Sacristo: 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 Sacristo dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It enumerates the returned information (columns, numeric flags, row count, provenance banner), which is useful, but does not mention any potential side effects, performance implications, or error conditions. This is adequate for a read-only metadata tool but not rich.
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 with no wasted words. The core content (what it returns) is front-loaded, and the usage directive is appended efficiently. This is a model of conciseness.
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 tool, the description covers what it returns and when to call it. It does not describe the exact output format or data types, but given the simplicity of the tool and the lack of an output schema, it is sufficient for an agent to understand its role.
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. The description does not need to explain parameters; instead it focuses on the output, which adds value beyond the empty 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 states a clear purpose: it returns columns, numeric flags, row count, and provenance banner. The phrase 'Call this first to learn the schema' adds a usage directive that helps distinguish it as the entry point, though it does not explicitly name alternatives.
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?
It explicitly instructs to call this tool first to learn the schema, providing a clear context for when to use it. However, it does not mention when not to use it or name sibling tools as alternatives, leaving some inference to the agent.
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 Sacristo 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 burden of behavioral disclosure. It does state the core behavior (filter by column values, preserve order), but it omits any mention of output format, potential limitations (e.g., max rows, empty results), or that it is a read-only operation. This is a moderate gap given the lack of annotation support.
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 conveys the essential purpose and the key behavioral detail (ordering). It is front-loaded with the main action and use case, with no superfluous wording. It could be slightly more structured but remains appropriately concise.
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 only two parameters and no output schema, the description covers the filtering logic and order but leaves gaps: it does not specify the return format (rows as objects? arrays?), what constitutes a 'compare' result, or error handling for no matches. Given the simplicity, it is mostly adequate but 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?
Schema description coverage is 0%, so the description must compensate. It explains the relationship between 'column' and 'values' (filtering by equality to any value) and that the order of values determines row order. However, it does not clarify details like case sensitivity, exact matching semantics, or what happens with duplicate values, leaving some ambiguity.
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: retrieving rows from the Sacristo dataset filtered by a column matching any of the given values, preserving the order of those values. It also hints at the use case ('X vs Y' questions). While the verb is implicit, it is clear enough to distinguish from siblings like dataset_search (which likely does broader text search) and dataset_row (single row lookup).
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 usage hint, implying it is appropriate when comparing a few specific values side by side. However, it does not explicitly state when not to use it or name alternative tools, leaving the agent to infer the selection from the sibling names.
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 Sacristo 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 present, so the description carries the full disclosure burden. It implies a read-only informational operation and lists the fields returned, but it does not explicitly state return format, side effects, or the significance of 'date it was computed' (e.g., staleness). No contradiction exists, but disclosure is minimal.
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 communicate the full purpose with no filler. The description first enumerates the returned fields, then states the actionable reason to read it. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-argument tool, the description is nearly complete: it names the dataset, the fields provided, and the intended use. It omits the output format (text, JSON, etc.), but the field list and title largely cover what an agent needs to invoke and interpret the call 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?
The tool has zero parameters, so the input schema is already exhaustive. The description adds value by clarifying the fixed target dataset ('Sacristo') and the semantic content of the result, which the schema cannot convey. Baseline 4 for zero params is appropriate.
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 provenance/metadata lookup for the Sacristo dataset and enumerates the exact contents: source, computed date, licence, and citation. This is immediately distinguishable from sibling tools like dataset_columns 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 sentence 'Read this to attribute a figure correctly' provides a clear, actionable use case. It does not explicitly contrast with sibling tools or state when not to use it, but the context is unambiguous enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The rows of the Sacristo dataset where a column equals a value exactly (case-insensitive).
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It adds the case-insensitive matching detail and indicates that rows are returned, but it does not state whether multiple matches are returned, what happens when there is no match, or explicitly confirm that the operation is read-only. Some transparency exists, but meaningful 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 sentence with no filler, and it conveys the core exact-match behavior efficiently. It loses a point because the key qualifier appears at the end rather than being front-loaded, and the nominal phrasing 'The rows...' is less direct than an imperative verb phrase.
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 exact lookup, the description conveys the essential inputs and the matching rule. However, with no annotations and no output schema, it leaves edge-case behavior such as no matches or multiple matches, and safety expectations, unstated. It is minimally viable but not 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 compensate for the bare string parameters. It does clarify that 'column' is a dataset column and 'value' is the exact value to match, but it does not explicitly map parameter names or add format/type constraints beyond what the schema already provides. This is only partial compensation for the lack of schema descriptions.
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 'Look a row up by an exact key' and the description 'rows ... where a column equals a value exactly (case-insensitive)' clearly identify the operation and resource. The exact-match qualifier distinguishes it from dataset_search in effect, but no sibling is named explicitly, so it does not fully meet the 5-level bar.
