Sowbird: 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 Sowbird 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. It reveals the returned content (columns, numeric flags, row count, provenance banner) and implies a non-mutating schema-introspection operation. However, it does not explicitly state that the call is read-only, has no side effects, or describe any constraints such as caching or dataset availability.
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, dense sentence with no filler. The key instruction "Call this first" is front-loaded, followed by a precise list of what the tool returns, making it maximally efficient for agent consumption.
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 low complexity (0 parameters, no output schema) and simple introspection purpose, the description is complete enough for an agent: it states the return contents and the recommended usage order. Minor context like the format of the provenance banner is omitted, but nothing essential for selecting and invoking the tool is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty input schema, so there are no parameter semantics to document. The description correctly focuses on the output artifact instead of parameter details, which is appropriate for this 0-parameter tool.
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 exposes: columns, numeric column flags, row count, and the provenance banner for the Sowbird dataset. It also tells the agent to call it first to learn the schema, which differentiates it from sibling operations like dataset_search or dataset_stats, though it does not name an explicit alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
"Call this first to learn the schema" provides explicit timing and intent, which is clear usage guidance for a schema-learning step. It does not describe when not to use it or point to a specific sibling alternative, but the sequencing is practical and unambiguous.
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 Sowbird 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 burden of explaining behavior. It implies a read-only retrieval by describing rows being returned, but it does not explicitly state that there are no side effects or mutations.
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 concise and front-loaded, with the title and first clause immediately conveying the operation. The extra phrase about 'X vs Y' questions adds relevant context without 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?
The description provides enough context for the agent to understand the resource, the filtering mechanism, and the ordering behavior. It lacks an explicit output schema, but for a row-returning tool, the description is sufficiently complete 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 description explains both parameters: 'column' is the field to match, and 'values' are the allowed values to filter rows. It also clarifies that rows are returned in the order of the given values, adding meaning beyond the raw 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 clearly indicates that the tool returns rows from the Sowbird dataset matching any of the given values in a specified column, in the order provided. The title 'Compare rows side by side' reinforces the intended comparison use case, and the description differentiates it from single-row or search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states that the tool is for 'X vs Y' questions, which tells the agent when to use it for comparisons. It does not explicitly mention sibling alternatives, but the use-case hint is sufficient guidance.
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 Sowbird 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 behavioral burden. It signals a non-mutating metadata lookup ('Read this') and discloses the exact content returned. It does not describe output format or any side effects, but for a zero-parameter provenance tool this is a minor omission rather than a transparency failure.
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 well-structured sentence, preceded by a clear title, front-loads the attribution use case and wastes no words. Every part of the description adds information an agent needs.
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: no parameters, no annotations, no output schema, and low complexity. The description enumerates the returned provenance fields and states why the agent would invoke it. Nothing needed to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool accepts zero parameters, so the baseline of 4 applies. The input schema already shows an empty object, and the description has no parameters to explain.
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 a specific resource (the Sowbird dataset) and enumerates exactly what it returns: source, computed date, licence, and citation. The phrase 'Read this to attribute a figure correctly' makes the tool's purpose unmistakable and clearly distinguishes it from structural/statistical siblings like dataset_columns and dataset_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit use case: attribute a figure correctly. It does not enumerate when not to use it or name alternatives, but no sibling appears to serve a provenance/citation role, so the guidance is clear enough without exclusions.
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 Sowbird 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 states the matching behavior but is ambiguous about whether it returns one row or multiple rows (title says 'a row', description says 'rows'). It also does not mention ordering, limits, error behavior, or whether any mutation is possible; no annotations fill this gap.
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 concise and front-loaded, conveying the core behavior in one sentence with no redundant content.
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 is adequate for a simple lookup but lacks an output schema, error semantics, and a clear definition of the returned row(s). It also leaves the typo 'Sowbird' unresolved, which could affect agent interpretation.
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 gives parameter names with minLength constraints. The description adds that one is a column and the other is a value, but does not define accepted column names, value types, or how case-insensitivity is applied.
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?
Title and description clearly indicate the tool retrieves rows from a dataset based on an exact, case-insensitive column/value match. However, it does not explicitly distinguish when to choose this over dataset_search or dataset_compare, and the resource name 'Sowbird' appears typoed.
