A
licenseNot graded
qualityB
maintenanceEnables AI agents to deploy production APIs from JSON schemas, with 44 tools for managing projects, schemas, deployments, and graph data.
864 PyPI
1
Academic Free v1.1
Structly MCP Server
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TDQS
B3.3/5.0
Scored across 47 tools
Disambiguation4/5
Most tools have clearly distinct purposes following a predictable verb_noun pattern. However, a few pairs like commit_schema vs commit_to_branch and ai_apply_schema vs import_sql_into_schema could cause confusion without careful reading of descriptions. Overall, the high number of tools makes selection slightly harder but still manageable.
Naming Consistency5/5
All tool names use consistent snake_case verb_noun structure, e.g., list_projects, create_table, edit_column, delete_relationship. The verbs are uniformly used: 'list' for collections, 'get' for single items, 'create/edit/delete' for CRUD operations, and domain-specific actions like 'commit', 'merge', 'checkout' are clearly named. No mixed conventions or inconsistent patterns.
Tool Count2/5
With 47 tools, the server is well beyond the 25+ threshold, making it feel heavy and potentially overwhelming for agents. While the domain is broad (project/schema/table management, version control, data sync, AI features), this count exceeds what is typically optimal for a single MCP server. The number could likely be consolidated or split into multiple focused servers.
Completeness5/5
The tool surface is exceptionally comprehensive, covering full CRUD for projects, schemas, tables, columns, groups, and relationships, plus branch/commit management, migration generation, AI-assisted schema design, SQL import/export, and data source sync. There are no obvious dead ends or missing essential operations; even advanced workflows like merging branches and scheduling sync jobs are supported.
Maintenance
ActivitySlowing
ResponsivenessNo issues