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 'exactly (case-insensitive)' implies this tool is for exact key lookups rather than fuzzy or full-text search, but there is no explicit when-to-use guidance or mention of alternatives like dataset_search. An agent can infer the intended use, but the description does not spell out exclusions or when a different tool would be preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Sacristo 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 behavioral burden. It discloses case-insensitive matching and a 50-row cap, which are non-obvious and valuable. However, it does not mention return row structure, ordering, read-only behavior, or what happens when no rows match.
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 with no filler. It front-loads the resource and the core action, then adds the two most important behavioral modifiers (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 with no output schema, the description tells the agent what will be returned (rows of the dataset) and how many. Some detail about the returned rows' structure would strengthen it, but nothing essential for a correct first invocation 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 schema documents 'query' as text to look for in any cell, but the description clarifies it is case-insensitive and clarifies the effect of 'limit' by saying results are capped at 50. This compensates well for the 50% schema description 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 specifies the resource (Sacristo dataset), the operation (search rows whose cells contain the query), and the matching behavior (case-insensitive). This clearly distinguishes it from sibling tools like dataset_columns, dataset_stats, or dataset_top.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The condition 'cells contain the query' makes the intended use clear: find rows by cell content. It does not explicitly name alternatives or exclusion cases, but the context is unambiguous enough for an agent to select this tool when a text search across rows is needed.
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 Sacristo 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 does disclose useful quirks: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. However, the phrase 'excluded and counted' is ambiguous (whether count includes non-numeric rows), and there is no mention of error behavior or return format.
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 conveys the core function, the exact statistics, and the notable data-cleaning behaviors. It is front-loaded with the most important information 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?
Given the tool's small parameter surface and simple statistical role, the description covers the essential inputs, behaviors, and outputs. It lacks an explicit output-structure description since there is no output schema, but listing the computed statistics is sufficient for an agent to infer expected results.
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 is only one parameter, 'column', and schema description coverage is 0%, so the description must compensate. It does this by stating that the parameter references a numeric column of the Sacristo dataset and by explaining how value formatting is handled. It stops short of providing examples or explicit valid-value guidance, but for a single string column name this is largely sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly names the resource ('Sacristo dataset') and the exact statistics computed (count, min, max, mean, median, sum), making the tool's purpose clear. It does not explicitly differentiate from sibling tools by name, but its statistical scope is distinct from row, search, and top 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 phrase 'numeric column' implies the appropriate use case, and the exclusion of non-numeric rows hints at what kinds of data are acceptable. However, there is no explicit guidance about when to choose this tool over siblings like dataset_row, dataset_search, or dataset_top.
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 Sacristo dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral details but does not. It fails to mention sorting behavior, output format, handling of ties or missing values, or the fact that only numeric columns are valid. This leaves significant ambiguity for an agent.
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, stating the core purpose in a single sentence. It avoids redundancy but could be slightly more structured to include parameter or usage hints.
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 be self-sufficient. It leaves out critical details such as return structure, error conditions, and how parameters interact. An agent would likely need to inspect the schema or make assumptions to call it 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?
Schema description coverage is only 33%, yet the description adds little. It hints that the column must be numeric but does not explain 'limit' or 'ascending' beyond what the schema already states. Given the low coverage, the description should compensate but does not.
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 clear, specific purpose: ranking rows of the Sacristo dataset by a numeric column to find the highest or lowest. The phrase 'which is the most/least X' makes the intent unmistakable and distinguishes it from siblings like dataset_search or 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?
No explicit guidance on when to use this tool versus alternatives. The description implies usage for ranking queries but does not mention any exclusions, alternatives, or conditions that would help an agent decide between this and sibling tools like dataset_stats or dataset_search.
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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Sbarvo: the site's own MCP server — dataset; every answer cites the site.
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Dsarvo: the site's own MCP server — dataset; every answer cites the site.
Sopvo: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Most tools target clearly distinct operations—schema, provenance, search, stats, top, and exact lookup. Minor overlap exists between dataset_compare and dataset_row, both filtering rows by column values, but the descriptions clarify that compare handles multiple values while row is for a single exact match.
All tools follow a uniform dataset_<operation> pattern with consistent snake_case naming. The suffixes are a mix of nouns and verbs, but the prefix and format are fully predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct query need without redundancy.
The toolset covers schema discovery, provenance, exact and fuzzy matching, multi-value comparison, numeric statistics, and top/bottom rankings—good coverage for a read-only dataset exploration server. A possible minor gap is lack of a distinct-values or category-listing tool, but common analytical questions appear addressable.