ChiCTR MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| HTTP_PROXY | No | HTTP proxy server URL (optional, e.g., http://your-proxy-server:port) | |
| HTTPS_PROXY | No | HTTPS proxy server URL (optional, e.g., http://your-proxy-server:port) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_trialsC | 搜索ChiCTR临床试验。支持按标题关键词、注册号、年份进行搜索，返回试验列表。 |
| get_trial_detailB | 根据注册号查询临床试验的完整详细信息 |
| get_cache_statsB | 获取缓存统计信息，包括搜索缓存和详情缓存的命中率等 |
| clear_cacheB | 清除所有缓存数据 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: clear_cache removes data, get_cache_stats retrieves performance metrics, get_trial_detail fetches a single trial by ID, and search_trials finds multiple trials based on criteria. An agent can easily differentiate these functions.
All tool names follow a consistent verb_noun pattern in snake_case: clear_cache, get_cache_stats, get_trial_detail, and search_trials. This uniformity makes the set predictable and easy to understand.
With 4 tools, the count is reasonable for a ChiCTR trials server, covering core operations like search and detail retrieval. It feels slightly thin but not inadequate, as it supports basic workflows without bloat.
The toolset covers search and detail retrieval for trials, plus cache management, but lacks update or creation tools for trials, which might be expected in a full CRUD lifecycle. This gap could limit agent actions in dynamic scenarios.