TermsBird: 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 TermsBird 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?
Given no annotations, the description carries the full burden. It implies a read-only operation by describing the returned metadata, and the 'call first' advice suggests no side effects. It does not explicitly state 'does not modify data', but the nature of the tool makes this obvious enough for a high score.
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 brief and to the point, listing the exact outputs in one sentence and adding a clear usage instruction in the second. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a schema-introspection tool, the description covers all essential aspects: the columns, numeric indication, row count, and provenance banner. It also includes the critical 'call first' guidance, making it complete for an agent to understand the tool's role and output.
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 takes zero parameters, so the baseline of 4 applies. The description correctly omits any parameter details since there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the columns, indicates which are numeric, provides the row count, and shows the provenance banner. It is specific and directly distinguishes this tool from the sibling tools, which focus on searching, comparing, or retrieving individual rows.
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?
Explicitly instructs to 'Call this first to learn the schema', providing clear when-to-use guidance. This tells the agent this is the initial exploratory tool, which is distinct from the other dataset operations.
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 TermsBird 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 burden of behavioral disclosure. It does mention that rows are returned 'in the order given', which is a useful behavioral detail. However, it does not disclose side effects (e.g., read-only nature), error handling (e.g., missing column or no matching values), or output format, leaving significant ambiguity about the tool's runtime 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, concise sentence that front-loads the primary function (extracting rows by column values) and then adds order behavior and use case. It avoids unnecessary verbosity and presents information in a logical flow, making it easy to parse. The structure is effective, though slightly dense.
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 an output schema and annotations, the description is incomplete for full context. It does not clarify the output row structure (e.g., whether all columns are returned), or handle edge cases like no matches or invalid column names. It also does not mention how this tool fits relative to the sibling tools, leaving gaps in the agent's ability to anticipate results and make robust decisions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains that 'column' is used for matching (i.e., rows whose column matches any value) and that 'values' are the set of values to match, also implying order significance. This provides some semantic meaning beyond the schema's bare types. However, it does not specify details like exact vs. fuzzy matching, case sensitivity, or handling of duplicate values, which are important given the schema has no parameter descriptions (0% 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 description clearly states that the tool returns rows from the TermsBird dataset where a specified column matches any of the given values, with results ordered by the provided value list. It explicitly mentions the intended use case ('for X vs Y questions'), making the core function unambiguous. However, it does not explicitly name the action verb (e.g., 'retrieve'), but the phrasing implies retrieval.
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 offers minimal guidance on when to use this tool over siblings. It hints at a comparison scenario ('for X vs Y questions') but does not explicitly differentiate from tools like dataset_search or dataset_row. It lacks clear instruction on when this tool is preferred or how it relates to alternatives, leaving the agent to infer the selection criteria.
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 TermsBird 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 full burden. It discloses the exact information returned (source, computed date, licence, citation) and implies a read-only, informational operation. It does not specify output format, but that is a minor gap for a zero-parameter metadata tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences: the first lists the content, the second states the use case. There is no redundant wording or 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?
Given zero parameters and no output schema, the description is complete. It identifies what is returned and why an agent would invoke this tool. Nothing else is needed 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 tool has zero parameters, so the baseline is 4. The description correctly avoids parameter details since none exist; there is nothing more it needs to communicate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool provides: source, date computed, licence, and citation for the TermsBird dataset. It also names the intended use ('attribute a figure correctly'). This differentiates it from sibling tools like dataset_columns or dataset_stats, which handle different data operations.
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 explicit usage context: 'Read this to attribute a figure correctly.' It does not name alternatives or exclusions, but the sibling tool names are semantically distinct enough that an agent can infer when this tool is relevant versus data manipulation tools.
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 TermsBird dataset where a column equals a value exactly (case-insensitive).
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| column | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It usefully reveals case-insensitive exact matching, but it does not clarify whether the result is a single row or all matching rows, nor what happens when no row matches. The title says 'a row' while the description says 'rows,' leaving the return behavior ambiguous.
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 tightly worded sentence with no filler. The matching semantics are front-loaded, making it quickly scannable.
