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    <title>PyPI recent updates for jdatamunch-mcp</title>
    <link>https://pypi.org/project/jdatamunch-mcp/</link>
    <description>Recent updates to the Python Package Index for jdatamunch-mcp</description>
    <language>en</language>    <item>
      <title>1.13.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.13.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 14 May 2026 16:32:56 GMT</pubDate>
    </item>    <item>
      <title>1.12.2</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.12.2/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 13 May 2026 17:29:30 GMT</pubDate>
    </item>    <item>
      <title>1.12.1</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.12.1/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 13 May 2026 00:00:19 GMT</pubDate>
    </item>    <item>
      <title>1.12.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.12.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 19:09:36 GMT</pubDate>
    </item>    <item>
      <title>1.11.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.11.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 18:58:09 GMT</pubDate>
    </item>    <item>
      <title>1.10.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.10.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 18:46:40 GMT</pubDate>
    </item>    <item>
      <title>1.9.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.9.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 17:27:41 GMT</pubDate>
    </item>    <item>
      <title>1.8.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.8.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 17:14:24 GMT</pubDate>
    </item>    <item>
      <title>1.7.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.7.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 16:40:34 GMT</pubDate>
    </item>    <item>
      <title>1.6.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.6.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 16:29:57 GMT</pubDate>
    </item>    <item>
      <title>1.5.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.5.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 12 May 2026 02:07:49 GMT</pubDate>
    </item>    <item>
      <title>1.4.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.4.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 28 Apr 2026 01:03:14 GMT</pubDate>
    </item>    <item>
      <title>1.1.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.1.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 28 Apr 2026 00:46:08 GMT</pubDate>
    </item>    <item>
      <title>1.0.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/1.0.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 28 Apr 2026 00:11:09 GMT</pubDate>
    </item>    <item>
      <title>0.8.4</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.8.4/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 15 Apr 2026 16:21:38 GMT</pubDate>
    </item>    <item>
      <title>0.8.3</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.8.3/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 09 Apr 2026 18:49:57 GMT</pubDate>
    </item>    <item>
      <title>0.8.2</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.8.2/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 18:00:42 GMT</pubDate>
    </item>    <item>
      <title>0.8.1</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.8.1/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 17:48:04 GMT</pubDate>
    </item>    <item>
      <title>0.8.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.8.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 16:47:48 GMT</pubDate>
    </item>    <item>
      <title>0.7.1</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.7.1/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 16:31:24 GMT</pubDate>
    </item>    <item>
      <title>0.7.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.7.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 16:19:17 GMT</pubDate>
    </item>    <item>
      <title>0.6.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.6.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 16:09:34 GMT</pubDate>
    </item>    <item>
      <title>0.5.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.5.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 15:59:18 GMT</pubDate>
    </item>    <item>
      <title>0.4.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.4.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 08 Apr 2026 15:48:02 GMT</pubDate>
    </item>    <item>
      <title>0.3.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.3.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 02 Apr 2026 02:44:57 GMT</pubDate>
    </item>    <item>
      <title>0.2.1</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.2.1/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Tue, 31 Mar 2026 13:14:03 GMT</pubDate>
    </item>    <item>
      <title>0.2.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.2.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel/Parquet/JSONL indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 26 Mar 2026 21:19:34 GMT</pubDate>
    </item>    <item>
      <title>0.1.3</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.1.3/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 26 Mar 2026 20:42:42 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.1.2/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 26 Mar 2026 15:23:36 GMT</pubDate>
    </item>    <item>
      <title>0.1.1</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.1.1/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel indexing</description>
<author>j@gravelle.us</author>      <pubDate>Thu, 26 Mar 2026 10:36:33 GMT</pubDate>
    </item>    <item>
      <title>0.1.0</title>
      <link>https://pypi.org/project/jdatamunch-mcp/0.1.0/</link>
      <description>Token-efficient MCP server for tabular data retrieval via CSV/Excel indexing</description>
<author>j@gravelle.us</author>      <pubDate>Wed, 25 Mar 2026 19:27:53 GMT</pubDate>
    </item>  </channel>
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