Sopvo: 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 Sopvo 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?
There are no annotations to indicate side effects or safety, and the description does not explicitly state that the operation is read-only. However, the language 'learn the schema' strongly implies a non-mutating metadata retrieval, so the behavior is mostly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, informative, and front-loaded with the core output. Every clause contributes meaningful detail, and there is no redundant or filler language.
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 and no parameters, the description adequately covers the return contents: columns, numeric flags, row count, and provenance banner. The only minor gap is the unexplained 'Sopvo' name, but it does not hinder understanding.
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 are no parameter semantics to document. The description fully explains what the tool returns without needing parameter details.
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 lists columns, identifies numeric columns, provides the row count, and shows the provenance banner. The title reinforces the schema-learning purpose, and 'Call this first to learn the schema' unambiguously distinguishes 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 explicitly instructs to call this tool first to learn the schema, giving clear usage timing relative to other dataset operations. It does not explicitly name sibling alternatives, but the instruction is sufficient for a zero-parameter metadata tool.
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 Sopvo 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?
The description discloses one behavioral trait: rows are returned in the order of the given values. However, there are no annotations (e.g., readOnly, destructive) and no mention of edge cases such as no matches, missing column, or case sensitivity. The description carries the burden but provides only partial transparency.
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, concise and without fluff. It front-loads the core action ('rows of the Sopvo dataset') and then specifies the matching logic and ordering. While the phrasing 'The rows...' is a bit noun-heavy, it is still 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?
The description explains the core functionality and ordering but omits details that would be helpful given there is no output schema. It does not specify the exact format of the returned rows, whether all columns are included, or how errors (e.g., invalid column, no matches) are handled. For a tool this simple, the description is 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?
The description clarifies the meaning of both parameters: 'column' is the column to match against, and 'values' are the allowed values (with an OR semantics, as indicated by 'any of the given values'). It also explains that the output ordering follows the order of 'values'. This goes beyond the schema's basic type and length constraints.
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 that the tool returns rows from the Sopvo dataset where a specified column matches any of the given values, and preserves the order of those values. It also hints at the intended use case ('X vs Y' questions). However, it does not explicitly start with a verb like 'retrieve' or 'get', making it slightly less direct.
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 a vague usage hint ('for X vs Y questions') but does not explicitly differentiate this tool from siblings like dataset_search or dataset_row. It does not state when to prefer this tool over others, nor does it describe any alternatives or exclusion criteria.
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 Sopvo 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 full burden. It clearly states the tool returns provenance information (source, date, licence, citation) with no side effects implied. The zero-parameter schema is consistent with a simple metadata retrieval.
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 well-structured, using a clear list of the data elements (source, date, licence, citation) and a directive to read it for attribution. Every sentence 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?
Given the tool's simple purpose and no parameters or output schema, the description covers the essential context: what information is provided and why an agent would use it. It could mention that the output is informational and read-only, but that is implicit in the zero-parameter design.
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 no parameters, and the schema reflects that. The description does not need to explain parameters since there are none, and the baseline of 4 is appropriate 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's purpose: to provide data provenance including source, computation date, licence, and citation. It distinguishes itself from sibling tools that handle columns, comparison, rows, search, statistics, and top values.
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 says to read this 'to attribute a figure correctly', which gives clear guidance on when to use it. It does not explicitly mention when not to use it, but the sibling tools are distinct enough that no exclusion is needed.
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 Sopvo 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 behavioral burden. It discloses exact equality and case-insensitivity, which are meaningful behavioral traits. However, it does not state whether multiple rows can be returned, what happens on no match, or what the response shape is.
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 filler or repetition. Every word contributes to the tool's meaning.
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?
This is a simple two-parameter read-style lookup, and the description explains the core behavior adequately. However, with no output schema and no annotation context, it would benefit from stating whether results are a single row or a list, and behavior when no row matches.
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 is the only source of parameter meaning. The phrase 'where a column equals a value exactly' maps column and value to the equality condition, and case-insensitivity adds value. Still, it provides no format details, examples, or clarification about valid column names.
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 dataset rows where a column equals a value exactly, and adds the case-insensitive qualifier. The title also frames it as an exact-key lookup. It does not explicitly name sibling tools to differentiate them, but the exact-match phrasing distinguishes it from broader 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?
