Topcoatly: 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 Topcoatly 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 behavioral disclosure burden. It clearly states the tool is read-only in effect by describing an informational schema-returning operation and identifies the exact data it exposes. It does not discuss limitations or side effects, but none are expected for a metadata 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?
Two concise sentences, front-loaded with the key return data and immediately followed by the recommended call order. Every clause adds information; there is no redundancy or padding.
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 fully equips an agent to understand what will be returned and why it should be called first. No critical context is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is an empty object covering 100% of the parameters. The description adds relevant context about the dataset and return contents, though there are no parameter semantics to clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies exactly what the tool returns: columns, numeric flags, row count, and the provenance banner of the Topcoatly dataset. It also positions the tool as the schema-learning entry point, distinguishing it from siblings like dataset_row and 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?
It explicitly instructs 'Call this first to learn the schema', giving a clear usage context. It does not enumerate alternatives or when not to use it, but for a zero-parameter schema introspection tool this guidance 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 Topcoatly 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 two behavioral traits: results are ordered according to the provided values, and the match is on 'any of' the values (an OR condition). However, it does not state whether the operation is read-only (no annotations are provided, so the description carries the full burden), nor does it mention response format, pagination, or error behavior. For a simple query tool this is acceptable but not thorough.
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 with no filler. It front-loads the primary purpose and includes the key qualifiers (column, values, order) without redundancy. Every part contributes to understanding the tool's function.
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 tool with no output schema and no annotations, the description covers the basic functionality but omits important contextual details: there is no mention of the dataset being 'Topcoatly' being the only dataset, no guidance on how this differs from dataset_row or dataset_search, and no statement about the read-only nature of the operation. An agent could call it correctly, but might misapply it without clearer sibling differentiation.
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 semantic relationship between the two parameters: 'column' is the field to filter on and 'values' are the values to match. This adds meaning beyond the raw schema, which has no descriptions and 0% coverage. However, it does not elaborate on constraints (e.g., that values are strings, that the column must be an existing column) or provide examples, leaving some ambiguity 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 rows are returned (rows of the Topcoatly dataset filtered by column values) and that they are presented in the given order. It also frames the tool as serving 'X vs Y' comparison questions, which distinguishes it from generic search. However, it lacks an explicit action verb like 'returns' or 'retrieves', and does not directly contrast with any sibling tool by name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for X vs Y questions' provides contextual guidance on when to use the tool, but there is no explicit statement of when not to use it or which sibling tool to prefer (e.g., dataset_search for broader filtering). The guidance is implied rather than prescriptive, leaving the agent to infer the intended use case.
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 Topcoatly 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 provided, the description carries the full burden and pays it: it discloses what the tool returns (source, date computed, licence, citation). The date-computed detail is useful behavioral context. The tool is inherently a non-destructive metadata lookup, so no side-effect warnings are needed.
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, front-loaded with content, then purpose. Every word earns its place; the title and description work together without 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 zero-parameter, no-output-schema metadata lookup, the description tells the agent what it will receive and why to call it. It could note read-only security posture explicitly, but that is strongly implied by the name and description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4, and the description compensates by describing output content rather than inputs. Nothing about parameter semantics is missing.
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 resource (the Topcoatly dataset) and the content returned: source, computation date, licence and citation. The title reinforces this. It doesn't brand itself against a sibling explicitly, but provenance is a distinct operation among the listed 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?
'Read this to attribute a figure correctly' gives an explicit when-to-use context for citation/attribution tasks. It doesn't name exclusions or alternatives, but the sibling set (columns, row, search, stats, top, compare) makes the boundary obvious.
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 Topcoatly 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 burden. It discloses exact matching and case-insensitivity, but omits whether it returns one or many rows, ordering, error behavior for no matches, or whether it is read-only. Some behavioral context is provided, but significant gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence, front-loaded with the core purpose and key qualifier (case-insensitive). No wasted words, perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple but the description lacks details on the return format (no output schema), whether multiple rows are returned, no-match behavior, and any prerequisite like knowing column names (which dataset_columns provides). An agent would need to infer too much 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 description maps the parameters implicitly: 'column' is a field name and 'value' is the exact value to match, with case-insensitivity noted. However, with 0% schema coverage, it does not explain the full meaning, such as whether column must be an existing column or how to format value. It adds basic semantics but not enough to fully compensate.
