Connect Claude Code, Cursor & Windsurf to enterprise data sources. One JSON config. Supports multiple databases, APIs, and services through a unified MCP interface.
22 tools with consistent naming (verb_noun pattern) and basic schemas, but critical gaps in descriptions and parameter documentation. All tools have input schemas with types, but descriptions are generic (avg ~60 chars, well below 194-char baseline). Parameters lack depth: no enums for constrained values (e.g., HTTP methods in call_rest_api), no format constraints, no dependency documentation. Output schemas are undocumented. Error handling is absent, no recovery guidance, no categorization. Security concerns: connector_id pattern exposes internal IDs; no evidence of secret injection or permission gates. Composition is sound (single responsibility per tool), but missing pagination, result limits, and chaining IDs in responses.
Make HTTP requests to REST APIs
Create a new GitHub issue
Execute a command on a remote server via SSH
Retrieve a GitHub issue
Read data from Google Sheets
Retrieve an object from AWS S3
List files in Google Drive
List objects in AWS S3 buckets
Parameter descriptions are minimal (avg ~20 chars) and lack actionable constraints. 'SQL query to execute' does not explain format, limits, or safety. 'Slack channel name or ID' does not clarify which is preferred or how to resolve ambiguity.
No output schemas documented. LLMs cannot predict response structure, forcing them to guess field names for downstream tool calls. E.g., does query_database return {rows: [], count: int} or {data: [], total: int}?
No enum constraints on constrained parameters. call_rest_api.method is enum [GET, POST, PUT, DELETE, PATCH] but other tools lack similar constraints. E.g., query_redis.command accepts any string, should enumerate valid Redis commands or document format.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 48 | 2026-07-28+ | v2 |
Publish messages to Kafka topics
Publish messages to MQTT topics
Upload an object to AWS S3
Execute queries against Google BigQuery
Execute SQL queries against configured database connectors (PostgreSQL, MySQL, Oracle, SQL Server, Snowflake, etc.)
Query Elasticsearch indices
Query LDAP directory services
Execute commands against Redis
Query vector databases (Pinecone, Qdrant, Milvus, LanceDB, Chroma)
Read emails from IMAP mailbox
Send an email via configured email provider
Send a message to a Slack channel
Subscribe to MQTT topics and receive messages
Write data to Google Sheets
No error handling guidance. Tools return success/failure but do not tell LLMs what to do next. E.g., if query_database fails, should the agent retry, ask the user, or try a different connector? No recovery paths documented.
Security: connector_id pattern exposes internal database IDs as required parameters. No evidence of secret injection, permission gates, or audit logging. Agents can invoke destructive tools (execute_ssh_command, query_database with DROP) without permission checks.