Orgplanly: 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 Orgplanly 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 burden of behavioral disclosure. It indicates what the call returns and implies a non-mutating schema-discovery operation, but it does not explicitly state read-only behavior or clarify the relationship between the 'provenance banner' and the dataset_provenance sibling.
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 that front-loads the key return values (columns, numeric flags, row count) and ends with an actionable instruction. No filler, though the phrase 'provenance banner' is slightly jargon-heavy.
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-discovery tool, the description names the main output components and tells the agent when to invoke it. There is no output schema, so exact formatting is unspecified, but the description provides enough to support a correct first call.
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 baseline is 4 and no parameter explanation is needed. The description adds useful context about what the returned metadata contains.
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 a metadata/schema-introspection tool for a specific dataset ('Orgplanly dataset') and lists what it returns: columns, numeric flags, row count, and provenance banner. It does not explicitly differentiate from sibling tools like dataset_provenance or dataset_stats, but 'Call this first to learn the schema' establishes its role.
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 explicit usage timing: 'Call this first to learn the schema,' which is actionable guidance for an agent. It does not mention when not to use it or name alternative sibling tools, but the sequencing instruction is clear enough.
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 Orgplanly dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It does disclose that rows are returned in the order of the given values, which is a specific behavioral trait beyond the schema. However, it does not state whether all matching rows are returned, how case sensitivity is handled, or any read-only guarantees, leaving gaps in 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 a single, tightly packed sentence with no redundant words. It front-loads the core action and then adds the purpose, making it easy for an agent to scan and understand.
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 tool with only two parameters and no output schema, the description covers the essential aspects: what it does, how the parameters relate, and the ordering behavior. It omits edge cases like empty results or error handling, but given the tool's low complexity and the lack of annotations, it is reasonably 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 description coverage is 0%, so the description must explain the parameters. It does so effectively: 'column' is the field to filter on, 'values' are the list of values to match, and it clarifies that row order follows the order of 'values'. This adds meaningful semantics that the schema alone does not convey.
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: it returns rows from the Orgplanly dataset where a specified column matches any of a given set of values, preserving the order of those values. The phrase 'for "X vs Y" questions' gives a concrete use case that distinguishes it from siblings like dataset_row (single row) or dataset_search (broader 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 hints at usage with 'for "X vs Y" questions', indicating a comparison scenario, but it does not explicitly mention when to avoid this tool or recommend alternatives. It lacks exclusions or references to sibling tools, so the guidance is implied rather than explicit.
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 Orgplanly dataset. Read this to attribute a figure correctly.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It signals a read-only informational operation via 'Read this' and discloses the provenance fields the agent will encounter. It doesn't describe output formatting, but for a zero-parameter metadata lookup this is largely sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences front-load the substantive fields and close with the intended use case. Every phrase earns its place, with 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 simple metadata tool with no parameters, the description supplies the provenance fields, the resource, and the correct usage context. It is complete enough for an agent to invoke and understand the result without an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the no-params baseline applies. There is no parameter meaning for the description to add, and the described content matches the empty-schema design.
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 the exact resource (Orgplanly dataset) and enumerates the specific contents returned (source, computed date, licence, citation). It is clearly distinct from sibling tools like dataset_search or dataset_stats, and the action 'Read this' adds operational clarity.
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 an explicit use case: read this to attribute a figure correctly. It doesn't name alternative tools or exclusions, but for a provenance-specific metadata lookup the when-to-use guidance is clear enough.
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 Orgplanly 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 disclose behavior. It mentions case-insensitive matching, which is helpful, but it does not disclose whether the tool returns a single row or multiple rows (the title says 'a row' while the description says 'rows'), nor does it mention read-only behavior, error handling, or what happens when no match is found. Significant behavioral gaps remain.
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 front-loads the core functionality. It is efficient and readable, though it sacrifices depth for brevity. No redundant phrasing or filler exists.
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, the description is incomplete. It does not clarify the number of rows returned (singular vs. plural ambiguity), the output format, or how it handles missing values or non-existent columns. Given the sibling tools (especially dataset_search), the lack of differentiation and absence of return semantics make it insufficient for confident tool 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?
