Deflectvo: 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 Deflectvo 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 disclosure. It describes the output contents (columns, numeric flags, row count, provenance banner) and implies a read-only, safe operation. It does not mention edge cases or detailed response formatting, but for a schema-discovery tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. The output components are listed first, and the usage directive is concise and front-loaded. 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 no-argument tool with no output schema, the description fully explains what an agent will receive. It covers all essential return items and gives a clear call order. No additional context is needed 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, so the schema is trivially complete. The description adds value by clarifying what the output will contain, which is the only relevant semantic information for an agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool returns: columns, which are numeric, row count, and provenance banner. It also positions it as the first call to learn the schema, distinguishing it from sibling data-retrieval 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?
Explicitly instructs to 'Call this first to learn the schema', which is a clear usage directive. It does not explicitly name alternatives or say when not to use it, but the context strongly implies it is a preliminary schema-discovery tool, distinct from the data-querying siblings.
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 Deflectvo 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 is the only behavioral disclosure. It usefully reveals OR-style matching and order preservation, but it does not specify exact-match semantics, case sensitivity, duplicate or no-match handling, or the output side-by-side representation. Useful but incomplete.
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 efficient sentence that front-loads the selection rule and adds the intended use case. No filler or repetition of the schema.
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 read-style tool, the description is mostly complete: it names the selection rule, the ordering, and the intended comparison use case. The lack of an output schema or annotations makes the omitted match/empty-result details more noticeable, but they are not fatal for this 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 description coverage is 0%, so the description carries the semantics burden. It explains that column is the field to match and values are the set of values to compare, and it ties them to the output ordering. This is meaningful semantic content beyond the bare 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 title 'Compare rows side by side' plus the description states the operation and the exact row-selection rule: rows whose column value is any of the supplied values, in the supplied order. This clearly distinguishes it from single-row siblings like dataset_row and from search-oriented 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 targets 'X vs Y' questions, which tells an agent when this tool is appropriate. It does not name alternatives or give exclusion criteria, so it stops short of a 5.
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 Deflectvo 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 disclosure. It states the output content (source, date, licence, citation) and implies a read-only operation. It does not explicitly confirm non-mutating behavior, but for a provenance metadata tool this is reasonable and clearly non-destructive.
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 wasted words. The key facts (source, date, licence, citation) are front-loaded, and the usage note follows. Every sentence 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 simple metadata tool with no parameters and no output schema, the description fully covers what an agent needs: what the tool returns and why to use it. No missing prerequisites or side effects need disclosure.
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 schema is empty (100% coverage by definition). Baseline for 0 parameters is 4. The description adds no parameter information because none exist, which 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 states the tool returns provenance metadata: source, date computed, licence, and citation. It also gives the purpose (correctly attributing a figure). This distinguishes it from sibling tools that operate on data rows, columns, and statistics.
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 a clear usage context: read this to attribute a figure correctly. It implicitly tells the agent when to use it (when citation/provenance is needed) but does not explicitly mention alternatives or exclusion criteria. Since the tool is unique in purpose, the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The rows of the Deflectvo 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?
No annotations are provided, so the description carries the full burden. It discloses only the case-insensitive matching behavior, but does not mention whether it returns all matching rows, ordering, pagination, error behavior for missing values, or any side effects (though read-only is implied). This is minimal transparency for a lookup 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 a single sentence with no redundancy, front-loading the dataset name and the matching condition. It is concise and easy to parse, though the phrasing is slightly awkward ('The rows... where') but acceptable.
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?
With no output schema, the description should clarify the return format, but it only states it returns 'rows' without specifying whether it is a list, a single row, or how matches are ordered. It also does not address edge cases like no matches or invalid column names. For an agent to use this tool reliably, more detail is needed.
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% (no parameter descriptions), so the description must compensate. It clarifies that 'column' is the field to match on and 'value' is the exact value to compare, and adds the case-insensitivity qualifier. However, it does not explain constraints like valid column names, expected value format, or how multiple matching rows are handled. Some value is added, but gaps remain.
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 the tool retrieves rows from a dataset based on an exact column-value match, with case-insensitivity explicitly stated. The verb is implied rather than explicit ('The rows... where'), but the title 'Look a row up by an exact key' reinforces the purpose and differentiates it from fuzzy search tools like 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 implies usage for exact, case-insensitive matches, which distinguishes it from dataset_search (likely fuzzy). However, it does not explicitly state when to prefer this over siblings, provide exclusions, or mention any prerequisites or limitations. Guidance is only implied by the phrase 'exactly (case-insensitive)'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Deflectvo 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?
