when cells contain the query, and mentions case-insensitivity and the limit. However, it does not explicitly state that the operation is read-only or describe potential errors (e.g., invalid query format), leaving some behavioral 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, concise sentence that covers the key aspects: dataset, search condition, case sensitivity, and limit. Every word contributes to clarity without redundancy, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description fully specifies the input behavior (search for text in any cell) and the output constraint (up to 50 rows). It does not mention ordering or pagination, which could be relevant for large results, but for a simple search operation, the provided context is sufficient. The absence of an output schema is acceptable given the straightforward nature of the 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?
The schema describes the 'query' parameter and its minLength, and the description clarifies that it is searched case-insensitively. The 'limit' parameter has type and range constraints but no description in the schema; the description mentions 'up to 50', which provides partial semantic coverage. Given 50% schema description coverage, the description adds some value but not comprehensive parameter documentation.
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: searching rows of the WalkthroughDesk dataset based on cell content. The verb 'search' and the explicit dataset name make the purpose unambiguous. It also distinguishes from sibling tools like dataset_columns and dataset_stats, 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 specifies that the search is case-insensitive and limited to 50 rows, providing clear usage constraints. While it doesn't explicitly compare against sibling tools, the distinct purpose is evident from the description, and the inclusion of the limit parameter offers practical guidance.
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 WalkthroughDesk 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 full disclosure burden. It adds valuable behavioral detail: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. It does not disclose return format or error behavior for missing columns, but the edge-case handling is genuinely informative.
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 dense sentence that front-loads the computed metrics and adds only essential behavioral caveats immediately after. Every clause earns its place; no filler or restatement of the title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity — one parameter, no output schema, no annotations — the description covers the purpose, the dataset context, and important parsing behaviors. It omits the exact response structure, but the listed statistics effectively convey what 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?
Schema coverage is 0% and there is a single 'column' parameter. The description compensates by specifying that the column must be numeric and belong to the WalkthroughDesk dataset, which is meaningful beyond the schema's bare type declaration. It could be richer still (e.g., exact column-name syntax, behavior for fully non-numeric columns), but it gives enough context for correct invocation.
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-resource pair: computing summary statistics (count, min, max, mean, median, sum) for a numeric column of a specific dataset. This clearly distinguishes it from siblings like dataset_row, dataset_search, and dataset_top, which address 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 the tool is relevant — when aggregate numeric summaries are needed — but does not explicitly state when not to use it or name the alternative for other cases. The sibling tool names offer context, but no explicit selection guidance is provided.
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 WalkthroughDesk 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 carries the full burden of behavioral disclosure. It conveys ranking direction and numeric-column requirement but does not state what the output looks like, how ties are handled, or whether this is a read-only operation.
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 with no filler. The key ranking purpose is front-loaded, though the trailing parenthetical phrase adds only modest 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 tool with no output schema and sparse parameter documentation, the description leaves important gaps: it does not describe the returned row structure, the default limit behavior, or how to combine limit and ascending effectively. It is adequate for simple use but not fully self-contained.
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 adds meaning by specifying the column must be numeric and that highest/lowest ranking is supported, but it does not explain the limit parameter beyond what the schema's min/max 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 the tool ranks rows of the WalkthroughDesk dataset by a numeric column and returns the highest or lowest rows. It does not explicitly contrast with sibling tools, but the ranking behavior is distinct enough from search, stats, and row retrieval.
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" implies usage for top/bottom ranking questions. However, it gives no explicit guidance on when to prefer this tool over dataset_search or dataset_stats, and no exclusions or alternatives are mentioned.
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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RunbookDesk: the site's own MCP server — dataset; every answer cites the site.
EmployeeDeskHQ: 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 clear role: schema, provenance, exact row lookup, substring search, compare, stats, and top/bottom rows. dataset_row and dataset_compare both filter rows, but the distinction between exact single-value lookup and ordered multi-value comparison is clear enough from the descriptions.
All tools follow a consistent dataset_<noun> pattern, making the tool family immediately recognizable and predictable. No mixed styles or vague verbs are present.
Seven tools is well-scoped for a dataset exploration server: schema, provenance, lookup, search, comparison, stats, and ranking cover the core operations without unnecessary redundancy.
The tool surface covers schema discovery, provenance, exact lookup, substring search, comparison, numeric statistics, and ordering, which covers most common dataset questions. Minor gaps exist such as no general multi-condition filtering or pagination for search results, but agents can work around these.