mcp-data-pipeline-connector
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 | {} |
| logging | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| connect_sourceA | Register a data source (CSV file, Postgres database, or REST API). Credentials must be in environment variables or a YAML config file — never pass connection strings directly. |
| list_sourcesA | List all registered data sources and their connection status. |
| list_tablesA | List available tables across all sources, or just the named source. |
| get_schemaA | Return the column names and types for a specific table. |
| queryA | Execute a SQL query against a registered data source using DuckDB. Returns up to --max-rows rows (default 1000). In read-only mode (default), only SELECT statements are allowed. Use source='_all' to query across all CSV sources with cross-source joins. Supports limit and offset for pagination. |
| transformB | Apply aggregations, filters, renaming, or column selection to a source table and return or save results. |
| check_healthA | Check whether registered data sources are still reachable and responsive. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| explore-data | Guide agents through schema discovery and querying. Walks through list_sources → list_tables → get_schema before writing any queries. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool targets a distinct concern: connection registration, listing sources, tables, schemas, querying, transforming, and health checks. No two tools have overlapping purposes, making selection unambiguous.
All tool names follow an imperative verb pattern (connect_source, list_sources, list_tables, get_schema, check_health), with 'query' and 'transform' as clear single-verb actions. The convention is consistent and predictable.
Seven tools cover the core pipeline connector workflow without bloat. Each tool serves a distinct function and contributes to the overall purpose, making the count well-scoped.
The surface covers source registration, discovery, schema inspection, querying, transformation, and health monitoring. Minor gaps exist (no delete/update source), but the core lifecycle of connecting and exploring data is well covered.