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 usage guidance is provided. The description does not state when to use this tool instead of siblings like dataset_search, dataset_compare, or dataset_top, nor any prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Sowbird dataset whose cells contain the query (case-insensitive), up to 50.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | text to look for in any cell |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal two meaningful traits: case-insensitive matching and a 50-row cap. However, it does not disclose result ordering, pagination behavior, what happens when more than 50 rows match, or whether the full row or only matching cells are returned.
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 conveys the result type, matching behavior, and limit with zero filler. Every clause adds useful information, and the description is an appropriate size for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter search tool with no output schema, the description covers the essential elements: what is returned (rows), how matching works (cells contain query, case-insensitive), and the hard cap (50). Minor omissions like result ordering and exact return format do not severely impair an agent's ability to invoke it, though more detail would help.
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%: query has a description, but limit does not. The description adds the case-insensitive detail and clarifies that the output is capped at 50, which partially compensates for the low coverage. It does not explain the default value or behavior of limit, or whether the 50-row cap is applied arbitrarily.
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 action (return rows) and resource (Sowbird dataset), and specifies matching semantics (cells containing the query, case-insensitive) and a result cap (50). This clearly distinguishes it from sibling tools like dataset_columns, dataset_row, and dataset_stats, which address different data views.
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 a full-text search use case but gives no explicit guidance about when to choose this tool over alternatives. It does not mention exclusions, such as 'use dataset_row for a single known row' or 'use dataset_stats for aggregates,' so an agent must infer appropriateness from the name and description.
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 Sowbird 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?
No annotations are provided, so the description carries the full burden. It discloses important behaviors: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a bare 'computes statistics' statement and gives the agent insight into data cleaning and edge-case handling.
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 list of statistics, then adds two parenthetical clarifications. Every clause earns its place, and there is no fluff. It is concise and well-structured for quick parsing.
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 one parameter and no output schema, the description covers the computed metrics and edge-case handling (non-numeric rows excluded and counted). It does not specify the exact return format (e.g., JSON keys) or error behavior for missing/empty columns, but it provides enough for an agent to invoke the tool and understand the result.
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, and schema description coverage is 0%. The description adds that the column is numeric within the Sowbird dataset, which helps infer intent, but it does not explain how to specify the column (e.g., exact name, case sensitivity) or what happens if the column does not exist. Some meaning is added but not enough to fully compensate for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly lists the statistics computed (count, min, max, mean, median, sum) and identifies the resource (Sowbird dataset). This is a specific verb–resource pairing that clearly distinguishes it from sibling tools like dataset_row or dataset_top, which handle raw rows or top values rather than summary aggregates.
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 usage for summary statistics but does not explicitly state when to use this tool over alternatives or provide exclusions. It names no sibling tools or conditions (e.g., 'use dataset_top for top-N values instead'). The context is clear but not explicit.
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 Sowbird 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. It mentions 'highest (or lowest)' and references a numeric column, but does not disclose default sort order, handling of ties, or the effect of the 'limit' and 'ascending' parameters. The behavior is partially described but not fully 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 sentence with an em dash and example, making it concise and easy to read. It is well-structured for a simple operation, though the example phrase is somewhat colloquial.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple ranking tool, the description provides a basic idea but omits details like return format, pagination, or edge cases (e.g., non-numeric data). The absence of an output schema and minimal behavioral disclosure leaves the description incomplete for robust use.
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 that 'column' should be numeric, but it does not explain the 'limit' parameter or the exact meaning of 'ascending' beyond the schema's own brief note. With schema coverage at only 33%, the description adds some context for 'column' but leaves limit and ascending under-explained.
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 the dataset by a numeric column, and provides a concrete example query ('which is the most/least X'). It is distinct from sibling tools like dataset_search or dataset_stats, which serve other 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 usage for ranking queries with the example 'which is the most/least X', which helps an agent know when to use it. However, it does not explicitly contrast with siblings like dataset_stats or dataset_search, leaving some room for ambiguity.
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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Enpso: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Each tool targets a distinct query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The descriptions clearly differentiate row/compare/search, though row and compare have some conceptual overlap.
All tool names follow the same `dataset_` prefix followed by a noun (columns, compare, provenance, row, search, stats, top), creating a predictable and consistent naming pattern.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool serves a unique purpose with no redundancy, covering schema, metadata, lookup, search, comparison, statistics, and ranking.
The set covers schema, provenance, exact and substring search, comparisons, summary stats, and extremes, which handles most dataset Q&A needs. Minor gaps like group-by aggregation or pagination are not critical for the apparent purpose.