Sbarvo: 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 Sbarvo 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 does disclose the return content (columns, numeric flags, row count, provenance banner), which is the key behavioral trait for a read-only introspection tool. However, it doesn't mention prerequisites (e.g., whether the dataset must be loaded) or any failure conditions, though these are minor for a zero-parameter read operation.
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 zero waste: the first enumerates the return content, the second delivers the operational directive. Every word earns its place and the information is densely packed.
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, read-only introspection tool with no output schema, the description is adequately complete. It names what is returned and how to sequence it. The only gaps — explicit sibling differentiation and failure modes — are minor given the simplicity of the tool.
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 there is nothing to document beyond the schema. The description implicitly fixes the operating scope to 'the Sbarvo dataset,' which is the only contextual information needed. Baseline 4 applies for a parameterless 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 the tool returns columns, numeric flags, row count, and the provenance banner for the Sbarvo dataset — a specific resource with specific content. It distinguishes itself from the sibling set (provenance, stats, search, etc.) by being the schema-introspection tool. It's phrased as a noun phrase rather than an imperative verb+resource, but the meaning is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Call this first to learn the schema' is explicit, actionable guidance that tells the agent the correct sequencing before other operations. It doesn't explicitly name alternatives or state when NOT to use it, but the 'call first' directive effectively routes the agent here ahead of the data-oriented 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 Sbarvo 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 full burden for behavioral disclosure. It explains the selection and ordering behavior but does not mention whether the operation is read-only, has side effects, or any limitations (e.g., result size, error conditions). This lack of safety/behavioral detail is a 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, concise sentence that packs essential information: dataset, filtering logic, ordering, and use case. No redundant words or unnecessary detail, making it highly efficient.
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?
Although there is no output schema, the description clearly states that rows are returned in the given order, which implies the output is a set/array of rows. This is sufficient for the tool's simple purpose, and no further explanation of return structure is needed given the context.
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 covers all parameters (column and values) at 100% coverage, providing type and constraints. The description adds meaning by explaining that 'column' is the field to match against and 'values' are the allowed values, reinforcing the schema. Baseline of 3 is appropriate since no further detail is necessary.
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' and description clearly state the tool's purpose: selecting rows from the Sbarvo dataset based on a column matching any of the given values, ordered as provided. It explicitly mentions the use case 'for X vs Y questions', distinguishing it from sibling tools like dataset_search 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?
The description indicates when to use this tool ('for X vs Y questions') and specifies the behavior of returning rows in the given order. While it does not explicitly name alternative tools, the context implies this is for comparing specific values, which 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 Sbarvo dataset. Read this to attribute a figure correctly.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. The imperative 'Read this' implies a read-only, non-mutating operation, but the description does not explicitly state the absence of side effects or describe any output/format details beyond the listed fields.
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, no filler. The content payload is front-loaded and the usage guidance follows immediately. 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-parameter, no-output-schema metadata tool, the description is sufficient: it states what data is returned and why an agent would want it. No additional invocation or selection context 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?
The tool has zero parameters, so there is no parameter risk. The description adds meaning by enumerating what the provenance record contains (source, date, licence, citation), which is more than the empty schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource ('the Sbarvo dataset') and the exact informational payload (source, computation date, licence, citation). The title reinforces the purpose, and the sibling tool names (dataset_columns, dataset_row, etc.) make clear this is the only provenance-related tool.
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 tells the agent exactly when to use it: 'Read this to attribute a figure correctly.' It establishes a clear context of use, though it does not explicitly name alternative tools or exclusion conditions.
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 Sbarvo 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 of disclosing behavior. It only reveals that matching is exact and case-insensitive. It does not mention whether all matching rows are returned or just the first, how errors are handled (e.g., missing column, no match), or any performance or pagination details. For a tool with no annotations, this is minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence that leads with the resource ('rows of the Sbarvo dataset') and immediately states the matching condition. There is no redundant phrasing or fluff, making it highly efficient and easy to parse.
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 simplicity (two string parameters), the absence of an output schema, and no annotations, the description should cover more operational context. It lacks information on return format (e.g., list of rows, count), error behavior for invalid column names or no matches, and whether all matches are returned. This is insufficient for an agent to predict the tool's behavior confidently.
