pubdata-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PUBDATA_DATA_GO_KR_KEY | No | Service key for data.go.kr API to fetch real data instead of mock data. Optional. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_datasetsA | List datasets available in the mart with their title and description. |
| describe_schemaB | Return column names and types for a table so the agent can write SQL. |
| query_sqlA | Run a read-only SELECT against the mart. Only single SELECT / WITH statements are allowed; results are row-capped. Use describe_schema to learn column names before querying. |
| price_statsA | Average / min / max / count of price_manwon grouped by a column. A convenience analytics tool so the agent needn't hand-write aggregation SQL. |
| correlateA | Pearson correlation between two numeric columns across two datasets. Joins the tables on their shared keys (obs_date and/or region_code) and returns corr(col_a, col_b) plus the joined sample size. Example: correlate weather.avg_wind_ms with air_quality.pm10 to see whether windy days have cleaner air. Table and column names are validated against the live schema before use. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| catalog | Human-readable catalog of datasets, exposed as an MCP resource. |
TDQS
Scored across 5 tools
Each tool has a clear, distinct purpose: listing datasets, describing schemas, running SQL queries, computing price statistics, and calculating correlations. No overlap or ambiguity.
All tools use consistent snake_case naming with verb-first patterns (list_datasets, describe_schema, query_sql, price_stats, correlate). The single verb 'correlate' fits the pattern.
Five tools cover the core functionality of data discovery, schema exploration, custom queries, and common analytics. This is a well-scoped set for a data mart server.
The tool set covers essential workflows: discover (list_datasets), understand (describe_schema), query (query_sql), and two analytics helpers. Minor gaps like missing aggregation functions, but overall complete for the intended domain.