cdisc-mcp
The CDISC MCP server bridges AI assistants and Python tools to the CDISC Library API, enabling queries across clinical data standards (SDTM, ADaM, CDASH, and Controlled Terminology) via 12 structured tools.
Discover & Search
List all available CDISC standards and their published versions (
list_products)Keyword search across all CDISC standards, e.g., "adverse event" or "AEDECOD" (
search_cdisc)
SDTM (Study Data Tabulation Model)
List all domains for a specific SDTM-IG version (
get_sdtm_domains)Get all variables within an SDTM domain (
get_sdtm_domain_variables)Retrieve the full definition of a specific SDTM variable (
get_sdtm_variable)
ADaM (Analysis Data Model)
List all data structures for a given ADaM-IG version (
get_adam_datastructures)Get the definition of a specific ADaM variable (
get_adam_variable)
CDASH (Clinical Data Acquisition Standards Harmonization)
List all domains for a given CDASH-IG version (
get_cdash_domains)Get all data collection fields for a CDASH domain (
get_cdash_domain_fields)
Controlled Terminology (CT)
List all available CT packages with release dates (
list_ct_packages)Retrieve a specific CT codelist definition and metadata (
get_codelist)List all valid terms within a CT codelist, up to 100 at a time (
get_codelist_terms)
Note: Use dashes instead of dots for version numbers (e.g.,
3-4not3.4).
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@cdisc-mcpList the variables for the SDTM DM domain in version 3.4"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CDISC Library MCP Server โ Query clinical data standards (SDTM, ADaM, CDASH, CT) directly from AI assistants.
๐ Translations: ไธญๆ README ยท ๆฅๆฌ่ช README
What is This?
The CDISC MCP Server connects AI assistants (Claude, VS Code Copilot, Cursor, etc.) to the CDISC Library REST API, exposing 11 structured tools for querying clinical trial data standards. Ask your AI assistant questions like:
"What variables are in the SDTM AE domain?"/m "Show me the ADSL variables in ADaM IG 1.3" "List all available Controlled Terminology packages"
For full setup instructions, see the User Manual โ. See Examples โ for real conversation samples.
Related MCP server: snowstorm-mcp-server
Quick Start
0 ยท One-Line AI Install
Have an AI assistant install everything for you:
curl -fsSL https://raw.githubusercontent.com/Teninq/cdisc-mcp/main/install.mdPaste the output into Claude, Copilot, or any AI chat โ it will read the guide and walk you through the full setup interactively.
1 ยท Get a CDISC Library API Key
Register at https://library.cdisc.org and obtain a personal API key.
2 ยท Install
# Runtime only
pip install -e .
# With dev dependencies
pip install -e ".[dev]"
# With web explorer
pip install -e ".[web]"3 ยท Set API Key
# Linux / macOS
export CDISC_API_KEY=your_key_here
# Windows โ Command Prompt
set CDISC_API_KEY=your_key_here
# Windows โ PowerShell
$env:CDISC_API_KEY = "your_key_here"4 ยท Run
# Start MCP server (for AI assistant integration)
cdisc-mcp
# OR: Start Web Explorer (quick interactive testing)
python web/app.pyWeb Explorer โ Quick Interactive Testing
The fastest way to verify your setup and explore tools without any AI client.
