Superseded listing, not a dead server. This entry was imported from an early Glama registration under com.moltlinestudio.mcp; the same server is listed under its official MCP Registry name — use com.moltlinestudio/data. Endpoint unchanged: https://mcp.moltlinestudio.com/data — still live, still free on the free tier. Only this duplicate entry is deprecated.
Paste-your-data analytics over MCP, computed in code: csv_profile profiles columns and data quality, ab_test runs two-proportion z-tests, correlation and growth_rates cover the basics, with funnel_report, cohort_retention, and forecast_trend in the premium tier. No uploads, no external calls, no data retention.
Run a two-proportion A/B significance test with a plain-language verdict. FREE.
Typical input {"conversions_a": 120, "visitors_a": 2400, "conversions_b": 156, "visitors_b": 2380} returns {"rate_a_pct": 5.0, "rate_b_pct": 6.55, "relative_lift_pct": 31.1, "z_score": ..., "p_value": ..., "significant_at_95": true, "verdict": "B beats A — statistically significant"}.
Use when exactly two variants each have a trial count and a conversion count. Not for continuous outcomes such as revenue per user, and not for three or more variants. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need visitors > 0 and 0 <= conversions <= visitors"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| visitors_a | Yes | Visitors in variant A; must be at least 1. | |
| visitors_b | Yes | Visitors in variant B; must be at least 1. | |
| conversions_a | Yes | Conversions in variant A; 0 or more, at most visitors_a. | |
| conversions_b | Yes | Conversions in variant B; 0 or more, at most visitors_b. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds detail: 'Every call is read-only and idempotent' and explains error handling behavior ('never raises a protocol error — it returns {"error": ...}'), which is valuable context beyond the annotations.
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 tightly written with three short paragraphs: purpose, example, usage/errors. Every sentence adds value, and the most critical information is front-loaded. No wasted words.
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 tool's simplicity (4 integer parameters, statistical test), the description covers purpose, input format, output format, usage boundaries, error behavior, and idempotency. The presence of an output schema further supports completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions. The description adds a typical input JSON example and explains the constraint 'conversions ≤ visitors' via the error message example. This provides concrete usage context, though the schema already covers the basics.
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 begins with 'Run a two-proportion A/B significance test with a plain-language verdict,' which is a specific verb+resource. It also distinguishes itself from sibling tools like cohort_retention, funnel_report, etc., which are not A/B test 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?
Explicitly states when to use ('exactly two variants each have a trial count and a conversion count') and when not to use ('Not for continuous outcomes... not for three or more variants'). This provides clear exclusions and implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build a retention table and average curve from raw cohort counts. PREMIUM (license).
Typical input {"cohorts": {"2026-01": [1000, 400, 300, 250]}} — index 0 is cohort size, each later index is users still active in that period — returns {"retention_table_pct": {"2026-01": [100.0, 40.0, 30.0, 25.0]}, "avg_curve_pct": [...], "reading": "..."}.
Use when each cohort has counts per period since acquisition. Not for a one-pass funnel (funnel_report). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "cohort '' must map to a list of numbers,"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| cohorts | Yes | Mapping of cohort label to a list of counts, where counts[0] is the cohort size and counts[n] is users active in period n, e.g. {"2026-01": [1000, 400, 300]}. The first 24 cohorts are used. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description reinforces this by stating 'every call is read-only and idempotent' and adds details about error response format and retry safety, providing context beyond annotations.
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 compact yet comprehensive, with a clear structure: purpose, example, usage guidance, error handling, and idempotency. Every sentence contributes meaning 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?
Given one parameter with full schema coverage, an output schema (referenced), nested objects, and a complex use case, the description covers input/output format, constraints (first 24 cohorts), and error behavior, leaving no critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a detailed description of the 'cohorts' parameter. The description adds value by giving a concrete input example and showing the expected output, slightly exceeding the baseline of 3.
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 specifies the verb 'build' and resources 'retention table and average curve' from 'raw cohort counts'. It provides an explicit input/output example and distinguishes from sibling tool 'funnel_report' by stating it is not for a one-pass funnel.
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?
It clearly states when to use (each cohort has counts per period since acquisition) and when not to use (not for a one-pass funnel), naming 'funnel_report' as alternative. It also explains error handling behavior, enabling safe retry.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compute the Pearson correlation between two numeric series. FREE.
Typical input {"x": [1, 2, 3, 4], "y": [2.1, 3.9, 6.2, 8.1]} returns {"pearson_r": 0.999, "r_squared": 0.998, "interpretation": "very strong positive correlation", "caution": "..."}.