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 only two string parameters and no output schema, the description conveys the core inputs and matching rule. It is largely sufficient to invoke the tool, although explicit routing to alternatives like dataset_search would make it 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 descriptions for the two parameters, so the natural-language phrase 'where a column equals a value' must supply meaning. It establishes that column is a field name and value is the comparison value, but it does not add details like acceptable column names or how case-insensitivity applies to both sides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the operation: it returns rows from the TermsBird dataset where a specified column equals a given value exactly (case-insensitively). This is specific about the resource and matching semantics, though it does not explicitly contrast itself with dataset_search or other 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 phrase 'equals a value exactly (case-insensitive)' implies this is for exact-match lookups, but there is no explicit guidance about when to choose this over dataset_search or dataset_compare. The intended use can be inferred, not directly instructed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rows of the TermsBird 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 that matching is case-insensitive and limited to 50 rows, but does not explicitly state that the operation is read-only or side-effect-free. Given the search nature, it is likely safe but not explicitly confirmed.
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 concise sentence that conveys the core functionality without unnecessary words. It is well-structured and front-loaded with the main action.
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, the description provides enough context: what it returns, the matching rule, and the row limit. It does not need to explain output format or error handling, given the simplicity. The absence of explicit side-effect info is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to both parameters: query is the text to search for in any cell, and limit is the maximum number of rows returned (up to 50). This supplements the schema, especially for limit which has no schema description. Slight room for more explicit parameter behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns rows from the TermsBird dataset that contain the query, which is a specific verb (search/filter) and resource. It distinguishes from sibling tools like dataset_top (top rows) or dataset_row (single row) by focusing on content matching.
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 (when you need rows matching a text query) but does not explicitly compare to alternatives or state prerequisites. It lacks direct guidance like 'use this to search across all cells' but the behavior is sufficiently clear.
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 TermsBird 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 behavioral burden. It discloses that grouping commas and currency are handled, and that non-numeric rows are excluded and counted, which is valuable edge-case context. It also enumerates the exact statistics returned.
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, well-organized sentence that front-loads the key statistics and then adds parenthetical edge-case handling. Every word contributes value with no repetition or 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 one-parameter aggregation tool with no output schema, the description adequately covers return values, column semantics, and data-cleaning behavior. It does not explicitly state the output response format or behavior for a missing/invalid column, but these are minor gaps for such a simple contract.
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 string with minLength 1, so parameter semantics are otherwise absent. The description adds meaning by clarifying that the column must be numeric, belong to the TermsBird dataset, and may contain formatted values like commas or currency that are normalized.
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 states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the TermsBird dataset. This clearly differentiates it from siblings like dataset_search, dataset_row, or dataset_top, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it operates on a numeric column, but it provides no explicit when-to-use guidance or comparisons to alternatives among the sibling tools. There are no exclusions, but also no advice on when to pick this tool over dataset_columns or dataset_top.
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 TermsBird 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?
No annotations are present, so the description carries the full burden. It discloses the core behavior of ranking rows and hints at ascending/descending directions, but omits details such as what fields are returned, the default limit, and behavior for ties or missing numeric values.
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 that efficiently expresses the tool's purpose. The parenthetical 'which is the most/least X' is slightly redundant with 'highest or lowest' but adds an intuitive query framing without bloating the text.
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 tool with no output schema, the description covers the core behavior. However, missing details about the default value for 'limit' and the exact structure of returned rows leave an agent with reasonable but incomplete guidance for a fully informed 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 described). The description adds meaningful semantics for 'column' by specifying it must be numeric, and 'highest (or lowest)' reinforces the ascending option. However, 'limit' receives no semantic context from either the schema or the description.
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 a specific operation: returning the highest or lowest rows of the TermsBird dataset by a numeric column, framed as 'which is the most/least X.' This goes beyond the title and distinguishes the tool from generic search or stats tools, though it does not explicitly differentiate from sibling tools by name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'which is the most/least X' framing provides clear context for when to use this tool: when the user wants extreme rows by a numeric measure. It does not list alternatives or exclusions, but the intended use case is unambiguous.
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 clearly distinct purpose: schema discovery, filtering, provenance, exact lookup, substring search, statistics, and top/bottom sorting. No two tools overlap in function, and the descriptions reinforce the boundaries.
All tools follow the consistent dataset_<verb> pattern with no deviations. The naming convention is uniform and predictable.
7 tools is well-scoped for a dataset querying server. Each tool covers a necessary operation without redundancy or bloat.
The tool set covers schema, row retrieval, search, statistics, sorting, comparison, and provenance. The only minor gap is a straightforward 'get all rows' operation, but the existing tools can likely cover most workflows.