ReceivableLedger: 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 ReceivableLedger 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 transparency burden. It discloses that this is a learning/read-style operation and enumerates what the agent will receive. It stops short of stating side-effect-free or auth requirements, but for a zero-argument metadata tool the behavior is reasonably 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 one dense sentence plus a direct instruction. It front-loads the key content and every phrase contributes meaning, with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no parameters and no output schema, so the description must stand alone. It lists the main return elements and the intended first-step role, which is enough for an agent to call it correctly. A named sibling or return-format note could push it to 5.
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, so the schema already fully covers this dimension. The baseline for 0 parameters is 4, and the description adds nothing contradictory or misleading.
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's outputs — columns, numeric flags, row count, and provenance banner — for the ReceivableLedger dataset, and frames it as the schema-learning entry point. It is specific, but it does not explicitly name a sibling it is not, so it earns a 4 rather than 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?
"Call this first to learn the schema" gives direct guidance on when to use the tool. It does not mention alternatives or explicit when-not-to-use conditions, but the sequencing instruction is a clear usage signal.
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 ReceivableLedger dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It discloses that results are ordered according to the given values and that matching is 'any of the given values', which is useful. However, it does not describe the output shape, whether full rows are returned, duplicate handling, or any read-only guarantees.
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 packs in the target dataset, the filtering behavior, ordering behavior, and intended use case. There is no filler or redundancy; 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?
The description is adequate for a relatively simple two-parameter tool, and it explains what rows are selected and in what order. However, there is no output schema and no explicit description of the returned structure or the 'side by side' presentation promised by the title, leaving some ambiguity about the tool's exact output behavior.
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 explain the parameters. It does this by describing the role of 'column' and 'values': rows whose column matches any of the given values, in the given order. This adds meaningful behavioral meaning beyond the raw JSON schema, though it leaves some validation details to the schema.
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 identifies a specific retrieval operation: returning rows from the ReceivableLedger dataset filtered by column values, in the given order. It also conveys the comparative intent ('X vs Y' questions), which distinguishes it from generic search or row tools, though it does not explicitly name a sibling.
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' gives clear context for when to use this tool: comparing rows across specific column values. It does not explicitly state when not to use it or mention alternative sibling tools, but the intended use case is reasonably clear.
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 ReceivableLedger 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 burden of behavioral disclosure. It clearly states what information the tool provides (source, computed date, licence, citation) and implies a read-only, informational operation. No hidden side effects or permissions are relevant given the zero-parameter scope.
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 concise sentences with no filler. The first sentence front-loads the key content, and the second sentence supplies the practical usage context.
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 informational tool, the description is complete: it names the resource, the fields returned, and the scenario in which to use it. No output schema is needed for this level of complexity.
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 already documents this fully. Per the baseline for zero-parameter tools, the description does not need to add parameter-specific semantics.
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 names the specific resource (ReceivableLedger dataset) and the exact metadata fields (source, computation date, licence, citation). This distinguishes it from the sibling dataset tools, which operate on columns, rows, search, stats, and comparisons.
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 the tool: 'Read this to attribute a figure correctly.' It provides clear context for usage, though it does not mention explicit exclusions or alternatives.
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 ReceivableLedger 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 behavioral disclosure. It adds useful detail: matching is exact and case-insensitive, and the operation targets the ReceivableLedger dataset. However, it says nothing about whether multiple rows can be returned, the output shape, ordering, pagination, or error behavior, leaving notable 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 clear sentence with no filler, and the title is equally concise. The most important information—dataset, match type, and case sensitivity—is front-loaded. Nothing in the text is wasted.
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 exact-key lookup, the description is largely complete: it specifies the dataset, the matching rule, and what is returned (rows where the column equals the value). There is no output schema, so the description's statement about the returned rows suffices. Minor omissions like multiple-match behavior and return structure are acceptable at this complexity level.
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 clarify the parameters. It maps 'column' to the column to match and 'value' to the exact value to look up, and adds the case-insensitive behavior. It does not provide examples, valid column names, or a note that values are strings, so the compensation is partial.
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 supplies the verb 'look up' and the resource 'a row', while the description states it returns rows of the ReceivableLedger dataset filtered by an exact column/value match. The word 'exactly' contrasts with sibling tools like dataset_search, though it never names the alternative. Clear, but phrased as a noun phrase rather than an explicit action statement.
