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tigergraph-mcp

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by tigergraph
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Python
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    TDQS

    A3.5/5.0

    Scored across 69 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes, with singular/plural pairs (add_node/add_nodes, get_edge/get_edges) and lifecycle operations well separated. However, get_edges and get_node_edges overlap heavily—both retrieve edges from a source vertex—and the schema trio (get_global_schema, get_graph_schema, show_graph_details) requires careful reading to distinguish.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern (add_node, get_edges, drop_query, list_graphs). Even meta tools like discover_tools and get_workflow fit the pattern. There is no mixing of conventions or vague generic verbs.

    Tool Count1/5

    69 tools is an extreme count, well above the 50+ threshold for over-fragmentation. Even though TigerGraph is a complex platform, the tool surface is bloated with many near-duplicate variants (singular/plural pairs, multiple vector loading methods) that will overwhelm agents and make selection harder.

    Completeness4/5

    The surface is remarkably comprehensive, covering schema management, node/edge CRUD, query lifecycle (interpreted, installed, described), loading jobs, data sources, vector search, and admin via gsql. Minor gaps exist, such as a dedicated list_queries tool or conditional bulk edge deletion, but agents can work around these with show_graph_details or gsql.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources