Lessonvo: 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 Lessonvo 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 full burden of behavioral disclosure. It implies a read-only metadata probe by describing schema learning and output contents, but it does not explicitly state read-only behavior, permissions, or side effects. The 'learn the schema' phrasing partially compensates for 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 a single sentence that front-loads the specific output items and ends with a clear directive. Every clause contributes value, 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?
With no parameters and no output schema, the description's enumeration of outputs (columns, numeric indicators, row count, provenance banner) is sufficient for an agent to call the tool and interpret results. It could explicitly state read-only behavior, but the tool's simple metadata role makes the current information largely 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 input schema has zero properties, so there are no parameters to document. The description adds context about what the returned schema contains, satisfying the baseline expectation for a 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 concretely lists the outputs (columns, numeric flags, row count, provenance banner) for the Lessonvo dataset, making the purpose clear. It lacks an explicit action verb like 'returns' or 'lists,' but 'Call this first to learn the schema' clarifies the operation and differentiates it from sibling 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 gives an explicit timing directive: 'Call this first to learn the schema.' This tells an agent when to use the tool relative to other dataset operations. It does not name alternatives or exclusions, but the 'first call' guidance is strong and contextually clear.
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 Lessonvo 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 must carry behavioral disclosure. It reveals that results are returned in the order of the given values, which is a useful detail. However, it does not specify whether full rows are returned, what happens on no matches, or any side effects. The description is not misleading but lacks depth.
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 conveys the core function and a usage context. It is concise and front-loaded with the action, though the 'X vs Y' phrase could be clearer to some agents.
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 no annotations and no output schema, the description provides essential parameter semantics and ordering behavior. However, it leaves ambiguity about output structure (e.g., does it return all columns or just the matching values?) and does not address edge cases like empty results or data types beyond what the schema implies.
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 explain the parameters. It effectively does: 'column' is the field to match, and 'values' are the list of values to filter by, with ordering implied. This adds meaning beyond the raw schema definitions.
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 returns dataset rows filtered by a specified column and matching any of the given values, with a specific ordering. It distinguishes itself from siblings like dataset_row (single row) and dataset_search (general search) by stating it's for 'X vs Y' questions, though it lacks an explicit verb like 'retrieves' or 'lists'.
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 provides a usage hint via 'for X vs Y questions', suggesting when it should be used, but does not explicitly mention alternatives or state when not to use it. No clear exclusions or comparisons to sibling tools are provided.
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 Lessonvo 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 must disclose behavioral traits. It states the tool is for reading ('Read this') and enumerates the returned data (source, date, licence, citation), implying a safe, non-mutating operation. However, it does not explicitly confirm read-only behavior, mention any prerequisites (e.g., dataset existence), or describe error conditions. It adds some behavioral context but not a full picture.
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, front-loaded with the core purpose (what it provides) and a clear directive for when to use it. No filler or redundancy; every word contributes to the tool's utility.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless, read-only tool with no output schema, the description fully covers what the tool does, what data it returns, and when to use it. There is no missing information an agent would need to invoke 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?
The tool has zero parameters, and the schema is empty. Per the rubric, a zero-parameter tool gets a baseline of 4. The description adds no parameter-specific details because there are none to document, and the schema coverage is trivially 100%. The description correctly focuses on the output rather than inputs.
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 provides provenance information (source, date, licence, citation) for the Lessonvo dataset, and positions it as the go-to for attribution. The verb 'read' plus the specific resource and purpose distinguish it from sibling tools like dataset_columns or dataset_stats, which handle different facets.
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 gives an explicit usage context: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool. It does not enumerate alternatives or exclusions, but the sibling tools are clearly for different operations, so the usage intent is evident.
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 Lessonvo 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 carries the full burden of behavioral disclosure. It adds case-insensitivity as a behavioral trait, but omits whether all matches are returned, output format, ordering, or error handling for unknown columns. This is a significant gap 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 concise sentence that delivers the core semantics without waste. It earns its place, though it is terse enough that additional behavior details would improve it without hurting structure.
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 clarify what the agent receives (all rows vs one row, empty results, error behavior). It fails to state return semantics, so the description is not complete enough for confident invocation without additional probing.
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 meaningfully explains that 'column' is the field to match against and 'value' is the exact value to match, which is useful but does not specify valid column names or format constraints beyond the schema's minLength.
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 lookup operation: rows where a column equals a value exactly, case-insensitively. It names the resource (Lessonvo dataset) and the matching behavior, which distinguishes it from a fuzzy search tool, though it does not explicitly name siblings.
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 on when to use this tool over dataset_search or other siblings is provided. The exact-match semantics imply a use case, but the description never states exclusions or alternatives, leaving the agent to infer selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Lessonvo 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 usefully discloses case-insensitive matching and a 50-row cap, but it does not state whether the operation is read-only, how results are ordered, what the default limit is when omitted, or the behavior for zero matches.
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 contains all key facts: search scope, matching rule, case sensitivity, and result cap. No filler or repetition of schema details.
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 or annotations, the description gives the core behavior but misses return format, ordering, default-limit behavior, and side-effect clarity. An agent can invoke it correctly but may not anticipate the exact response shape.
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 50%, and the description adds meaning to both parameters: 'case-insensitive' enriches the query semantics, and 'up to 50' gives the limit parameter context beyond its schema min/max. Still, it doesn't specify the default limit when omitted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific operation: returning rows of the Lessonvo dataset whose cells contain the query, with explicit qualifiers (case-insensitive, up to 50). This clearly distinguishes it from sibling tools such as dataset_columns, dataset_top, or dataset_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?
No guidance is given about when to choose this tool over the listed siblings. There are no prerequisites, examples, or mention of alternatives for row lookup or dataset inspection; usage must be inferred entirely 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 Lessonvo 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 disclosure burden. It adds valuable behavioral context: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond the schema, though it doesn't cover edge cases or error behavior.
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 dense sentence with no filler, front-loading the key outputs and then adding formatting/edge-case detail. Every clause 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?
Given there is no output schema, the description usefully enumerates the returned statistics and even mentions the excluded-row count. It covers the essential behavior for a single-parameter tool, though exact return formatting is left unspecified.
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 in the Lessonvo dataset and explains how formatted and non-numeric values are treated, but it stops short of giving examples or a precise naming convention.
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 computed statistics (count, min, max, mean, median, sum) and scopes them to a numeric column of the Lessonvo dataset, so an agent knows exactly what the tool does. The distinction from siblings like dataset_top or dataset_row is clear without opening their schemas.
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 provides implied usage guidance: use this tool for summary statistics on a numeric column of the Lessonvo dataset. However, it does not explicitly state when to prefer it over alternatives or when not to use it, leaving the routing largely to inference.
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 Lessonvo 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 provided, the description carries the full burden and does state the core behavior: ranking rows by a numeric column in either direction. It does not disclose tie-breaking, null handling, default limit behavior, or whether the operation is purely read-only, which are meaningful gaps for a no-annotation 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 front-loaded sentence that communicates purpose, numeric-column requirement, and direction with no filler. The clarifying 'most/least X' example 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 top-N ranking tool, the description is minimally viable: it covers what the tool returns and which column type is required. Gaps remain around the meaning of an omitted 'limit', output shape (since there is no output schema), and edge-case behavior such as ties or missing values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33%, so the description must compensate. It adds important semantics by specifying that 'column' must be numeric and indicating the high/low direction. However, it does not explain the meaning or default behavior of 'limit' beyond what the parameter name and constraints imply.
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 the operation: returning the highest or lowest rows of a dataset by a numeric column. It is more specific than a plain 'rank' statement and gives a natural-language framing ('which is the most/least X'), though it does not explicitly contrast itself with sibling 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 implies when to use the tool through the 'most/least X' phrasing, which signals top-N queries. However, it does not explicitly state when to prefer this over siblings like dataset_stats or dataset_search, leaving exclusion guidance to inference.
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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Focusvo: 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.
Clientvo: the site's own MCP server — dataset; every answer cites the site.
Funnelvo: 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, exact match, multi-value match, substring search, stats, and top-N. The only potential confusion is between dataset_row and dataset_compare, which both fetch matching rows but differ in single vs. multiple values — the descriptions make this distinction reasonably clear.
All tools share the consistent 'dataset_' prefix in snake_case, which is good. However, the suffix mixes nouns (columns, provenance, row, stats) with verbs (compare, search), and 'dataset_top' is cryptic while 'dataset_row' is singular despite returning rows.
Seven tools is well-scoped for a single-dataset query server. Each tool earns its place covering a distinct query type: schema, attribution, exact lookup, set membership, substring search, aggregation, and ranking.
The surface covers schema, provenance, exact/partial lookup, comparison, stats, and top-N queries well. Obvious gaps include no way to page through or list all rows, no multi-condition (AND) filtering, and no group-by counts — limitations that may force agents to work around when answering comparison questions.