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VeritasGraph

VeritasGraph

by bibinprathap
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

veritasgraph_import_graph

Import a pre-built knowledge graph from Graphify, Understand-Anything, AI Atlas, or generic node/edge JSON while tagging provenance to keep curated content distinguishable.

Instructions

Import a pre-built knowledge graph produced by another tool (Graphify graph.json, Understand-Anything KnowledgeGraph, AI Atlas taxonomy JSON, or any node/edge JSON). Imported nodes/edges are tagged with source_type provenance (curated/extracted/inferred) and their original id/version so curated content stays distinguishable.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "graph": "<graph>"
    },
    "name": "veritasgraph_import_graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
graphYesRaw uploaded graph JSON (nodes + edges).
titleNoOptional display title for the imported graph.
formatNoauto | graphify | understand_anything | ai_atlas | generic.
source_typeNoProvenance: curated | extracted | inferred (default curated).
merge_strategyNopreserve_curated | overwrite | skip_existing.
origin_versionNoOptional source version/commit to preserve.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.0

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden, and it does disclose real behavior: imported nodes/edges get source_type provenance tagging and retain original id/version so curated content stays distinguishable. However, it says nothing about conflict handling, idempotency, whether existing curated nodes can be clobbered, size limits, or what the call returns — all material for a bulk mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with the accepted-input enumeration front-loaded and the provenance guarantee second; no filler. The parenthetical format list is dense but every item carries information an agent would otherwise have to guess at.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter bulk-mutation tool with a nested graph object, no annotations, and no output schema, the description covers inputs and provenance but omits outcome semantics: what is returned, how merge_strategy affects existing nodes, and whether the operation is reversible. Adequate to attempt the call, not adequate to predict its effect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description earns above that by mapping the terse format tokens to actual products (graphify, understand_anything, ai_atlas) and explaining the purpose of source_type provenance. It still leaves merge_strategy's three modes and origin_version's effect unexplained, so the coverage is partial rather than complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Import a pre-built knowledge graph') and enumerates the concrete producer formats accepted (Graphify graph.json, Understand-Anything KnowledgeGraph, AI Atlas taxonomy JSON, generic node/edge JSON). That is far more specific than a restatement of the name. It stops short of a 5 because it never names or contrasts the closest sibling, veritasgraph_ingest_document, leaving the agent to infer the graph-vs-document split.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by 'produced by another tool' and the format list, so an agent can tell this is for bringing in externally generated graphs. There is no explicit when-to-use/when-not guidance, no mention of prerequisites, and no statement about what happens if the graph already exists or how merge_strategy should be chosen.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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