EmployeeDeskHQ: 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 EmployeeDeskHQ dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose what the tool returns, but it does not explicitly state that the tool is read-only, how the provenance banner is represented, or whether there are any caveats about row-count accuracy. The description is adequate for a simple metadata query but leaves some behavioral traits implicit.
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 and every part earns its place: the output components are listed compactly, the dataset is named, and the usage instruction is front-loaded. There is no redundant restatement of the tool name or 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 zero-parameter schema-discovery tool, the description is nearly complete: it names the dataset, enumerates the returned information, and instructs the agent to call it first. The lack of an output schema and an explicit return-format description is a minor gap, but it does not hinder 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 tool has zero parameters, so there is no parameter semantics to explain; the baseline of 4 applies. The description adds useful context by naming the exact dataset, which is the only relevant scoping information, and schema coverage is 100%.
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 (EmployeeDeskHQ dataset) and the exact information returned: columns, numeric flags, row count, and provenance banner. It does not use an explicit action verb like 'returns', but 'Call this first to learn the schema' makes the tool's role as the schema-discovery entry point clear. It differentiates from siblings mainly through this first-step guidance rather than naming alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Call this first to learn the schema' provides explicit timing/context: use this before other dataset tools. It does not name sibling tools or state when not to use it, so some exclusion guidance is missing, but the instruction is specific enough for an agent to sequence correctly.
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 EmployeeDeskHQ dataset whose column is any of the given values, in the order given — for "X vs Y" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| values | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of disclosing behavior. It states that rows are filtered by column value and that output order follows the order of the given values, which is useful. However, it does not disclose the return format, whether all columns are included, or any side-by-side presentation details promised by the title.
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 sentence that front-loads the core behavior and ends with the use-case tagline. No filler or redundant restatement of the schema. It is appropriately sized for the tool's simplicity.
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 tool with two simple parameters and no output schema, the description explains what rows are selected but leaves the return representation unspecified. It does not explicitly state that the output will present rows 'side by side' as the title suggests, nor whether full rows or a subset of columns are returned. The dataset name and filter semantics are present, so it 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 schema provides no parameter descriptions (0% coverage), but the description clearly maps both parameters: 'column' is the field to match and 'values' are the candidate values to compare. It also clarifies the match is 'any of' (OR) and that ordering follows the input list. This compensates for the schema's lack of semantic detail, though it omits constraints like valid column names or case sensitivity.
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 the exact resource (EmployeeDeskHQ dataset) and the precise filter logic: rows whose column matches any of the given values, in the provided order. It is clear this is a retrieval/filter operation, though it lacks an explicit verb and does not contrast with sibling tools. The 'X vs Y' phrase hints at the intended comparison use case.
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 dash-delimited phrase 'for X vs Y questions' explicitly signals when this tool should be used: comparing rows corresponding to two or more specific values. It provides clear context but does not mention alternative tools or exclusions. The context is sufficient for an agent to select it for side-by-side comparisons.
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 EmployeeDeskHQ 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 behavioral disclosure burden. 'Read this' signals a read-only lookup, and the listed fields describe what the agent will receive. It does not discuss auth or rate limits, but for a parameterless metadata endpoint this is a minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The identifying content (source, date, licence, citation) is front-loaded, and the usage instruction follows naturally.
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 parameterless metadata tool, this is complete: it says what the tool returns, why it exists, and when to use it. The lack of an output schema is compensated by the explicit list of fields the agent can expect.
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 has zero properties, so there are no parameters to document; the baseline for 0-parameter tools is 4. The description appropriately focuses on the output content rather than inventing parameter guidance.
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 the exact resource (EmployeeDeskHQ dataset provenance) and the specific content it provides: source, computed date, licence, and citation. It also connects the tool to a clear goal ('attribute a figure correctly'). This distinguishes it cleanly from the sibling dataset tools, which focus on data inspection and manipulation.
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 the intended use case: read this to attribute a figure correctly. It does not enumerate when not to use it or name alternatives, but the parameterless provenance scope and the sibling tool names make the appropriate selection 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 EmployeeDeskHQ 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 exist, so the description carries full behavioral burden. It usefully discloses case-insensitive exact matching, but it does not clarify whether one row or multiple rows are returned, what happens on no match, or what the response looks like. The title says 'a row' while the description says 'rows', creating 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 a single focused sentence that front-loads the core matching behavior without wasted words. It is concise and readable, though it could have included sibling or return-info details without becoming bloated.
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 tool with no annotations, no output schema, and a 0% schema description coverage, the description is too thin. It omits the return shape, multiple-match behavior, error/empty-result handling, and how to discover valid column names, leaving an agent to guess 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?
The input schema has 0% description coverage, but the description explains the relationship between column and value: the column's value must equal the provided value exactly, case-insensitively. This adds meaning beyond the bare parameter names, though it does not enumerate valid columns or value formatting rules.