Use when two equal-length numeric series may move together. Reports association only, never causation. Not for a single series over time (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need two equal-length series of 3+ values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| x | Yes | First numeric series; at least 3 values, same length as y. | |
| y | Yes | Second numeric series; at least 3 values, same length as x. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by confirming 'Every call is read-only and idempotent' and detailing the error behavior ('never raises a protocol error — it returns {"error": ...}'). This provides practical context beyond the annotations.
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 efficiently structured: purpose first, then example, usage guidance, error behavior, and retry advice. Every sentence serves a purpose, with no redundancy or filler. It is concise yet comprehensive.
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 tool is relatively simple, has a full output schema, 100% schema coverage, and comprehensive annotations, the description still adds essential context: usage boundaries, error format, retry safety, and a sibling comparison. Nothing obvious is missing for an agent to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear parameter descriptions (e.g., 'First numeric series; at least 3 values, same length as y.'). The description adds a concrete usage example ('{"x": [1, 2, 3, 4], "y": [2.1, 3.9, 6.2, 8.1]}') and reinforces the requirement for equal-length series of 3+ values, improving agent understanding beyond the 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 starts with a clear verb+resource: 'Compute the Pearson correlation between two numeric series.' It distinguishes itself from sibling 'growth_rates' by explicitly stating 'Not for a single series over time (growth_rates).' The typical input/output example further solidifies what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use ('Use when two equal-length numeric series may move together'), when-not-to-use with an alternative ('Not for a single series over time (growth_rates)'), and a critical caveat ('Reports association only, never causation'). It also explains error handling, guiding the agent on retry behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Profile pasted CSV data column by column with data-quality flags. FREE.
Reports per-column type, null rate, unique count, numeric stats (min/mean/max), and top values. Typical input {"csv_text": "name,age\nAda,36\nLin,29"} returns {"rows": 2, "columns": {"age": {"type": "numeric", "null_pct": 0.0, "unique": 2, "min": 29, ...}}, "quality_flags": ["..."], "note": "first 2000 rows profiled"}.
Use as the first look at unfamiliar tabular data. Not for testing a hypothesis (ab_test, correlation) and not for time-ordered trends (growth_rates, forecast_trend). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "delimiter must be a single character, e.g. ',' or ';'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| csv_text | Yes | Raw CSV content including a header row, pasted as a single string; the first 2000 data rows are profiled. | |
| delimiter | No | Field separator, exactly one character, e.g. "," or ";". Default ",". | , |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry' and explains error handling: 'never raises a protocol error — it returns {"error": "..."}'. This provides valuable behavioral context beyond the annotations.
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 well-structured with a front-loaded purpose, followed by output format, usage guidelines, error handling, and safety. Every sentence adds value, and there is no redundancy. It is concise given the amount of useful information packed in.
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 tool's moderate complexity (2 parameters, simple output) and the presence of an output schema, the description covers all necessary aspects: input format, output structure, error handling, usage context, and safety. It is fully self-contained and leaves no gaps 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?
Schema description coverage is 100%, so the baseline is 3. The description includes an example usage and notes the delimiter constraint, but it does not add significant new meaning beyond what the schema already provides for the two parameters. The example is helpful but not essential for understanding parameter 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 starts with 'Profile pasted CSV data column by column with data-quality flags,' which is a specific verb+resource combination. It provides a typical input and output example, and clearly distinguishes from sibling tools by stating what it is not for (ab_test, correlation, growth_rates, forecast_trend).
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 says 'Use as the first look at unfamiliar tabular data' and then lists exclusions: 'Not for testing a hypothesis (ab_test, correlation) and not for time-ordered trends (growth_rates, forecast_trend).' This directly names alternatives and gives clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Forecast future periods with a linear trend and honest fit quality. PREMIUM (license).
For quick planning, not statistical modeling. Typical input {"values": [100, 120, 138, 161], "periods_ahead": 3} returns {"trend_per_period": 20.2, "r_squared": 0.998, "forecast": [180.9, 201.1, 221.3], "caveat": "..."}.
Use when a series is roughly linear and fit quality matters as much as the projection. Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 4 historical values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| values | Yes | Ordered historical series, oldest first; at least 4 values. | |
| periods_ahead | No | How many future periods to forecast; values outside 1-12 are clamped. Default 3. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds significant behavioral detail: 'this tool never raises a protocol error — it returns an error object with fix instructions' and 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry.' No contradictions with annotations.
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 well-structured: purpose statement, premium note, example, usage guidelines, error handling. It is front-loaded with the main action. The example, while helpful, adds a bit of length; overall it is concise without unnecessary repetition.
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?
With only 2 parameters, rich annotations, and an output schema, the description covers all essential aspects: purpose, usage context, behavioral traits (idempotent, read-only, error handling), parameter semantics, and output format (trend_per_period, r_squared, forecast, caveat). It leaves no gaps for an agent to misunderstand.