The description discloses useful behavioral details beyond the title, including case-insensitive matching and the 50-row limit. But with no annotations and no output schema, it leaves unspecified what a result row looks like, whether the limit is a default or a hard cap, and how empty results are handled.
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 delivers the matching semantics, case sensitivity, and result cap with no filler. The behavior is front-loaded and every phrase adds information.
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 read-only search over a dataset, the description provides the essential operational facts: what is searched, how matching works, and the maximum result size. It does not document the result fields, but the tool's simplicity and the absence of an output schema keep this from being a major gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the query parameter by stating case-insensitive cell containment, which is not fully captured by the schema's one-line description. The limit parameter, however, is only covered by the schema's min/max values; the description does not clarify whether 'up to 50' is the default, a hard cap, or tied to the limit parameter.
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 Deflectvo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly separates it from the dataset_stats, dataset_top, and dataset_columns siblings by establishing it as the content-search operation.
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?
Usage is implied from the description: an agent can infer it should call this when it needs rows matching a string in any cell. However, it does not explicitly state when to prefer this over dataset_row, dataset_columns, or the other sibling tools, and it gives no 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 Deflectvo 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 full burden. It discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is valuable behavioral context. It doesn't mention return format or error cases, but the disclosed details go beyond the schema.
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 and then adds handling details. Every word is informative, with no fluff 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 tool with one parameter and no output schema, the description covers the core behavior: what stats are computed, how formatting is handled, and how non-numeric rows are treated. It lacks explicit output format details, but that is often not required. The description is adequate for the tool's simplicity.
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 should compensate. It mentions 'numeric column' but doesn't explicitly clarify the expected format of the 'column' parameter (e.g., exact column name string). The parameter name and type are simple, but the description adds minimal value over the schema for parameter semantics.
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 computes specific summary statistics (count, min, max, mean, median, sum) for a numeric column of the Deflectvo dataset. This is a specific verb+resource combination that distinguishes it from sibling tools like dataset_columns (listing columns) or dataset_row (fetching a row).
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 obtaining summary statistics on a column but does not explicitly state when to choose this over siblings like dataset_top or dataset_compare. No when-not-to-use conditions are provided, so guidance is only 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 highest (or lowest) rows of the Deflectvo 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 states the core sorting behavior but omits important details such as default limit if not specified, handling of non-numeric values in the column, and the structure of the returned rows. This is thin for a tool with no 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 sentence that front-loads the core action and is free of fluff. The parenthetical example is slightly informal but does not detract from clarity; it remains concise and structured.
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 with 3 parameters and no output schema, the description is adequate for basic invocation. However, it omits the default limit value and does not describe the return format, which an agent might need for correct handling. Missing these details leaves it incomplete.
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% (only 'ascending' has a description). The description adds that 'column' must be numeric, but it does not clarify 'limit' semantics or default, nor does it elaborate on how 'ascending' interacts beyond the schema. It fails to compensate for the undocumented parameters.
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?
Clearly states it ranks rows of the Deflectvo dataset by a numeric column, returning highest or lowest. The phrase 'which is the most/least X' makes the intent obvious and distinguishes it from siblings like dataset_row (single row) and dataset_stats (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?
Usage is implied by the purpose—use this to get top/bottom rows by a numeric column—but there is no explicit guidance on when to choose this over alternatives like dataset_stats or dataset_search. No exclusions or alternative references are provided.
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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Funnelvo: the site's own MCP server — dataset; every answer cites the site.
Exitvo: the site's own MCP server — dataset; every answer cites the site.
Duesvo: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Tools are mostly distinct: columns, provenance, stats, and top are clearly separate. There is some overlap between dataset_compare, dataset_row, and dataset_search for retrieving rows, but the descriptions clarify exact vs substring vs multi-value filtering, reducing confusion.
All tools share the 'dataset_' prefix, providing strong consistency. The second part mixes nouns (columns, provenance, row, stats, top) and verbs (compare, search), which is a minor deviation but still predictable and readable.
With 7 tools, the server is well-scoped for a dataset querying purpose. Each tool addresses a distinct query pattern (schema, provenance, search, exact match, comparison, stats, top/bottom) without redundancy or bloat.
The tool surface covers the main query types needed for exploring a dataset: schema, provenance, search, filter, stats, and ranking. Missing a 'list all' or 'distinct values' tool, but for typical analytical questions the coverage is strong and no dead ends are apparent.