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 a specific dataset where a column equals a supplied value exactly, with case-insensitive matching. The phrase 'exactly' differentiates it from the likely fuzzy dataset_search sibling, though no sibling is named explicitly.
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?
Usage is implied: use this when an exact, case-insensitive column match is needed. However, the description gives no explicit guidance about when to prefer this over dataset_search or dataset_columns, nor does it mention any alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the EmployeeDeskHQ 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 and does it well: it discloses case-insensitive matching, substring containment in any cell, and a 50-row cap. It does not mention ordering or output format, but for a read-only search tool the key behavioral traits are present.
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 sentence conveys the resource, the matching predicate, case sensitivity, and the row cap. There is no filler or redundant restatement of the tool name.
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 search tool, the core behavior is well covered. However, with no output schema, the description does not clarify whether returned rows contain all columns, the ordering, or the exact effect of the limit parameter, leaving some ambiguity for an agent.
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 already documents query as 'text to look for in any cell', and the description adds the useful case-insensitivity detail. However, the limit parameter is only implicitly referenced via 'up to 50'; the description does not explicitly explain that limit controls the maximum number of returned rows.
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 exactly what the tool returns: rows of the EmployeeDeskHQ dataset containing the query text in any cell, case-insensitively, up to 50 rows. This clearly distinguishes it from sibling tools like dataset_row, dataset_stats, or dataset_top, which serve different lookup or aggregation 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 use case is clear: call this tool when you need rows matching arbitrary text across the dataset. It does not explicitly name alternatives or exclusions, but no sibling tool performs the same cell-substring search, so the context is sufficient.
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 EmployeeDeskHQ 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 and does so well: it reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This gives the agent useful insight into the computation, though it doesn't specify the output structure or error handling.
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 front-loads the computed statistics and packs edge-case behavior into a parenthetical. Every part adds value, and there is no filler or 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 single-parameter statistics tool with no output schema, the description covers the input requirements, the dataset scope, the returned statistics, and key data-cleaning behaviors. The only notable gap is the exact return format, but the listed statistics largely substitute for it.
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% and the only parameter 'column' has no schema description. The description compensates by specifying that the column must be numeric and belong to the EmployeeDeskHQ dataset, which is essential for correct invocation.
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 concrete resource (numeric column of the EmployeeDeskHQ dataset), a specific verb (compute summary statistics), and enumerates the exact outputs (count, min, max, mean, median, sum). It is readily distinguishable from siblings like dataset_search or dataset_top.
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 by the statistics list and the dataset name, but there is no explicit statement about when to choose this tool over sibling tools. Alternatives are not mentioned and no exclusions are given.
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 EmployeeDeskHQ 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 burden, and it does convey the core behavior: rows are ordered by a numeric column and can be returned highest or lowest. It does not disclose tie-handling, null/missing-value behavior, non-numeric column errors, or the default limit, leaving material behavioral 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 focused sentence that front-loads the core ranking behavior and adds a natural-language query example. No words are 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 3-parameter ranking tool, the description plus schema cover the main call shape. However, without annotations or an output schema, an agent is left guessing about returned row shape, limit defaults, and error behavior, so the definition is only minimally 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?
Schema description coverage is only 33%, so the description must compensate for the undocumented 'column' and 'limit' parameters. It adds only that the column must be numeric, and does not explain the semantics or defaults of 'limit' or how 'ascending' maps to the 'highest/lowest' wording beyond what the schema already says.
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 identify a ranking operation on the EmployeeDeskHQ dataset, returning rows by highest or lowest value in a numeric column. It is specific enough to distinguish from dataset_row and dataset_search, though it does not explicitly name any sibling it is not.
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 tool is for top/bottom row queries, giving a clear intended context. However, it does not state when to prefer dataset_top over siblings such as dataset_stats or dataset_search, and offers no 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!
HeadcountDesk: the site's own MCP server — dataset; every answer cites the site.
RoughInDesk: the site's own MCP server — dataset; every answer cites the site.
RunbookDesk: the site's own MCP server — dataset; every answer cites the site.
DailyLogDesk: the site's own MCP server — dataset; every answer cites the site.
Add one secure layer between your agents and this server.
Most tools are clearly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping filtering behavior that could lead an agent to pick the wrong one. Schema, provenance, stats, and top are unambiguous.
All tools share the dataset_ prefix with snake_case, which is easy to follow. However, the suffixes mix nouns (columns, row, stats) and verbs (compare, search), so it is not a fully consistent verb_noun convention.
Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct mode of exploration without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, full-text search, comparison ordering, numeric stats, and top/bottom rows. A minor gap is the lack of a general paginated 'all rows' or arbitrary aggregation tool, but common dataset questions are well supported.