Defectbird: 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 Defectbird 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 must carry the behavioral disclosure burden. It discloses the contents of the response, including columns, numeric flags, row count, and provenance banner, and implies a read-only metadata operation by saying 'call this first'. However, it does not explicitly address side effects, permissions, or possible failure modes.
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 first sentence enumerates the returned information, and the second provides a clear usage directive. 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 zero-parameter, no-output-schema tool, the description is complete: it states what the agent gets, identifies the dataset, and instructs the agent to call it first. There is nothing critical missing for selecting and invoking this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no parameter documentation burden. The description adds value by enumerating what the agent will learn from the call, which is more meaningful than an empty input schema alone.
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 (Defectbird dataset) and the returned information: columns, numeric flags, row count, and provenance banner. It lacks an explicit verb like 'returns', but the phrase 'Call this first to learn the schema' makes the tool's role unambiguous and distinguishes it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to call this tool first to learn the schema, establishing a clear usage context. It does not explicitly describe when not to use it or name alternatives, but for an initial schema-discovery 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 Defectbird 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 carries the full behavioral burden. It clearly discloses the key behavior: rows are matched by 'any of the given values' and returned 'in the order given.' This adds meaningful semantics beyond the raw schema, though it does not mention edge cases such as no matches, duplicate values, or output formatting.
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, well-structured sentence that front-loads the core behavior and ends with a clear usage cue. Every part earns its place, with no redundant or filler wording.
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 tool with no output schema, the description conveys the essential selection and ordering semantics. It is complete enough for an agent to invoke the tool correctly, though it could optionally mention what happens when no rows match or whether the full row is returned.
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: 'column' selects the column to match against, and 'values' are the list of values to match, with the output order tied to the order of the values. This is sufficient semantic clarity for both required parameters, although it omits explicit mention of the schema's min/max array constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states exactly what the tool does: it returns rows from the Defectbird dataset matching any of the provided column values, in the given order. This distinguishes it from siblings like dataset_row (likely single-row lookup) and dataset_search (likely free-text search) by specifying a value-list comparison with order preservation.
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' implies the intended use case, but the description does not explicitly state when to use this tool versus alternatives like dataset_row or dataset_search, nor does it mention exclusions or conditions. Usage guidance is present but only implied, not explicit.
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 Defectbird 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, the description carries the behavioral disclosure burden. It states what information is contained (source, date, licence, citation) and implies a read-only operation via 'Read this', but it does not explicitly state whether any computation or external interaction occurs. For a simple metadata tool this is acceptable but not fully 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 two short sentences with no filler. It front-loads the key content and follows with the actionable purpose. 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 parameterless provenance/metadata tool, the description is complete: it names the dataset, the exact fields provided, and the intended use case. No output schema exists, but the listed contents sufficiently set expectations for the response.
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. The description adds meaningful context about what the tool returns, which is more than the empty input schema provides.
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 subject — provenance of the Defectbird dataset — and lists the specific contents (source, computed date, licence, citation). It is distinguishable from sibling dataset tools like dataset_columns or dataset_stats, though it lacks an explicit verb like 'get' or 'show'.
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 a concrete use case and tells the agent when this tool is relevant. It does not explicitly discuss alternatives, but none of the sibling tools serve a citation purpose, so the 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 Defectbird 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 full burden of behavioral disclosure. It does state the core behavior: matching rows where a column equals a value, with case-insensitivity. However, it leaves ambiguity about whether one row or multiple rows are returned and does not mention any error or edge-case behavior.
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, and the central exact-match constraint is front-loaded. The title also reinforces the purpose 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 low-complexity tool with two string parameters and no output schema, the description is nearly sufficient. The singular title 'a row' versus the plural description 'The rows' creates ambiguity about cardinality, and the description assumes the agent already knows which columns are valid in the Defectbird dataset.
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 by explaining the parameters. It does connect 'column' and 'value' to the matching logic, which adds meaning beyond the bare schema. It does not, however, explain accepted column names, formatting rules, or the exact scope of case-insensitivity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The title and description clearly state that this tool looks up rows by an exact column-value match. The phrase 'where a column equals a value exactly (case-insensitive)' distinguishes it from sibling tools like dataset_search, which implies broader or fuzzy 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 does not explicitly state when to use this tool versus alternatives such as dataset_search, dataset_top, or dataset_columns. The exact-match behavior implies a use case, but there is no direct guidance on exclusions or preferred conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the Defectbird 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 behavioral disclosure burden. It meaningfully discloses case-insensitive matching, substring-like containment semantics, and the 50-row cap. It does not describe output shape or empty-result behavior, but for a read-only search tool this 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 a single concise sentence with no filler. It front-loads the core behavior and adds only relevant constraints, making it easy for an agent to parse quickly.