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 guidance on when to prefer this tool over alternatives such as dataset_search or dataset_compare. It implies exact-match lookup through 'exactly (case-insensitive)' but does not state when not to use it or point to a sibling for fuzzy/partial matching. An agent is left to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the ReceivableLedger 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 carries the full burden. It discloses case-insensitive matching and the 50-result cap, but it does not state whether the operation is read-only, what the response shape is, or what happens with zero matches.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, information-dense sentence with the core action front-loaded and modifiers appended. Every word earns its place; there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with no annotations and no output schema, the description covers the search mechanism, scope, and limit. It does not spell out the return format, but 'Rows...' implies row-level results, which is likely enough for a read-only dataset query.
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 ('text to look for in any cell') but not limit. The description adds meaning by specifying case-insensitive behavior for query and 'up to 50' for the limit cap, compensating for the 50% schema coverage.
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 ('Rows of the ReceivableLedger dataset whose cells contain...'), clearly distinguishing it from siblings like dataset_stats or dataset_columns. The only redundancy is the generic title, but the description itself is fully disambiguating.
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 text-search use case ('cells contain the query') but does not explicitly say when to prefer it over dataset_row or dataset_top. No alternatives or exclusion conditions are named, so the agent must infer the choice from context.
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 ReceivableLedger 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 burden. It usefully discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not discuss error or edge-case behavior, but adds meaningful behavioral context beyond the obvious statistics computation.
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 entire description is one dense, front-loaded sentence with no redundant phrasing. Every clause contributes either to what is computed, which dataset is targeted, or how the data is preprocessed.
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 single-parameter aggregation tool, this is largely complete: it names the dataset, the input column type, the returned statistics, and key data-handling behaviors. It lacks explicit output formatting or error behavior, but those are not critical 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 schema only defines 'column' as a non-empty string, so the description must compensate. It clarifies that the column must be a numeric column of the ReceivableLedger dataset and implies how currency/grouping formatting will be interpreted, which adds real semantic value.
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 precise operation — computing six named summary statistics on a numeric column of a specific dataset. This clearly distinguishes it from sibling tools like dataset_search, dataset_top, or dataset_row.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: call this when you need summary statistics of a numeric column in ReceivableLedger. However, it does not explicitly contrast with sibling tools or state when not to use it, so routing relies on inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The highest (or lowest) rows of the ReceivableLedger 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 of behavior disclosure. It states the sorting direction (highest or lowest) but fails to mention the default limit, tie-handling, the output row shape, or whether this is a read-only 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?
The description is a single, front-loaded sentence with no filler. It is concise, though its brevity leaves out important behavioral details that would be expected for a tool with no annotations and no output schema.
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 lack of annotations and output schema, the description should clarify defaults (e.g., default limit) and result format. It omits what the returned rows look like and does not explain the limit parameter's behavior, leaving an agent without enough information to guarantee a correct call.
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% (only 'ascending' is documented). The description adds some meaning to 'column' by specifying it must be numeric, but it does not explain the 'limit' parameter or its default behavior, so it does not compensate for the low schema coverage.
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 the task: 'Rank rows by a numeric column' and return 'the highest (or lowest) rows' of the ReceivableLedger dataset. The phrase 'which is the most/least X' makes the use case concrete and distinguishes it from siblings such as dataset_row (single row) and dataset_stats (aggregate statistics).
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 usage scenario ('which is the most/least X') but does not explicitly state when to use this tool over siblings or what conditions would make an alternative preferable. There are no exclusions or alternative tool mentions.
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!
OrderPadLedger: the site's own MCP server — dataset; every answer cites the site.
BurdenRateLedger: the site's own MCP server — dataset; every answer cites the site.
ProbeLedger: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes: schema, provenance, search, stats, and top are easy to tell apart. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by matching a column value, with only the number of allowed values clearly differing.
All seven tools share the same dataset_ prefix and consistent snake_case formatting, making the family immediately recognizable. While some suffixes are nouns and some are verbs, the overall convention is uniform and predictable.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct common operation without adding redundant or overwhelming surface area.
The set covers schema discovery, provenance, exact lookup, multi-value comparison, fuzzy search, numeric statistics, and top/bottom ranking. Missing features like distinct-value listing or numeric-range filtering are minor gaps given the apparent Q&A-oriented purpose.