Koskamo

Dataset Aggregate & Pivot MCP

A remote MCP server that enables agents to perform SQL GROUP BY operations and generate pivot tables directly on JSON row data. It supports 11 functions including date buckets, top N selections, totals, and handling messy numbers, with a limit of 500 rows per call.

Endpoint and access

No sign-in to connect and list tools. Individual tools may still need a vendor account, credentials, or permissions; check the documented prerequisites.

Dataset Aggregate & Pivot MCP logo, by Nero LabsCommunity listingStreamable HTTPNo sign-inEndpoint checked 2026-11-07Published by Nero Labs
MCP endpoint (Streamable HTTP)
https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp
Vendor documentation

What its tools do

2 tools, grouped by the hints the server advertises. Hints are self-declared, not verified behavior.

2 of 2 have a description · 0 of 2 declare behavior hints · 1 of 1 with parameters document them all

Checks and signals

Each line is separate evidence, not a trust score.

  • Passed Endpoint answered the live check ·
  • Availability 1 of 1 checks passed in 30 days

What you can do with it

  • Perform SQL GROUP BY aggregations on JSON datasets
  • Generate pivot tables from JSON rows
  • Calculate totals and top N values
  • Handle date bucketing and messy numbers

Source and maintenance

Community listingRegistry 0.1.0
Maintenance checked

Protocol and capabilities

Server
nerolabs-dataset-aggregate-pivot 0.1.0
MCP protocol negotiated in check
2025-06-18
Tools
2 advertised
Resources
Not advertised
Resource templates
Not advertised
Prompts
Not advertised
Server-advertised capabilities
{
  "tools": {
    "listChanged": false
  }
}

Server-provided instructions

Untrusted server output shown for inspection. It is not Koskamo guidance and is excluded from knowledge search.

Call aggregate_rows with your rows, groupByFields and aggregations to get a GROUP BY or pivot-table summary in one call. Call list_capabilities first if you are unsure which functions, date buckets or limits apply.

Tools

2 advertised

list_capabilities
Advertised by the server during the last successful anonymous check.

Returns the 11 aggregation functions and what each one does, the date bucket formats, the labels used for blank, invalid-date and total rows, and the limits per call (rows, pivot columns, pivot cells, aggregations). Call this first if you are unsure what is available. Free, processes no data.

list_capabilities input schema
{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
aggregate_rows
Advertised by the server during the last successful anonymous check.

SQL GROUP BY and a spreadsheet pivot table for a list of JSON rows, in one call. Returns one output row per group with the aggregated columns, plus a summary: groups found, groups dropped by topN, values skipped because they were blank or not numeric (never guessed), and warnings such as a misspelled field name. Use it to turn scraped or API records into totals: orders and revenue per region, average price per brand, listings per city per month, top 10 products by revenue. Messy data is expected: "South" and "south " group together, and "$1,234.50" sums as 1234.5. Leave groupByFields empty to summarise all rows into one row. At most 500 rows per call.

aggregate_rows input schema
{
  "type": "object",
  "required": [
    "rows"
  ],
  "properties": {
    "rows": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "The records to aggregate, up to 500. Each row is a JSON object; keys may differ between rows."
    },
    "topN": {
      "type": "integer",
      "minimum": 1,
      "description": "After sorting, keep only the first N groups. The totals row still covers every input row."
    },
    "sortBy": {
      "type": "string",
      "description": "An output column to sort by: a group field, an aggregation alias such as \"total_amount\", or a pivot column. Omitted sorts by the group fields."
    },
    "pivotField": {
      "type": "string",
      "description": "Turns this field's distinct values into columns, pivot-table style: group by \"region\" and pivot on \"product\" for one row per region with a column per product. Must not also be a group-by field. At most 50 distinct values."
    },
    "aggregations": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "function"
        ],
        "properties": {
          "alias": {
            "type": "string",
            "description": "Output column name."
          },
          "field": {
            "type": "string",
            "description": "The field to aggregate. Optional for count, which then counts rows."
          },
          "function": {
            "enum": [
              "count",
              "countDistinct",
              "sum",
              "avg",
              "min",
              "max",
              "median",
              "first",
              "last",
              "list",
              "listDistinct"
            ],
            "type": "string"
          }
        }
      },
      "description": "What to compute per group, for example [{\"function\":\"count\",\"alias\":\"orders\"}, {\"field\":\"amount\",\"function\":\"sum\",\"alias\":\"total_amount\"}]. Every function except count needs a field. alias is the output column name (defaults to function_field, or \"count\"). Omitted gives a plain row count per group. Up to 20."
    },
    "groupByFields": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "One output row per distinct combination of these field values, like SQL GROUP BY, for example [\"region\"] or [\"city\", \"category\"]. Dot paths like \"address.city\" work. Empty or omitted aggregates every row into a single row."
    },
    "groupMatching": {
      "enum": [
        "normalized",
        "exact"
      ],
      "type": "string",
      "description": "normalized (default) ignores letter case and extra whitespace when grouping, so \"South\" and \"south \" are one group. exact requires identical values."
    },
    "pivotFunction": {
      "enum": [
        "count",
        "countDistinct",
        "sum",
        "avg",
        "min",
        "max",
        "median",
        "first",
        "last",
        "list",
        "listDistinct"
      ],
      "type": "string",
      "description": "How pivot cell values are combined. Defaults to sum when pivotValueField is set; ignored (row count) without one."
    },
    "sortDirection": {
      "enum": [
        "asc",
        "desc"
      ],
      "type": "string",
      "description": "asc (default) or desc. Use desc with topN for \"top N by\" questions."
    },
    "lenientNumbers": {
      "type": "boolean",
      "description": "On by default: \"$1,234.50\", \"49 USD\", \"12%\" and \"(300)\" count as numbers for sum, avg, min, max and median. Set false to accept only real numbers and plain numeric strings."
    },
    "dateBucketField": {
      "type": "string",
      "description": "A date or timestamp field to group by time period, for example \"orderedAt\". Adds a group column named like \"orderedAt_month\". Unreadable dates land in an \"(invalid date)\" group."
    },
    "pivotValueField": {
      "type": "string",
      "description": "The field whose values fill the pivot cells, for example \"amount\". Omitted fills each cell with a row count."
    },
    "includeTotalsRow": {
      "type": "boolean",
      "description": "Appends a grand-total row labelled \"(total)\" and adds a _rowType column (\"group\" or \"total\")."
    },
    "dateBucketGranularity": {
      "enum": [
        "day",
        "week",
        "month",
        "quarter",
        "year"
      ],
      "type": "string",
      "description": "Bucket size for dateBucketField: day (2026-08-19), week (2026-W34), month (2026-08, default), quarter (2026-Q3) or year."
    }
  },
  "additionalProperties": false
}