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 with no output schema, the description covers the essential contract: what is searched, how matching works, and the result cap. It could optionally mention the default limit or explicitly point to sibling tools, but 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?
Schema description coverage is 50%: query is documented but limit is not. The description adds useful context by clarifying that matching is case-insensitive and that results are capped at 50. However, it does not explain the default limit behavior or how limit interacts with the query, so compensation for the schema gap is only 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 description clearly specifies what the tool does: returns rows of the Defectbird dataset whose cells contain the query. It adds explicit matching details (case-insensitive, up to 50 rows), which distinguishes it from sibling tools like dataset_row or dataset_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: call this when you need rows matching a text query across dataset cells. However, it does not explicitly state when not to use it or which sibling tool to choose instead, so some routing burden falls on the agent.
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 Defectbird 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 full disclosure burden. It usefully discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted. However, it does not mention return format, error behavior, or handling of missing columns, leaving important 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, efficient sentence with no redundant filler. The statistic list is front-loaded, and the dataset scope and data-handling notes follow logically.
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 single-parameter tool with no output schema, the description covers scope and some edge-case behavior (commas, currency, non-numeric rows). However, it doesn't specify the output structure, so an agent may be uncertain whether the result is a flat object with keys count/min/max/mean/median/sum or something else. Error behavior is also unspecified.
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 description for the 'column' parameter (0% coverage), so the description must compensate. It clarifies that the parameter refers to a numeric column in the Defectbird dataset, adding some meaning beyond the bare schema. Yet it omits details like naming conventions, case sensitivity, or behavior for nonexistent or non-numeric 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 explicitly enumerates the statistics computed (count, min, max, mean, median, sum) and names the target resource as a numeric column of the Defectbird dataset. This makes the tool's function clear and distinguishes it from siblings like dataset_search or dataset_row, though it lacks an explicit verb such as 'computes' or 'returns'.
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 numeric columns of the Defectbird dataset, providing a clear context. However, it offers no guidance on when to prefer this tool over sibling tools like dataset_top or dataset_compare, and no exclusions are stated.
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 Defectbird 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 behavioral burden. It discloses ordering behavior (highest/lowest) and the numeric-column constraint, which are the main traits of a top-N query. It does not address tie handling, nulls, non-numeric columns, or confirm that the operation is non-mutating, but these are less central for a ranking 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?
A single, front-loaded sentence conveys the tool's core purpose without filler. It earns a 4 rather than 5 because it buries an important clarification ('which is the most/least X') in a trailing em-dash phrase instead of making it the primary lead.
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 three-parameter, read-style ranking tool, the description covers the main intent and the numeric-column requirement, and the schema fills in parameter types and bounds. It is slightly incomplete because it does not mention output behavior or how to choose this tool from its six siblings, and there is no output schema or annotations to compensate.
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 has a schema description), so the description must compensate. It does add meaning by describing the column parameter as numeric and by linking highest/lowest to ordering direction, but it provides no guidance on the limit parameter or the default sort direction 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?
Description states a specific operation — returning the highest or lowest rows of the Defectbird dataset ordered by a numeric column — and the parenthetical 'which is the most/least X' reinforces the ranking intent. It does not explicitly contrast with sibling tools such as dataset_stats or dataset_row, so it falls 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 'which is the most/least X' framing implies this tool is for top-N/bottom-N analytic questions, and 'numeric column' implies a prerequisite. However, there is no explicit statement of when to prefer this over dataset_stats, dataset_search, or dataset_row, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
dataset_columnsdataset_comparedataset_provenancedataset_rowdataset_searchdataset_statsdataset_topClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
Each tool has a distinct type of access: schema, provenance, exact row lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare are somewhat related, but the descriptions make the intended use clear.
All tools use a consistent dataset_ prefix followed by an operation noun or verb such as columns, compare, search, stats, and top. The naming pattern is predictable and makes the tool purpose easy to infer.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct data access need without redundancy or bloat.
The set covers schema discovery, provenance, exact lookup, substring search, comparisons, statistics, and top/bottom ordering, which covers most dataset Q&A workflows. There is no general-purpose filter or pagination tool, but the provided operations form a coherent query surface.