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 retrieves rows from a dataset where a column exactly equals a value (case-insensitive). This distinguishes it from fuzzy search or top rows, though it doesn't explicitly name alternatives. The verb is implied but the purpose is evident.
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 versus siblings like dataset_search or dataset_top. The description does not mention conditions for choosing it over alternatives, leaving the agent to infer from the 'exact' and 'case-insensitive' qualifiers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Topcoatly 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 provided, the description carries the full burden. It discloses key behaviors: case-insensitive matching, cell containment, and a maximum of 50 rows. However, it does not mention potential side effects (none expected), error conditions, or the return format. This is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core purpose and constraints. There is no wasted text; every part is informative.
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 with two parameters and no output schema, the description covers the essential behavior: what it searches, how it matches, and the limit. It lacks explicit mention of return structure or empty-result behavior, but these are not critical for a basic search. It is sufficiently complete for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (query has a description, limit does not). The description adds value by clarifying that the query is matched case-insensitively and that the result is limited to 50 rows, which helps interpret the 'limit' parameter beyond its min/max. However, it does not elaborate on the limit parameter's name or behavior in detail.
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 Topcoatly dataset where cells contain the query. It specifies the verb (search/return), resource (dataset), and conditions (case-insensitive, limit). It is distinguishable from siblings like dataset_stats or dataset_columns, 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 usage for finding rows matching a text query, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. The context is clear from the name and description, but no direct guidance is given.
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 Topcoatly 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 behavior. It explicitly discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which adds useful context 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, packed sentence with no filler. The statistics list is front-loaded and every phrase 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?
For a one-parameter tool with no output schema, the description covers the computed statistics and data-handling nuances. It stops short of stating the exact return shape, but the listed statistics make the return type reasonably inferable.
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 has zero description coverage for the 'column' parameter, so the description must compensate. It does so partially by indicating the column must be numeric, but it does not elaborate on naming, format, or behavior for invalid columns.
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 verb (count, min, max, mean, median, sum) and a clear resource (numeric column of the Topcoatly dataset). It is immediately distinguishable from siblings like dataset_row 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 implies use when summary statistics for a numeric column are needed, and it clarifies the dataset context. However, it does not explicitly mention when to use this tool over alternatives or any exclusions.
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 Topcoatly 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?
The description discloses the core ranking behavior and the ascending/descending intent through 'highest (or lowest)' and 'most/least X'. However, with no annotations, it does not mention return shape, default limit behavior, tie handling, or whether the operation is read-only.
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 compact and front-loaded, with the key operation stated immediately. The quoted phrase adds a little color but no real bloat; using a clearer verb like 'Returns' would improve it slightly.
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 mention of result shape, defaults, or how this tool relates to its siblings, the description leaves an agent to infer important invocation details. It is minimally adequate only for very simple calls.
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 the undocumented column and limit parameters. It adds the 'numeric' qualifier for column and the high/low semantics, but it does not clarify limit defaults or the relationship between ascending and ordering beyond what the schema already states.
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 text clearly conveys a specific operation: selecting the highest or lowest rows of a dataset by a numeric column. It aligns with the title and the intended 'most/least X' behavior, though it does not explicitly differentiate itself 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?
There is no guidance about when to use dataset_top versus dataset_row, dataset_search, or dataset_stats, and no exclusions or alternative recommendations are given. The only implied context is that the user wants top/bottom rows by a numeric value.
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 query pattern—schema, provenance, exact match, contains search, ordered value comparison, aggregation, and top/bottom ranking. The only mild ambiguity is between dataset_row and dataset_search, but their exact-match versus contains-match descriptions make the boundary clear.
All tools share the consistent dataset_ prefix followed by a clear operation name, and all use lowercase snake_case. The pattern makes the purpose of each tool predictable at a glance.
Seven tools is an appropriate scope for a read-only dataset querying server. Each tool addresses a distinct question type without unnecessary sprawl or redundancy.
The toolkit covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, descriptive statistics, and top/bottom ranking—the core workflows for answering dataset questions. It lacks general range filtering or grouped aggregation, but these are minor gaps for the stated purpose.