Loading ToolBench
MCP Server for competitive intelligence monitoring of AI training datasets, tracking labs, vendors, open-source releases across HuggingFace, GitHub, arXiv, blogs, X/Twitter, and Reddit
This MCP server exposes 16 read-only tools for competitive intelligence monitoring. While tool names are action-oriented (radar_*) and schemas are present with enums and constraints, descriptions are in Chinese with minimal English context, parameter descriptions lack depth regarding use cases and prerequisites, and output schemas are completely undocumented. Many parameters have defaults but lack explicit range documentation. No error recovery guidance is visible in the code. The server demonstrates basic competence in schema structure but falls short of production quality in areas critical for LLM tool selection: descriptions are not LLM-optimized (too terse or non-English), parameter relationships are undocumented, and output types are inferred rather than declared.
获取最新博客文章（来自 62+ 个博客源）
查看当前监控配置（监控的组织、关键词等）
获取最新发现的数据集列表
对比两期报告，自动识别新增/消失的数据集、仓库、论文等变化
获取 GitHub 组织的最新活动
查看历史扫描报告时间线，展示各期报告的统计摘要和变化趋势
获取数据集谱系分析：派生关系、版本链、Fork 树和根数据集
Descriptions are primarily in Chinese with minimal English context. LLMs struggle to infer intent when descriptions are not in their primary training language. All 16 tool descriptions should be in English or include substantial English translations.
Output schemas are completely undocumented. Tools return TextContent responses but their internal structure is not declared. LLMs cannot plan downstream tool chains or extract specific fields without knowing the response format. Every tool needs a documented output schema.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 56 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 34 | - | v1 |
获取竞品矩阵：各组织在不同数据类型上的数据集/仓库/论文/博客数量交叉分析
获取组织关系图谱：投资、并购、合作关系可视化
获取最新相关论文
获取 Reddit AI/ML 社区相关帖子（r/MachineLearning, r/LocalLLaMA 等）
运行 AI 数据集竞争情报扫描，监控 HuggingFace、GitHub、arXiv 和博客上的最新动态
跨所有数据源全文搜索（数据集、GitHub、论文、博客、X/Twitter、Reddit），支持关键词和正则
获取最新扫描报告的摘要统计
查询数据集增长趋势：上升最快、突破性增长、指定数据集的历史曲线
查看历史趋势数据：各数据源随时间的数量变化，支持折线图数据输出
Parameter descriptions are too terse or missing context. For example, 'days' in radar_scan has description 'Scanning days (default 7 days)' but does not explain what happens if days=0, if days can be negative, or what the upper bound is. All numeric parameters need explicit ranges.
No error handling or recovery guidance is visible in the code. If a tool fails (e.g., no data found, API timeout), there is no indication of what the LLM should do next. Tools should return actionable error messages with recovery steps.
Parameter relationships and dependencies are undocumented. For example, radar_trend's 'dataset_id' parameter is only valid when mode='dataset', but this constraint is not stated in either parameter description. Undocumented dependencies cause silent misuse.
Tools returning lists (radar_datasets, radar_papers, radar_blogs, radar_reddit, radar_search) accept a 'limit' parameter but it is unclear if results are paginated or truncated. There is no 'offset', 'cursor', or 'next_page_token' mechanism documented, forcing an LLM to retry with different parameters to fetch additional results.