Prompts

Not advertised

No prompt definitions available.

Resources

Not advertised

No resource definitions available.

90-day uptime

A lightweight connection check about every six hours, sampled on hosts with many listings. It shows the server answered, not that its tools work.

Too few checks for a percentage yet (1 of 1 answered).

  1. 2026-11-07: 1 of 1 checks answered

30-day availability

Aggregated from read-only remote checks; this is not a tool-execution or security guarantee.

Successful checks
1 of 1 (100%)
Average latency
290 ms
95th percentile latency
290 ms

Maintenance history

Public check and source-refresh events. Editorial and ownership records remain private.

  • · probe · passed · 290 ms · metadata changed · 2 tool(s)

Connect from your client

Each snippet registers the same endpoint; pick the client you use.

This server is operated by the named third party. Listing and endpoint checks are not an endorsement or a security audit. Review the operator's permissions, terms, and data handling before connecting. How listings are checked and corrected.

Claude Code

Run this once in a terminal. It registers the remote server. Use --scope project to write .mcp.json for the repo.
Claude Code configuration
claude mcp add --transport http dataset-aggregate-pivot-mcp https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp

Codex

Run this once. Codex stores MCP servers in ~/.codex/config.toml (or .codex/config.toml in a trusted project).
Codex configuration
codex mcp add dataset-aggregate-pivot-mcp --url https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp

Cursor

Merge this server into .cursor/mcp.json in your project, then enable dataset-aggregate-pivot-mcp in your MCP settings.
Cursor configuration
{
  "mcpServers": {
    "dataset-aggregate-pivot-mcp": {
      "url": "https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp"
    }
  }
}

Cline

Merge this into cline_mcp_settings.json (Cline → MCP Servers → Configure).
Cline configuration
{
  "mcpServers": {
    "dataset-aggregate-pivot-mcp": {
      "type": "streamableHttp",
      "url": "https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp",
      "disabled": false
    }
  }
}

VS Code

Merge this server into .vscode/mcp.json. Use MCP: List Servers to start dataset-aggregate-pivot-mcp and inspect its tools.
VS Code configuration
{
  "servers": {
    "dataset-aggregate-pivot-mcp": {
      "type": "http",
      "url": "https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp"
    }
  }
}

Gemini CLI

Run this once. It writes .gemini/settings.json in the current project; pass --scope user for ~/.gemini/settings.json.
Gemini CLI configuration
gemini mcp add --transport http dataset-aggregate-pivot-mcp https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp

Claude Desktop

Open Claude Settings > Connectors > Add custom connector, enter this server URL and enable it for the conversation. This is a manual remote-connector handoff, not a desktop JSON configuration.
Claude Desktop configuration
https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp

Windsurf

Merge this into ~/.codeium/windsurf/mcp_config.json, then refresh MCP in Cascade. Windsurf remote servers use serverUrl, not url.
Windsurf configuration
{
  "mcpServers": {
    "dataset-aggregate-pivot-mcp": {
      "serverUrl": "https://dataset-aggregate-pivot.nerolabs.workers.dev/mcp"
    }
  }
}

Let your agent find its tools

Find servers like Dataset Aggregate & Pivot MCP from your agent. Add Koskamo once to search the index for endpoints, access requirements, and client setup. Public discovery needs no key.