# 1. Install web dependencies
pip install -e ".[web]"
# 2. Set your API key
export CDISC_API_KEY=your_key_here # Linux/macOS
set CDISC_API_KEY=your_key_here # Windows CMD
$env:CDISC_API_KEY = "your_key_here" # Windows PowerShell
# 3. Start the bridge server
python web/app.py
# 4. Open in browser
# โ http://localhost:8080The explorer provides:
Sidebar navigation โ all 11 tools organized by standard (SDTM / ADaM / CDASH / Terminology)
Auto-generated forms โ dropdowns for versions and domains, text inputs for variables
Live JSON responses โ syntax-highlighted, copyable output with response time
Bridge status indicator โ confirms your API key and connectivity
Tip: Use version strings with dashes โ
3-4not3.4,1-3not1.3. Example: SDTM-IG3-4, ADaM-IG1-3, CDASH-IG2-0
Available Tools
# | Tool | Standard | Description |
1 |
| โ | List all available CDISC standards and published versions |
2 |
| SDTM | List all datasets in a SDTM-IG version |
3 |
| SDTM | List all variables in an SDTM domain/dataset |
4 |
| SDTM | Get full definition of a specific SDTM variable |
5 |
| ADaM | List all data structures in an ADaM-IG version |
6 |
| ADaM | Get definition of a specific ADaM variable |
7 |
| CDASH | List all domains in a CDASH-IG version |
8 |
| CDASH | Get all data collection fields for a CDASH domain |
9 |
| CT | List all available Controlled Terminology packages |
10 |
| CT | Get definition and metadata of a CT codelist |
11 |
| CT | List all valid terms in a CT codelist |
Version Reference
Standard | Available Versions (use dashes) |
SDTM-IG |
|
ADaM-IG |
|
CDASH-IG |
|
Connect to an AI Assistant
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"cdisc": {
"command": "cdisc-mcp",
"env": {
"CDISC_API_KEY": "your_key_here"
}
}
}
}VS Code / Cursor
Add to .vscode/mcp.json or equivalent MCP config:
{
"servers": {
"cdisc": {
"command": "cdisc-mcp",
"env": {
"CDISC_API_KEY": "your_key_here"
}
}
}
}Claude Code (CLI)
Claude Code supports MCP servers at two scopes: user (global, all projects) and project (local, current project only).
Option A โ CLI command (recommended)
# Add globally (available in all projects)
claude mcp add cdisc-mcp -e CDISC_API_KEY=your_key_here -- python -m cdisc_mcp.server
# Add for the current project only
claude mcp add cdisc-mcp --scope project -e CDISC_API_KEY=your_key_here -- python -m cdisc_mcp.server
# Verify the server was registered
claude mcp listOption B โ Edit ~/.claude.json directly
{
"mcpServers": {
"cdisc-mcp": {
"command": "python",
"args": ["-m", "cdisc_mcp.server"],
"env": {
"CDISC_API_KEY": "your_key_here"
}
}
}
}Tip: If
CDISC_API_KEYis already in your system environment, omit theenvblock entirely โ Claude Code inherits it automatically.
Once registered, confirm with /mcp in any Claude Code session, then use the tools conversationally:
User: What SDTM domains are defined in version 3.4?
Claude: [calls get_sdtm_domains with version="3-4"] ...Using in Your Own Python Tools
You can call CDISC MCP tools programmatically from any Python script using the official MCP client SDK.
Install the client
pip install mcpMinimal example
import asyncio
import os
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
SERVER = StdioServerParameters(
command="python",
args=["-m", "cdisc_mcp.server"],
env={"CDISC_API_KEY": os.environ["CDISC_API_KEY"]},
)
async def main():
async with stdio_client(SERVER) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
# List all available tools
tools = await session.list_tools()
print([t.name for t in tools.tools])
# Call a tool
result = await session.call_tool(
"get_sdtm_domains",
arguments={"version": "3-4"},
)
print(result.content[0].text)
asyncio.run(main())Reusable helper
import asyncio, os, json
from contextlib import asynccontextmanager
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
_SERVER = StdioServerParameters(
command="python",
args=["-m", "cdisc_mcp.server"],
env={"CDISC_API_KEY": os.environ["CDISC_API_KEY"]},
)
@asynccontextmanager
async def cdisc_session():
"""Async context manager that yields an initialised MCP session."""
