text2flink
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | No | OpenAI API key for the LLM layer (default gpt-4o). | |
| ANTHROPIC_API_KEY | No | Anthropic API key for the LLM layer. |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_flink_jobA | Generate an Apache Flink SQL streaming job from a natural-language request and a schema, then RUN it on real Flink against sample data and return the verified SQL plus its actual output (or the Flink error). Use whenever the user wants a Flink streaming job (windowed aggregates, top-N, dedup, interval/temporal joins, filters) — it produces jobs that provably run, unlike hand-written SQL. Supply representative sample rows. |
| ground_kafka_topicA | Discover a Kafka topic's schema by sampling its messages, returning a schema + sample rows ready to pass to generate_flink_job. Use when the user references a real topic and you don't have its schema. Requires a reachable Kafka broker with the topic populated. |
| deploy_to_kafkaA | Produce a deployable topic->topic Flink job: INSERT INTO a Kafka sink topic SELECT ... The SELECT logic is verified on the sample data by running it on real Flink; the returned deployable_sql is the full pipeline (upsert-kafka sink for updating jobs). Use when the user wants to WRITE results to a Kafka topic, not just query them. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: schema discovery, job generation with verification, and deployment to Kafka. There is no overlap that would confuse an agent.
All tool names follow a consistent verb_noun pattern in snake_case (generate_flink_job, ground_kafka_topic, deploy_to_kafka), making the set predictable and easy to navigate.
Three tools is a well-scoped count for a focused pipeline server, covering the essential stages without bloat or deficiency.
The tools form a complete workflow: discover a Kafka schema, generate and verify a Flink job, and deploy it to a Kafka sink. No obvious gaps or dead ends for the stated domain.