Midgard Finance drug data
Obesity and incretin drug trials: weight loss by dose with its estimand, adverse events, catalysts
我该使用它吗
质量与安全性
发现（1）
- LOWTool 'get_trial' description lacks action verb在 get_trial 中
基于对工具定义和协议合规性的自动分析。
上下文开销
这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。
安装
一键安装
将以下内容添加到你的 `claude_desktop_config.json` 文件中：
{
"mcpServers": {
"drugs": {
"url": "https://mcp.midgardfinance.com/mcp"
}
}
}远程端点
https://mcp.midgardfinance.com/mcpstreamable-http它能做什么
工具清单
工具（7）
🟢search_drugs(query, area, sponsor, status, limit)
Search the drug boards by free text and by field.
输入模式
{
"type": "object",
"properties": {
"query": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Query"
},
"area": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Area"
},
"sponsor": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Sponsor"
},
"status": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Status"
},
"limit": {
"default": 10,
"title": "Limit",
"type": "integer"
}
},
"title": "search_drugsArguments"
}🟢get_drug(name)
Full record for one drug, with its efficacy figures estimand-tagged and its page URL. Matching goes slug, then exact name or brand, then substring, so "wegovy", "Wegovy HD" and "semaglutide-obesity" all land somewhere sensible without the caller knowing the slug scheme.
输入模式
{
"type": "object",
"properties": {
"name": {
"title": "Name",
"type": "string"
}
},
"required": [
"name"
],
"title": "get_drugArguments"
}🟢get_trial(identifier)
One trial's design, by NCT number or by trial key (SURMOUNT-1, scale, redefine-1).
输入模式
{
"type": "object",
"properties": {
"identifier": {
"title": "Identifier",
"type": "string"
}
},
"required": [
"identifier"
],
"title": "get_trialArguments"
}🟢compare_efficacy(drugs, timepoint_weeks, estimand, metric, limit)
The extracted weight-loss table for the named drugs, joined to trial design, one row per arm and timepoint. Rows are NOT collapsed to one number per drug. The corpus holds several doses, timepoints and estimands per trial, and picking a single winner per drug would mean choosing between them here, silently and on the caller's behalf. The comparison is theirs to make; this returns the rows and the labels needed to make it correctly.
输入模式
{
"type": "object",
"properties": {
"drugs": {
"items": {
"type": "string"
},
"title": "Drugs",
"type": "array"
},
"timepoint_weeks": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"title": "Timepoint Weeks"
},
"estimand": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Estimand"
},
"metric": {
"default": "mean_weight_change_pct",
"title": "Metric",
"type": "string"
},
"limit": {
"default": 10,
"title": "Limit",
"type": "integer"
}
},
"required": [
"drugs"
],
"title": "compare_efficacyArguments"
}🟢get_adverse_events(drug, trial, ae_type, limit)
Extracted adverse-event rates for a named drug or trial, by arm, with the arithmetic that was checked. `verification` is carried through verbatim. It is the raw-count recomputation recorded when the row was extracted ("997/2481 active = 40.18%"), and it is what lets a caller see that the percentage was checked against the paper rather than copied from an abstract.
输入模式
{
"type": "object",
"properties": {
"drug": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Drug"
},
"trial": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Trial"
},
"ae_type": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Ae Type"
},
"limit": {
"default": 10,
"title": "Limit",
"type": "integer"
}
},
"title": "get_adverse_eventsArguments"
}🟢get_trajectory(drug)
The digitised weight-loss curve for a drug: week-by-week points, plus per-dose series. Returned with `endpoint_pct` and its estimand alongside the curve, because the endpoint marker and the last plotted point are different quantities and the curve alone does not say which one a reader is looking at.
输入模式
{
"type": "object",
"properties": {
"drug": {
"title": "Drug",
"type": "string"
}
},
"required": [
"drug"
],
"title": "get_trajectoryArguments"
}🟢list_catalysts(from_date, to_date, tier, limit)
Upcoming and recent readouts, approvals and trial starts across sponsors. Dates are ISO strings where the event has a date and a bare year where the sponsor has only guided to one, so they are compared as strings rather than parsed - "2026" sorts before "2026-07-01", which is the right answer for an event guided to a year.
输入模式
{
"type": "object",
"properties": {
"from_date": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "From Date"
},
"to_date": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "To Date"
},
"tier": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"title": "Tier"
},
"limit": {
"default": 10,
"title": "Limit",
"type": "integer"
}
},
"title": "list_catalystsArguments"
}社区
证据