async with stdio_client(_SERVER) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
yield session
async def call(tool: str, **kwargs) -> dict:
async with cdisc_session() as s:
result = await s.call_tool(tool, arguments=kwargs)
return json.loads(result.content[0].text)
# --- Usage examples ---
async def demo():
# Fetch SDTM domains
domains = await call("get_sdtm_domains", version="3-4")
# Fetch all variables in the AE domain
variables = await call("get_sdtm_domain_variables", version="3-4", domain="AE")
# Look up a single variable
aeterm = await call("get_sdtm_variable", version="3-4", domain="AE", variable="AETERM")
print(aeterm)
asyncio.run(demo())Available tool signatures (quick reference)
# Products / versions
await session.call_tool("list_products", {})
# SDTM
await session.call_tool("get_sdtm_domains", {"version": "3-4"})
await session.call_tool("get_sdtm_domain_variables", {"version": "3-4", "domain": "AE"})
await session.call_tool("get_sdtm_variable", {"version": "3-4", "domain": "AE", "variable": "AETERM"})
# ADaM
await session.call_tool("get_adam_datastructures", {"version": "1-3"})
await session.call_tool("get_adam_variable", {"version": "1-3", "data_structure": "ADSL", "variable": "USUBJID"})
# CDASH
await session.call_tool("get_cdash_domains", {"version": "2-0"})
await session.call_tool("get_cdash_domain_fields", {"version": "2-0", "domain": "AE"})
# Controlled Terminology
await session.call_tool("list_ct_packages", {})
await session.call_tool("get_codelist", {"package_id": "sdtmct-2024-03-29", "codelist_id": "C66781"})
await session.call_tool("get_codelist_terms", {"package_id": "sdtmct-2024-03-29", "codelist_id": "AGEU"})Version format: Always use dashes, not dots โ
"3-4"not"3.4".
Architecture
MCP Client (Claude / VS Code / Cursor)
โ
โ MCP protocol (stdio)
โผ
server.py โโโโ FastMCP tool registration
โ
โผ
tools/ โโโโโโโโ domain functions (sdtm, adam, cdash, terminology, search)
โ
โผ
client.py โโโโโ CDISCClient (async HTTP ยท TTL cache ยท retry)
โ
โ HTTPS
โผ
library.cdisc.org/api โโโโ CDISC Library REST APIKey design decisions:
CDISCClientis a singleton async HTTP client with 1-hour TTL in-memory cacheOnly
429and5xxresponses are retried;4xxraise immediatelyTool functions are pure async โ independently testable without patching
format_response()strips HAL_linksmetadata, extracts structured data for LLM consumption
Development
Development workflow (devel-first, branch & merge SOP): See Contributing Guide.
Running Tests
# Full suite with coverage (โฅ80% required)
pytest
# Specific modules
pytest tests/test_tools.py tests/test_client.py -v
# Single test
pytest tests/test_tools.py::test_list_products -vCode Quality
ruff check src/ tests/ # Linting
mypy src/ # Type checkingGitHub Branch Protection (Required Checks)
Configure main branch protection to require:
CI Tests / testsCI Lint / lintCI Types / types
Project Structure
src/cdisc_mcp/
โโโ server.py # FastMCP server + tool registration
โโโ client.py # Async HTTP client (cache, retry)
โโโ config.py # Config dataclass + env loader
โโโ errors.py # AuthenticationError, ResourceNotFoundError, RateLimitError
โโโ response_formatter.py # HAL response normalization
โโโ tools/
โโโ search.py # list_products (product catalog)
โโโ sdtm.py # SDTM domain/variable tools
โโโ adam.py # ADaM datastructure/variable tools
โโโ cdash.py # CDASH domain/field tools
โโโ terminology.py # CT package/codelist tools
โโโ _validators.py # Path traversal guards
web/
โโโ app.py # FastAPI bridge server
โโโ index.html # Single-file browser explorer
tests/
โโโ test_config.py
โโโ test_client.py
โโโ test_response_formatter.py
โโโ test_tools.py
โโโ test_errors.py
โโโ test_server.pyLicense
MIT license.
Built for clinical data professionals working with CDISC standards.
User Manual ยท Examples ยท CDISC Library ยท API Docs
Available Tools
12 toolsget_adam_datastructuresB
List all ADaM data structures for a given version.
Args: version: ADaM version, e.g. "1.3", "2.1"
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool lists data structures but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or what the output format entails. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a concise parameter explanation. Every sentence adds value without redundancy, making it efficiently structured and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description doesn't need to explain return values. It covers the purpose and parameter semantics adequately, but lacks behavioral details and usage guidelines, which are notable gaps despite the schema support.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'version' by providing examples ('e.g. "1.3", "2.1"'), which compensates for the 0% schema description coverage. This clarifies the expected format beyond the basic type in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'all ADaM data structures', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_adam_variable' or 'get_sdtm_domains', which might handle related but different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions a version parameter but doesn't specify prerequisites, exclusions, or compare it to sibling tools such as 'get_adam_variable' or 'search_cdisc', leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_adam_variableB
Get the definition of a specific ADaM variable.