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 the roles of the two parameters: 'column' is the column to match on, and 'value' is the exact value to match. This adds meaning beyond the bare schema property names. However, it does not specify constraints like valid column names, value format, or that both are strings (though the schema states string types). With 0% schema description coverage, it partially compensates but could be more explicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: returning rows from the Sbarvo dataset that exactly match a given value in a specified column, with case-insensitive comparison. The verb 'look up' (from title) and the explicit condition 'where a column equals a value exactly' distinguish it from sibling tools like dataset_search (likely broader search) and dataset_top (likely top rows).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any explicit guidance on when to use this tool versus alternatives. It only states what it does, without mentioning that dataset_search should be used for partial or fuzzy matches, or that dataset_top is for sampling. No context about prerequisites or preferred use cases is given, leaving the agent to infer from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Sbarvo 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?
No annotations are provided, so the description must carry the full burden. It discloses case-insensitivity and a 50-row cap, but does not state whether the operation is read-only, whether any permissions are required, or what happens on no matches. For a search tool, these are notable gaps that could affect agent expectations.
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 with no filler. It front-loads the core behavior (rows whose cells contain the query) and appends key constraints (case-insensitive, up to 50). Every word adds value.
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?
Since there is no output schema, the description should clarify the return format. It says 'Rows' but not whether it returns an array of row objects or their structure. It also does not address pagination beyond the limit. For a simple search tool, this may be adequate, but the lack of return-type details leaves some ambiguity.
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 describes 'query' as 'text to look for in any cell', and the description adds that the search is case-insensitive and that up to 50 rows are returned, which clarifies the limit parameter. However, it does not specify the default limit or behavior when no matches are found. With 50% schema coverage, the description partially compensates but leaves 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 action (search) and resource (Sbarvo dataset rows), with precise behavior: cells containing the query, case-insensitive, up to 50. This clearly differentiates it from siblings like dataset_row (likely index-based) and dataset_stats (statistical), so an agent can confidently select it.
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 clear context that this is for finding rows by cell content, which implies its use case. However, it does not explicitly say when not to use it or name alternatives, but the purpose is unambiguous enough that an agent can infer when it applies.
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 Sbarvo 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 behavioral burden. It discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which is useful. However, it doesn't clarify behavior for missing values, empty columns, or non-existent columns.
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 efficiently lists the statistics and the data-cleaning behavior. It's concise and front-loaded with the key result types.
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 one parameter and no output schema, the description lists the computed statistics but doesn't specify the output structure (e.g., object keys) or error conditions. It covers the core behavior but leaves some context 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?
Schema coverage is 0%, and the description only indirectly references the column parameter as 'a numeric column'. It doesn't explicitly state that the 'column' parameter is the name of the column, nor does it specify valid values or error behavior.
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 computes: count, min, max, mean, median, and sum for a numeric column of the Sbarvo dataset. It distinguishes itself from siblings like dataset_columns and dataset_search by focusing on statistics, though it doesn't 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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention when to choose dataset_stats over dataset_search or dataset_top, leaving the agent to infer the use case from context.
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 Sbarvo 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?
There are no annotations, so the description carries the full behavioral burden. It discloses only that ranking is by a numeric column and can be highest or lowest; it does not state default ordering, the meaning of limit, behavior on non-numeric columns or ties, or the shape of the returned rows.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It is terse without being rambling, though it could have included a parenthetical on the default ordering without losing 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?
Given the absence of annotations and an output schema, the description is too thin. An agent still cannot predict the return format, default limit behavior, or error handling, and the lack of sibling differentiation means it may invoke dataset_stats instead for similar questions.
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 for undocumented parameters. It adds meaning for 'column' (must be numeric) and partially for 'ascending' (highest vs lowest), but it says nothing about 'limit', leaving a key parameter semantically empty.
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 and description clearly state a ranking operation: get the highest or lowest rows of the dataset by a numeric column. The gloss 'which is the most/least X' makes the purpose concrete and distinguishes it from column-listing, stats, or search siblings, though it does not explicitly call out the difference.
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 natural use case ('which is the most/least X') but gives no explicit when-to-use or when-not-to-use guidance. It doesn't mention alternatives like dataset_stats or dataset_search, so the agent must infer the appropriate context from the tool name and siblings.
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.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but dataset_row and dataset_compare can overlap when a single value is used, since both retrieve rows by column equality. dataset_search is distinct because it searches across all cells rather than a specific column.
All tools share the consistent dataset_ prefix, but the second part mixes nouns (columns, provenance, row, stats) and verbs (compare, search, top). The pattern is still predictable and readable, so it is only a minor deviation.
Seven tools is well-scoped for querying a single dataset: schema, provenance, exact lookup, substring search, ordered comparison, statistics, and ranking each earn their place. No tool feels redundant or excessive.
The tool surface fully covers read-only exploration of the Sbarvo dataset: learning the schema, checking provenance, retrieving rows by exact match or substring, comparing values, computing statistics, and finding top/bottom rows. No obvious missing operation for the stated domain.