No guidance is given about when to use this tool versus alternatives such as dataset_search or dataset_top. The exact-match phrasing implies a use case, but there is no explicit when-to-use or when-not-to-use direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Sopvo 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 important traits: substring containment, case-insensitivity, and a 50-row maximum. However, it does not mention default limit behavior, result ordering, or what happens when no rows match, which would be useful in the absence of annotations.
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, dense sentence conveys the resource, matching rule, case sensitivity, and row cap with no filler or redundancy. It front-loads the core behavior and earns every word.
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 search tool, the description covers the essential semantics needed to call it correctly. It is slightly incomplete in not specifying the default row limit or result ordering, but the return value is clearly described as rows and the input schema covers the two parameters.
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 value by clarifying case-insensitive matching and the 50-row cap, but the schema already documents the query as text to look for in any cell. The optional limit parameter still lacks a description in the schema, and the description does not explain its default value or effect 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 resource (Sopvo dataset) and a precise matching behavior: rows whose cells contain the query, case-insensitively, up to 50. This clearly differentiates dataset_search from siblings like dataset_columns, dataset_stats, and dataset_top, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool—when you need row-level results matching text in any cell—but it provides no explicit guidance on when to prefer alternatives or exclude other tools. There is no mention of cases where dataset_row, dataset_stats, or dataset_top would be more appropriate.
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 Sopvo 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 burden of behavioral transparency. It usefully discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, but it does not mention behavior for empty columns, missing columns, or error cases.
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 clear sentence with no redundant information or filler. It efficiently conveys the tool's purpose and key handling behavior.
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 adequately lists the returned statistics and mentions non-numeric row handling. It could be more complete by describing the exact output structure, but it is sufficient for a simple stats 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 input schema only states that 'column' is a required string. The description adds that the column must be numeric, which is helpful, but it does not clarify allowed column names, whether the column must exist, or other format expectations.
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 that the tool computes summary statistics for a numeric column in the Sopvo dataset, listing the exact statistics (count, min, max, mean, median, sum). This distinguishes it from sibling tools like dataset_top 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 description explains what the tool does but provides no guidance on when to choose it over sibling tools, nor does it mention alternatives or the conditions under which it should be preferred.
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 Sopvo 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 behavioral disclosure burden. It does state that the operation is numeric and can return either highest or lowest rows, which is useful. But it does not mention limit behavior, default ordering, tie handling, 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?
The description is a single, compact sentence with the core ranking behavior front-loaded. There is no filler or redundant restatement of the title.
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 this is adequate but not complete: it conveys the operation and the numeric-column requirement, but since there is no output schema and no annotations, the description does not explain return values, default ordering, or limit semantics.
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 needs to compensate. It clarifies that `column` must be numeric and links `ascending` to highest/lowest ordering, adding value beyond the schema. However, the `limit` parameter is not explained at all in the description.
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 that the tool returns the highest or lowest rows of the Sopvo dataset ranked by a numeric column, matching the title 'Rank rows by a numeric column'. It is specific about the resource and operation, though it does not explicitly distinguish itself 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?
The phrase 'which is the most/least X' implies the intended use case for top-N or bottom-N ranking questions. However, the description gives no explicit guidance about when to prefer this tool over siblings such as dataset_stats or dataset_search, and it does not mention any exclusions.
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.
Each tool targets a distinct operation on the Sopvo dataset: schema (columns), metadata (provenance), exact row retrieval (row), substring search (search), statistics (stats), ranking (top), and value comparison (compare). No two tools overlap in purpose, making selection unambiguous.
All tools follow a consistent 'dataset_<operation>' pattern with lowercase snake_case, such as dataset_columns, dataset_search, and dataset_stats. The naming is uniform and predictable, aiding agent selection.
With 7 tools, the server is well-scoped for exploring a single dataset. Each tool covers a necessary aspect—schema, provenance, data access, search, stats, and top/bottom queries—without bloat or missing essentials.
The tool surface comprehensively covers the domain of dataset exploration: schema discovery, metadata, exact and fuzzy retrieval, comparison, statistical summaries, and extreme-value queries. No obvious gaps exist for a read-only dataset server.