Amortlane: 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 Amortlane dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavioral traits. It does disclose what information is returned (columns, numeric indicators, row count, provenance banner), which implies a read-only schema inspection. However, it never explicitly states that the tool has no side effects or what happens if the dataset is unavailable, leaving some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The output contents are front-loaded in the first sentence, and the crucial usage instruction ('Call this first') is in the second. 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 simple zero-parameter schema-introspection tool, the description is complete: it names the dataset, lists the returned information categories, and gives clear first-step guidance. With no output schema, it covers the essential return values well enough for an agent 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, so there is no parameter semantics to document. The baseline for zero-parameter tools is 4, and the description appropriately focuses on what the call returns rather than inventing 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 identifies the resource ('the Amortlane dataset') and the specific outputs: columns, which are numeric, row count, and provenance banner. It lacks an explicit verb like 'retrieves' but is unambiguous and not tautological. It does not directly contrast with sibling tools, though the 'learn the schema' framing sets it apart.
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 instruction 'Call this first to learn the schema' gives explicit when-to-use guidance and establishes this as the entry point before other dataset tools. It does not name alternatives or state when not to use it, but for a zero-parameter introspective tool this is clear and useful context.
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 Amortlane 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?
No annotations are provided, so the description carries the full behavioral burden. It does disclose two important behaviors: matching any of the provided values (OR semantics) and returning rows in the order of the given values. Yet it does not mention what happens when no rows match, whether matching is case-sensitive, or how the rows are presented in the comparison view.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. The core filtering and ordering behavior is front-loaded, and the 'X vs Y' usage cue is appended economically. Every word contributes to understanding the tool.
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 two-parameter read tool, the description covers the core selection logic and is fairly complete. However, without an output schema it does not describe the return structure or layout, and it omits edge cases such as zero matches or duplicate values. Given no annotations, a bit more context would make the definition fully self-sufficient.
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%, but the description compensates by explaining the roles of both parameters: 'column' is the field to filter on and 'given values' are the values to match, with order preservation. This adds meaningful semantics beyond the bare schema types, though it does not elaborate on constraints like the minimum of two values.
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 explains what the tool does: it returns rows from the Amortlane dataset filtered by a specified column matching any of the given values, preserving the given order. The 'X vs Y' phrasing gives a distinctive use case, but there is no explicit verb like 'retrieve' or 'compare', and it does not name sibling tools, so it falls just short of a 5.
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 clear usage context: it is intended for 'X vs Y' questions, which signals when an agent should choose this tool over alternatives like dataset_search or dataset_row. However, it does not explicitly state when not to use it or how it differs from specific siblings, leaving some inference to the agent.
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 Amortlane 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 of disclosure. It names the exact contents returned (source, date computed, licence, citation) and frames the operation as a read, which implies no side effects. It does not elaborate on output format, but that is a minor omission for a simple provenance lookup.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The content summary is front-loaded and the intended use case is stated immediately, making it easy for an agent 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?
For a zero-parameter provenance metadata tool with no output schema, the description sufficiently covers what it returns and why an agent would call it. The title reinforces the purpose. Nothing critical is missing 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?
There are no parameters and the schema is empty, so parameter documentation is not needed. The description instead explains what information the tool returns, which is the relevant semantic content for this 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 identifies the tool as the provenance/citation source for the Amortlane dataset, listing what it returns: source, computation date, licence, and citation. This distinguishes it from sibling tools like dataset_columns, dataset_stats, or dataset_search, which operate on data content rather than metadata.
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 states when to use it: 'Read this to attribute a figure correctly.' It gives a clear use case, though it does not explicitly mention alternatives or when not to use this tool; however, the sibling tools are sufficiently distinct.
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 Amortlane 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 provided, the description carries the full burden of behavioral disclosure. It does disclose the key behavioral trait, case-insensitive exact matching, which is valuable for a lookup tool. However, it does not state edge-case behavior such as what happens when no rows match, whether multiple rows are returned, or whether column names are validated; no output schema exists to fill these gaps.
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 dense sentence with no filler words. Every element — dataset scope, match condition, and case-insensitivity — earns its place and is front-loaded.
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 two-parameter read tool, the description is mostly adequate, but with no output schema it must explain return behavior and does not. It does not mention whether a single row or multiple rows are returned, what happens on no match, or how column names are specified. The simplicity of the tool keeps this from being a lower score.
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 for parameter meaning, and it partially does: 'where a column equals a value exactly' explains that `column` names a dataset field and `value` is the comparison target. The parameter names are already self-descriptive, but the description offers no examples, format requirements, or clarification that `column` must be an existing column name.
