Skip to main content
Glama

Related Servers

Alternatives to japan-data-mcp

  • A
    license
    A
    quality
    A
    maintenance
    MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.
    1
    29 PyPI
    1
    MIT

Related Servers

  • A
    license
    A
    quality
    C
    maintenance
    A read-only DuckDB MCP server offering context-efficient analytics tools (list_datasets, describe_table, profile_column, explain, query) with a semantic layer for business rules, security guards, and disclosed truncation to help LLMs produce correct answers while minimizing token usage.
    5
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    An MCP server for discovering, downloading, querying, and analyzing datasets from Ontario's open data portals, allowing natural language questions and high-performance analytics via DuckDB.
    23
    1
    MIT
  • F
    license
    B
    quality
    D
    maintenance
    MCP server for searching Japanese government procurement notices via the Kanpou API. Enables LLMs to search by date, keyword, or detailed criteria.
    3
    1
    -
  • A
    license
    A
    quality
    B
    maintenance
    MCP server for Japanese public business data, enabling AI agents to validate and look up corporate numbers, search bank/branch codes, and check national holidays. Runs locally with no telemetry; live corporate registry data requires a free NTA app ID.
    8
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server implementation that connects AI assistants to DuckDB, enabling them to query and analyze data from various sources including CSV, Parquet, JSON, and cloud storage through SQL.
    19
    MIT

TDQS

A4.4/5.0

Scored across 5 tools

Disambiguation4/5

Each tool has a clearly distinct role (discover datasets, inspect schema, fetch rows, run SQL, list B2B bundles), and descriptions explicitly cross-reference WHEN NOT TO USE. The only mild overlap is between search_japan_official_data and execute_japan_megabank_sql, since both retrieve records, but the simple-lookup vs JOIN/aggregation boundary is well delineated.

Naming Consistency4/5

All names follow a consistent snake_case verb_noun pattern (list_*, describe_*, search_*, execute_*). Minor inconsistency in the domain qualifier used (japan, japan_official, japan_megabank, flagship_b2b) and singular/plural switch between describe_japan_dataset and list_japan_datasets, but the convention is still predictable.

Tool Count5/5

Five tools is well-scoped for a data-discovery-and-query server, and each earns its place covering a distinct stage of the workflow. No redundant or trivial tools, and nothing feels artificially padded.

Completeness4/5

The read-only lifecycle is well covered: discover datasets, inspect schema, fetch rows, run cross-table SQL, plus a B2B bundle listing. The main gap is the absence of an export/download or bundle-purchase action, but for a read-only query surface the coverage is essentially complete.

Maintenance

ActivityMaintained
ResponsivenessResponsive