Schema description coverage is 0%, so the description must compensate. It only says 'column equals a value', which mirrors the parameter names without explaining how to specify column names, whether values need escaping, or any format expectations. The description adds minimal semantic value beyond the schema's property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool returns rows of the dataset where a column matches a value exactly, and the title adds 'exact key' to convey the lookup nature. It is clear about the resource (dataset rows) and the condition, but it does not explicitly contrast with the sibling dataset_search, which likely offers fuzzy or more complex search. Still, the core 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?
There is no guidance on when to use this tool versus alternatives like dataset_search or dataset_top. The description only states what it does, leaving the agent to infer that it is for exact key lookups. No exclusion criteria or recommended use cases are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Orgplanly 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, the description carries the full burden. It adds meaningful behavioral details: case-insensitive matching and an upper bound of 50 rows. It does not disclose output shape, ordering, or behavior when no rows match, but the core behavior of a read-only search is stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no filler. The key qualifiers—case-insensitive and up to 50—are immediately visible, making it easy for an agent to parse quickly.
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?
Adequate for a simple search tool: the description states the main action, target, matching semantics, and result cap. However, with no output schema, no annotations, and no sibling routing, an agent is left to infer output format and how limit behaves, 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?
The schema documents query but not limit, giving 50% coverage. The description adds value by noting case-insensitive matching for query and the 50-row cap, which overlaps with limit's schema maximum. It does not explain whether limit is optional, its default, or exactly how it applies, so it only partially compensates for the schema gap.
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 returns: rows of the Orgplanly dataset where cells contain the query, with case-insensitive matching and a 50-row cap. It clearly distinguishes from siblings like dataset_columns or dataset_stats by framing the result as matching rows.
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 use case is implied: use this when you want rows matching a text query across any cell. However, there is no explicit guidance on when to prefer it over sibling tools like dataset_columns, dataset_row, or dataset_stats, nor any exclusions.
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 Orgplanly dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which adds meaningful behavioral context beyond the title.
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 returned statistics and appends important handling caveats. Every phrase earns its place with no 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 simple one-parameter tool with no output schema, the description adequately covers the returned statistics and key edge-case behavior. It omits error handling for invalid column names, but overall it is sufficiently complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It clarifies that the 'column' parameter refers to a numeric column, adding meaning to the otherwise undocumented parameter. It doesn't provide exhaustive detail, but it gives the essential constraint.
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 enumerates the exact statistics (count, min, max, mean, median, sum) and identifies the target resource as a numeric column of the Orgplanly dataset. This clearly distinguishes it from sibling tools like dataset_top or 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 makes the tool's scope evident, but it does not explicitly state when to use this tool over siblings or mention exclusions. Usage context is implied rather than directly guided.
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 Orgplanly 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 at all, the description carries the full burden, but it only restates highest/lowest ordering. It does not disclose behavior on ties, missing/null values, non-numeric columns, or the shape of the returned rows, which an agent would need to anticipate.
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 no fluff, and the title reinforces the operation. Minor awkwardness in the em-dash phrasing keeps it from being a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, but with no output schema and no annotations, the description should say what the returned result looks like and clarify edge behavior. It currently leaves output shape and fallback behavior undefined, and the sibling context is 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?
Schema coverage is only 33%, and the description helps by clarifying that column must be numeric and that ascending corresponds to lowest-first. However, it adds nothing about limit or any additional constraints beyond what the schema already specifies.
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 a specific operation: returning the highest or lowest rows of the Orgplanly dataset ranked by a numeric column. It is clear, but it does not explicitly contrast with siblings like dataset_stats or dataset_search, so differentiation is left to inference.
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 given on when to choose this tool over the sibling tools, nor when not to use it. The intended use is implied by the title and 'most/least' phrasing, but no alternatives or exclusions are named.
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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Orgbix: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Each tool maps to a distinct query mode—schema, provenance, exact match, substring search, ordered multi-value comparison, numeric stats, and top/bottom ranking—so an agent can generally choose based on question type. The only mild overlap is between dataset_row and dataset_compare for single-value lookups, but the wording clarifies exact equality versus ordered multi-value matching.
All seven tools share the dataset_ prefix and consistent snake_case, making the family instantly recognizable. The suffix varies between noun-like and verb-like forms, but the pattern remains predictable and readable across the whole set.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a necessary operation without redundancy, and none feel superfluous or missing.
The toolset covers the full range of common data exploration needs for the Orgplanly dataset: schema discovery, provenance attribution, exact lookups, substring search, comparisons, numeric summaries, and ranked extremes. For a read-only dataset server, there are no obvious dead ends or significant gaps.