Args: version: ADaM version, e.g. "1.3" data_structure: Data structure name, e.g. "ADSL", "ADAE" variable: Variable name, e.g. "USUBJID", "AVAL"
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes | ||
| data_structure | Yes | ||
| variable | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a definition, implying a read-only operation, but does not address potential errors (e.g., invalid inputs), rate limits, authentication needs, or the format of the returned definition. This leaves significant gaps for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the purpose stated clearly in the first sentence. The parameter explanations are concise and use examples efficiently. However, the structure could be slightly improved by integrating usage context or behavioral details more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 required parameters) and the presence of an output schema, the description is reasonably complete. It explains the parameters well and relies on the output schema for return values. However, it lacks behavioral context (e.g., error handling) and usage guidelines, which are minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter with examples (e.g., 'version: ADaM version, e.g. "1.3"'), clarifying their roles and expected formats. This compensates well for the schema's lack of documentation, though it could benefit from more detail on constraints or valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the definition of a specific ADaM variable.' It specifies the verb ('Get') and resource ('ADaM variable'), making the function unambiguous. However, it does not explicitly differentiate this tool from its siblings (e.g., 'get_sdtm_variable'), which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lacks context about prerequisites, such as needing to know the ADaM version and data structure beforehand, and does not mention sibling tools like 'get_adam_datastructures' or 'get_sdtm_variable' for related tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cdash_domain_fieldsB
Get all data collection fields for a CDASH domain.
Args: version: CDASH version, e.g. "2.0" domain: Domain code, e.g. "DM", "AE", "VS"
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes | ||
| domain | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] all data collection fields,' implying a read-only operation, but does not cover aspects like error handling, rate limits, authentication needs, or response format. For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the purpose stated clearly in the first sentence. The parameter explanations are brief but informative, avoiding unnecessary details. However, the structure could be slightly improved by integrating the parameter info more seamlessly, rather than as a separate 'Args:' section.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 required parameters) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and parameter semantics adequately. However, it lacks behavioral details (e.g., error cases) and usage guidelines, which are minor gaps in an otherwise sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'version' is a 'CDASH version, e.g. "2.0"' and 'domain' is a 'Domain code, e.g. "DM", "AE", "VS"', providing concrete examples and context. This compensates well for the schema's lack of descriptions, though it could be more detailed (e.g., explaining valid ranges).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get all data collection fields for a CDASH domain.' It specifies the verb ('Get') and resource ('data collection fields for a CDASH domain'), making it easy to understand. However, it does not explicitly differentiate from siblings like 'get_cdash_domains' (which likely lists domains) or 'get_sdtm_domain_variables' (which is for SDTM, not CDASH), so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention siblings such as 'get_cdash_domains' (for listing domains) or 'get_sdtm_domain_variables' (for SDTM data), leaving the agent to infer usage based on context alone. This lack of explicit direction reduces its effectiveness in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cdash_domainsB
List all CDASH domains for a given version.
Args: version: CDASH version, e.g. "2.0", "1.1"
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists domains, implying a read-only operation, but does not cover critical aspects such as authentication requirements, rate limits, error handling, or pagination. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear purpose statement followed by parameter details in a separate 'Args' section. Every sentence earns its place by providing essential information without redundancy. It is appropriately sized for the tool's complexity and front-loaded with the main functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which handles return values), one parameter with low schema coverage, and no annotations, the description is reasonably complete. It explains the purpose and parameter semantics effectively. However, it lacks usage guidelines and behavioral details, which are minor gaps given the output schema reduces the need for return value explanation. Overall, it provides a solid foundation but could be more comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaningful context by explaining the 'version' parameter as 'CDASH version, e.g. "2.0", "1.1"', which clarifies the expected format and provides examples. This goes beyond the bare schema, effectively documenting the single required parameter. Since there is only one parameter, the description adequately covers its semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all CDASH domains for a given version.' It specifies the verb ('List'), resource ('CDASH domains'), and scope ('for a given version'), which is clear and specific. However, it does not explicitly differentiate from sibling tools like 'get_cdash_domain_fields' or 'get_sdtm_domains', which might have overlapping or related functions, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools such as 'get_cdash_domain_fields' (which might list fields within domains) or 'get_sdtm_domains' (for a different standard), nor does it specify prerequisites or exclusions. The only implied context is the need for a version parameter, but this is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_codelistA
Get a specific Controlled Terminology codelist definition.
