MCP-based Knowledge Graph Construction System (turambar928/MCP_based_KG_construction) is an MCP server listed on the M8ven Trust Index. It scores 32 out of 100, grade F. It declares 7 tools. No publisher has claimed this listing.

Warning

32/100

MCP-based Knowledge Graph Construction System

Item: MCP-based Knowledge Graph Construction System
Rating: 32
Author: M8ven

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

→ turambar928/MCP_based_KG_construction · listed on glama

Warning. Serious findings were identified. Review the full report before connecting. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

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Who stands behind it

turambar928

Source: Glama

Is this your MCP?

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Install from

Glamanot scored here

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings

🚨

Hardcoded credentials detected

15 live-looking API keys in source: 15 OpenAI API key

🔐

You'll be asked for 1 credential: OPENAI_API_KEY

These are read from process.env at runtime. Make sure you trust where they’ll be sent.

// tools this server exposes5 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

handle_call_tool

处理工具调用

build_knowledge_graph

全自动构建知识图谱：自动评估数据质量、补全知识、构建图谱并生成可视化

analyze_knowledge_graph

高级知识图谱分析：全局分析+细节分析，提供质量评估和改进建议

build_and_analyze_kg

构建知识图谱并进行高级分析：结合构建和分析功能的一体化工具，支持自动增强

process_text_file_to_cypher

批量处理文本文件，提取三元组并生成 Neo4j Cypher 脚本

// environment variables

To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.

🔐 secretOPENAI_API_KEY

configKG_MAX_TEXT_LEN

configKG_BUILD_TIMEOUT

configKG_RUN_EVAL

configOPENAI_BASE_URL

configOPENAI_MODEL

configBASE_URL

configAPI_BENCH_N_DOCS

configAPI_BENCH_MODEL

configMODEL

// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

7/7 tools missing one or more hints — handle_call_tool (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); build_knowledge_graph (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); handle_call_tool (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +4 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

License file

No license file

Add a LICENSE file (MIT, Apache-2.0, etc.).

No hardcoded API keys

15 live-looking API keys in source: 15 OpenAI API key

Move secrets to environment variables (process.env.X) or your secret manager.

Tool test coverage

Only 2/7 tools referenced in tests (29%)

Write tests that reference each tool by name so every tool has at least one test.

Secrets not logged

2 secret values sent to print

Redact or omit secret values from log output.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail

The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.

// improvement guidance — verified publishers only

We have 6 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.

// embed badge in your README

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commit: 2920f35132c23ab33cccc1fa6cda8930a9d18485

code hash: 0825810f0f9bf5d41c55a2220e36ee91fab7a82aafd5c122264e418eea19aad0

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