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 verb+resource+condition: it returns rows of the Amortlane dataset where a column equals a value exactly. The title reinforces the purpose as an exact-key row lookup. It does not explicitly name a sibling, but the 'exact' vs. 'search' contrast with dataset_search is implicit, so it falls just short of full differentiation.
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 no explicit guidance on when to use this tool versus dataset_search or other siblings. The word 'exact' hints at the intended use case, but there is no when-to-use/when-not-to-use statement or exclusion of alternatives. An agent must infer selection criteria from the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Amortlane 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 behavioral disclosure burden. It usefully states that matching is case-insensitive and that results are limited to 50 rows, but it does not mention default limit behavior, result ordering, empty-result behavior, or the response structure for a tool with no output 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, information-dense sentence with no filler. It front-loads the resource and search behavior, and every element contributes to understanding how the tool behaves.
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 is adequate for a simple search tool, but given the absence of annotations and output schema, it leaves gaps: no default limit, no ordering guarantee, no empty-result behavior, and no routing guidance among sibling dataset tools. It is not incomplete enough to be unusable, but it is not fully self-sufficient.
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%, so the description is expected to compensate. It does add meaning beyond the schema: 'up to 50' clarifies the limit parameter's role, and 'case-insensitive' adds a search behavior not present in the schema. However, it does not state what happens when limit is 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?
The description clearly identifies the operation as searching rows of the Amortlane dataset for cells containing the query, with a concrete cap of 50 results. It does not explicitly name sibling tools or contrast with them, but the behavior is specific and distinct from the listed dataset_* 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 is given about when to choose dataset_search over alternatives like dataset_row, dataset_top, or dataset_columns. The intended use can be inferred, but the description offers no explicit context, exclusions, or comparisons.
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 Amortlane 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 behavioral disclosure burden. It discloses meaningful behaviors beyond the schema: grouping commas and currency symbols are handled, non-numeric rows are excluded, and excluded rows are counted. This gives the agent realistic expectations about data cleaning.
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 conveys the operation, the target resource, the computed metrics, and the edge-case handling with no filler. 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?
For a one-parameter tool with no output schema, the description is sufficiently complete: it names the expected inputs, the statistics returned, and the preprocessing behavior. Minor omissions like the exact output shape or behavior when the column does not exist are not critical for a 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?
Schema coverage is 0% and the parameter is simply named 'column' with minLength 1. The description compensates by specifying that the column must be numeric, and that formatting and non-numeric values are handled in a particular way. It does not list valid column names, but that is reasonable for a single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific operation — computing summary statistics on a numeric column — and enumerates the exact outputs (count, min, max, mean, median, sum). This clearly distinguishes it from sibling tools like dataset_search, dataset_row, and dataset_compare.
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 summary statistics of a numeric column are needed, and it clarifies that only numeric columns are supported. However, it does not explicitly state when-not-to-use or mention alternatives such as dataset_columns for listing valid columns or dataset_top for sample rows.
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 Amortlane dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose the core behavior: returning rows ordered by highest/lowest values of a numeric column, with no hint of side effects. However, it omits default limit behavior, tie-breaking, and how non-numeric or null values are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One compact sentence with no filler, and the paraphrase 'which is the most/least X' makes the purpose immediately relatable. The core idea is front-loaded and every element 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 no annotations and no output schema, the description is adequate but minimal. It doesn't specify expected row count, default ordering, or return shape, so an agent would still need to infer important invocation details.
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 clarifies that `column` must be numeric and implicitly references `ascending` via 'highest (or lowest)', but it never explains `limit` or a default row count, leaving a key parameter semantically under-specified.
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 verb and resource ('highest (or lowest) rows of the Amortlane dataset by a numeric column'), which clearly identifies a ranking operation. It doesn't explicitly contrast with sibling tools like dataset_row or dataset_stats, so it stops short of full differentiation.
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 gloss 'which is the most/least X' implies the tool is for ordering/top-N questions, but there is no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives such as dataset_compare or dataset_stats, leaving the routing decision 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.
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.
dataset_row, dataset_compare, and dataset_search all retrieve rows and could be confused at first, but their descriptions clearly separate exact equality, multi-value ordered comparison, and substring search. The other tools are distinct in purpose.
All tools share a dataset_ prefix in snake_case, which aids recognition, but the suffix mixes nouns like columns, row, stats, and provenance with verbs like compare and search. There is no consistent verb_noun pattern across the set.
Seven tools is well-scoped for a single-dataset query server. Each tool addresses a distinct class of question, from schema and provenance to exact lookup, search, comparison, stats, and ranking.
The toolset covers the full read-only query lifecycle for the Amortlane dataset: schema discovery, provenance, exact and fuzzy retrieval, multi-value comparisons, numeric aggregation, and top/bottom ranking. No critical operation appears missing for typical analytical workflows.