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 100%, so baseline is 3. The description provides a typical input example that demonstrates proper parameter usage and includes error handling details that clarify validation behavior (e.g., minimum 4 values). This adds value beyond the schema's parameter descriptions.
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 'Forecast future periods with a linear trend and honest fit quality,' specifying the verb (forecast), resource (future periods), and method (linear trend). It includes a typical input/output example, distinguishes from the sibling 'growth_rates' by noting it's not for measuring already observed growth, and sets expectations with 'For quick planning, not statistical modeling.'
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 says 'Use when a series is roughly linear and fit quality matters as much as the projection. Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates).' This provides clear when-to-use and when-not-to-use guidance, and names an alternative tool (growth_rates).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze a conversion funnel and find the biggest drop-off. PREMIUM (license).
Typical input {"stages": {"Visited": 1000, "Signed up": 200, "Paid": 50}} returns {"steps": [{"from": "Visited", "to": "Signed up", "conversion_pct": 20.0, "lost": 800}, ...], "overall_conversion_pct": 5.0, "biggest_dropoff": {...}, "recommendation": "..."}.
Use when stage counts descend through one funnel. Not for retention over time (cohort_retention) and not for two-variant comparisons (ab_test). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 2 stages"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| stages | Yes | Ordered mapping of stage name to count, top of funnel first; at least 2 stages with non-negative numeric values, e.g. {"Visited": 1000, "Signed up": 200}. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds critical behavioral context: error handling returns a structured error object instead of protocol errors, and it explains retry safety. The 'PREMIUM (license)' note also sets usage expectations.
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 efficiently structured: purpose, example, usage guidance, and error behavior in four clear sections. Each sentence adds value, and the front-loaded purpose lets an agent quickly grasp the tool's function.
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 tool with a rich output schema, the description covers purpose, input example, usage constraints, error semantics, and licensing. It is fully self-contained for selection and 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 100%, so the schema already documents the 'stages' parameter. The description provides a typical input example, which is helpful but doesn't add semantic meaning beyond what the schema offers. Baseline 3 applies.
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 opens with 'Analyze a conversion funnel and find the biggest drop-off,' which uses a specific verb and resource. It further distinguishes from siblings by explicitly naming cohort_retention and ab_test as alternatives for other use cases.
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?
It gives explicit when-to-use guidance ('Use when stage counts descend through one funnel') and when-not-to-use guidance with named alternatives ('Not for retention over time (cohort_retention) and not for two-variant comparisons (ab_test)').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compute period-over-period growth and CAGR for a numeric series. FREE.
Typical input {"values": [1000, 1100, 1320]} returns {"period_over_period_pct": [10.0, 20.0], "total_change_pct": 32.0, "avg_growth_per_period_pct_cagr": 14.89}.
Use when one series is already in period order. Not for comparing two variants (ab_test) and not for projecting future periods (forecast_trend). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 2 values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| values | Yes | Ordered numeric series, oldest first, at least 2 values, e.g. monthly revenue [1000, 1100, 1320]. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint; description adds value by reinforcing idempotency with retry guidance and detailing error behavior ('never raises a protocol error — it returns {"error": ...}'), exceeding what annotations offer. No contradiction.
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?
Every sentence earns its place: purpose, example, usage guidelines, error handling, idempotency. Front-loaded with the key action and typical input/output. No redundancy or waste.
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 one-parameter tool with an output schema, the description covers purpose, input format, output fields, error behavior, and usage guidance. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description of the single parameter. The tool description provides an example and output fields but adds no new semantic meaning beyond the schema. Baseline 3 is appropriate.
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 uses a specific verb ('Compute period-over-period growth and CAGR') and resource ('numeric series'), and clearly distinguishes from siblings by naming alternative tools (ab_test, forecast_trend) and contexts.
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 states when to use ('when one series is already in period order') and when not to ('Not for comparing two variants... not for projecting future periods'), with specific sibling tool names for both 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.
ab_testcohort_retentioncorrelationcsv_profileforecast_trendfunnel_reportgrowth_ratesClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool addresses a distinct analytical task: A/B test, cohort retention, correlation, CSV profiling, forecasting, funnel analysis, and growth rates. There is no overlap, and the descriptions clearly state when to use each and what not to use it for.
All tool names follow a consistent snake_case pattern with descriptive noun_noun combinations (e.g., ab_test, cohort_retention, growth_rates). No mixing of conventions or vague verbs.
7 tools is a well-scoped set for a data analysis server. It covers a range of common statistical and data profiling tasks without being overwhelming or too sparse.
The tool surface covers core analytical needs: hypothesis testing, retention analysis, correlation, data profiling, forecasting, funnel analysis, and growth rates. Minor gaps exist (e.g., no general descriptive statistics beyond CSV profiling, no regression), but the set feels intentional and sufficient for typical data desk queries.