Args: package_id: CT package identifier, e.g. "sdtmct-2024-03-29" codelist_id: Codelist concept ID or submission value, e.g. "C66781", "AGEU"
| Name | Required | Description | Default |
|---|---|---|---|
| package_id | Yes | ||
| codelist_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a definition (implying a read-only operation) but does not mention authentication needs, rate limits, error conditions, or the format of the returned definition. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a structured 'Args:' section with clear parameter explanations and examples. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which handles return values), 2 parameters with good semantic clarification in the description, and no complex nested structures, the description is largely complete. However, the lack of behavioral details (e.g., error handling) and explicit usage guidelines relative to siblings slightly reduces completeness for optimal agent operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'package_id' is a 'CT package identifier' and 'codelist_id' is a 'Codelist concept ID or submission value', providing examples like 'sdtmct-2024-03-29' and 'C66781', 'AGEU'. This clarifies the semantics and expected formats, compensating well for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'Controlled Terminology codelist definition', making the purpose specific and unambiguous. It distinguishes this tool from siblings like 'get_codelist_terms' (which retrieves terms within a codelist) and 'list_ct_packages' (which lists packages rather than retrieving a specific codelist).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying what it retrieves (a codelist definition), but does not explicitly state when to use this tool versus alternatives like 'get_codelist_terms' or 'search_cdisc'. No guidance is provided on prerequisites or exclusions, leaving the agent to infer context from the tool name and parameter examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_codelist_termsB
List all valid terms in a CT codelist (max 100 shown, check has_more).
Args: package_id: CT package identifier codelist_id: Codelist concept ID or submission value
| Name | Required | Description | Default |
|---|---|---|---|
| package_id | Yes | ||
| codelist_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it lists terms, has a max limit of 100 shown, and requires checking 'has_more' for pagination. However, it lacks details on permissions, rate limits, error handling, or what 'has_more' entails, leaving gaps in transparency for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the main purpose stated first followed by parameter details. Every sentence adds value, but the structure could be slightly improved by integrating the parameter explanations more seamlessly or adding bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with pagination), no annotations, and an output schema exists (which likely covers return values), the description is moderately complete. It covers the purpose, parameters, and key behavior (limit, has_more), but lacks context on errors, authentication, or how to interpret results beyond the output schema, leaving room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaning by explaining that 'package_id' is a 'CT package identifier' and 'codelist_id' is a 'Codelist concept ID or submission value', clarifying the semantics beyond the bare schema. Since there are only 2 parameters and the description covers both adequately, it scores well but not perfectly due to lack of format or example details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'valid terms in a CT codelist', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_codelist' or 'search_cdisc', which might have overlapping functionality, so it lacks sibling differentiation for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_codelist' or 'search_cdisc'. It mentions a limit of 100 terms and checking 'has_more', which implies usage for pagination, but does not specify contexts, prerequisites, or exclusions, leaving the agent without clear direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sdtm_domainsA
List all SDTM domains for a given version.
Args: version: SDTM-IG version, e.g. "3.4", "3.3". Use list_products() first.
