The server implements 2 tools with basic schemas and descriptions, but falls significantly short of production quality. Tool naming uses action verbs (create_, update_) which is good, but descriptions are minimal (13-15 chars), far below the 194-char baseline. The update_database tool has a critical security flaw: it accepts connectionString as a plain parameter, violating secret injection patterns. Parameter descriptions are present but lack constraint details (e.g., no regex for filePath, no enum for databaseType). Output schemas are undocumented, callers cannot plan downstream operations. Error handling exists but provides only generic McpError messages without recovery guidance. The create_note tool is trivial and doesn't reflect real-world complexity. No pagination, no batch operations, no idempotence guarantees documented.
Credentials exposed as tool parameters: connectionString is passed as a plain string parameter, violating secret-injection pattern. This leaks database credentials into agent logs, traces, and prompt history.
Tool descriptions far below baseline: create_note (13 chars), update_database (15 chars). Baseline is 194 chars. Descriptions must explain WHAT the tool does, WHEN to use it, and any prerequisites. Current descriptions provide no context for LLM tool selection.
Output schemas not documented: Neither tool describes its return structure. LLMs cannot plan downstream operations or extract structured data without knowing what fields are returned.
Parameters lack constraint details: databaseType accepts free-form string instead of enum (PostgreSQL|MySQL|MongoDB|SQLite). filePath has no validation for file existence or path traversal protection.
Recommendations
Rewrite tool descriptions to meet 150-250 char baseline. E.g., create_note: 'Create a new note and store it in memory. Provide a title (string) and content (string). Returns the note ID and confirmation message.' Update_database: 'Load data from a CSV or Excel file and insert it into a database table. Specify the file path, database type, table name, and connection parameters. Supports PostgreSQL, MySQL, MongoDB, and SQLite. Returns the number of rows inserted or an error with remediation steps.'
Move connectionString to server-side configuration or environment variable injection. Do NOT accept it as a parameter. Implement credential vaults (HashiCorp Vault, AWS Secrets Manager) for storing database credentials.
Add enum constraints to databaseType: {enum: ['postgresql', 'mysql', 'mongodb', 'sqlite']} to prevent hallucinated values.
Document output schemas as JSON Schema in each tool's response. E.g., create_note returns {type: 'object', properties: {note_id: {type: 'string'}, title: {type: 'string'}, content: {type: 'string'}}, required: ['note_id', 'title', 'content']}.
Implement actual database updates: Replace console.log placeholder with real DB driver calls (pg for PostgreSQL, mysql2 for MySQL, mongodb for MongoDB, sqlite3 for SQLite). Include transaction support and rollback on error.
Add pagination to update_database: Accept optional limit (default 1000, max 10000) and offset parameters. Return total_rows_processed, rows_inserted, rows_failed, and a next_cursor if more data exists.
Error handling lacks recovery guidance: McpError responses are generic ('Error updating database: [message]'). Should include actionable next steps, e.g., 'File not found at [path]. Verify the file exists and is readable.'
Missing input validation and SQL injection protection: The tool accepts filePath and tableName from user input without sanitization. An attacker could pass ../../../sensitive_file.csv or a table name with SQL metacharacters.
No pagination support: If CSV/Excel file is large, parsing and storing entire result in memory risks OOM. No limit parameter, no streaming response documented.
Placeholder implementation: update_database function does not actually update any database, it logs data and returns success without side effects. This is a mock that will not work in production.
update_database
Enhance error messages with recovery guidance: 'File not found: [filePath]. Ensure the file exists and the path is readable. If using a relative path, verify it's relative to the server's working directory.' / 'Invalid database type: [type]. Must be one of: postgresql, mysql, mongodb, sqlite.'
Add dry-run mode to update_database to support confirmation-request pattern: accept optional 'dry_run: boolean' parameter. When true, parse and validate data without executing INSERT statements, returning a preview of rows to be inserted.
Add per-item error reporting: If parsing or inserting rows fails, return {rows_inserted: 45, rows_failed: 5, failures: [{row_index: 2, error: 'Duplicate key'}, ...]} so the agent knows what succeeded and what didn't.