An MCP server for querying IPL (Indian Premier League) cricket database using SQL with LangChain agent integration
Server has 3 tools with minimal quality. Two trivial tools (add, greet) lack meaningful descriptions and serve no real purpose. The primary tool (ipl_sql_agent) has a vague description and no documented output schema. No parameter descriptions beyond type hints. No error handling guidance. No input validation. Schemas are inferred from type hints only, no explicit JSON Schema visible. This is a proof-of-concept, not production-ready.
Add two numbers
Say hello to someone
Use the SQL agent to answer questions about the IPL database
No output schema documented for any tool. ipl_sql_agent returns a string but LLMs cannot plan downstream actions without knowing the structure of the response.
ipl_sql_agent description is vague ('Use the SQL agent to answer questions about the IPL database'). Does not explain when to use it, what it returns, error conditions, or rate limits. LLMs cannot reliably select this tool.
No error handling or recovery guidance. If ipl_sql_agent fails (malformed query, database error, LLM timeout), the tool returns nothing actionable. No guidance for the LLM to retry, refine, or escalate.
add and greet are trivial placeholder tools with no real utility. They dilute the tool registry and waste LLM reasoning cycles. Remove them or replace with domain-relevant tools.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 41 | 2026-07-28+ | v2 |
No input validation. ipl_sql_agent accepts any string as 'query' with no length limits, SQL injection guards, or rate limiting. Malicious or runaway queries could hang the database or exhaust resources.