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('List') but doesn't mention other traits like rate limits, error handling, or response format. While it hints at a dependency on 'list_products()', it lacks details on permissions or data sensitivity, leaving significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured: the first sentence states the purpose, and the 'Args' section efficiently explains the parameter with an example and prerequisite. Every sentence adds value without redundancy, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers purpose, parameter semantics, and usage guidance. However, with no annotations, it could benefit from more behavioral context (e.g., read-only nature, potential errors), slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by explaining that 'version' is an 'SDTM-IG version' with examples ('3.4', '3.3'), which clarifies beyond the schema's generic string type. However, it doesn't detail all possible values or constraints, preventing a perfect score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all SDTM domains for a given version.' It specifies the verb ('List'), resource ('SDTM domains'), and scope ('for a given version'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_sdtm_domain_variables' or 'get_cdash_domains', which prevents a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance by stating to 'Use list_products() first' to determine valid versions, which helps the agent understand prerequisites. It implies when to use this tool (for SDTM domains) but doesn't explicitly contrast with alternatives like 'get_cdash_domains' or specify when not to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sdtm_domain_variablesB
Get all variables defined in an SDTM domain.
Args: version: SDTM-IG version, e.g. "3.4" domain: Two-letter domain code, e.g. "DM", "AE", "LB"
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes | ||
| domain | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get all variables'), implying a read-only operation, but does not mention potential side effects, authentication needs, rate limits, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and well-structured, with a clear main sentence followed by bullet-pointed parameter explanations. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 required parameters) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and parameter basics, but could improve by addressing behavioral aspects and sibling differentiation, which holds it back from a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds basic semantics for both parameters by providing examples (e.g., 'SDTM-IG version, e.g. "3.4"' and 'Two-letter domain code, e.g. "DM", "AE", "LB"'), which clarifies their expected formats. However, with 0% schema description coverage, it does not fully compensate by explaining constraints like valid version ranges or domain codes, keeping it at the baseline for partial enhancement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('all variables defined in an SDTM domain'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'get_sdtm_variable' or 'get_sdtm_domains', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as how it differs from 'get_sdtm_variable' (which might fetch a single variable) or 'get_sdtm_domains' (which lists domains). Without any context on prerequisites, exclusions, or comparisons to siblings, usage is implied but not clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sdtm_variableB
Get the full definition of a specific SDTM variable.
Args: version: SDTM-IG version, e.g. "3.4" domain: Domain code, e.g. "AE" variable: Variable name, e.g. "AETERM", "AEDECOD"
| Name | Required | Description | Default |
|---|---|---|---|
| version | Yes | ||
| domain | Yes | ||
| variable | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read-only operation, it doesn't specify whether this requires authentication, has rate limits, returns structured data, or handles errors. For a tool with no annotation coverage, this leaves significant behavioral aspects unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by a well-formatted parameter explanation. Every sentence earns its place by adding value, and it's front-loaded with the core functionality. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 required parameters), 0% schema description coverage, and the presence of an output schema (which handles return values), the description does a good job of explaining the tool's purpose and parameters. However, it lacks behavioral context (e.g., error handling) and usage guidelines relative to siblings, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains what each parameter represents (e.g., 'SDTM-IG version', 'Domain code', 'Variable name') and provides concrete examples (e.g., '3.4', 'AE', 'AETERM'), effectively compensating for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('full definition of a specific SDTM variable'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_sdtm_domain_variables' or 'get_adam_variable', which might provide related but different information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_sdtm_domain_variables' (which might list variables for a domain) or 'get_adam_variable' (which handles a different standard). It only describes what the tool does, not when it's the appropriate choice among similar siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_ct_packagesA
List all available CDISC Controlled Terminology packages with release dates.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It implies a read-only operation ('List') but doesn't specify details like pagination, rate limits, authentication needs, or error handling. The mention of 'release dates' adds some context about output content, but overall behavioral traits are minimally covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function and key output detail ('release dates'). It's front-loaded with the core purpose and contains no wasted words, making it optimally concise for a zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (zero parameters, output schema exists), the description is reasonably complete. It covers the purpose and hints at output content, though it could benefit from more behavioral context (e.g., data format or limitations). The existence of an output schema reduces the need for detailed return value explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema description coverage is 100%, so there's no need for parameter explanation in the description. The description appropriately focuses on the tool's purpose without redundant parameter details, earning a high baseline score for this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('CDISC Controlled Terminology packages'), making the purpose specific and understandable. It adds useful detail about what's included ('release dates'), but doesn't explicitly differentiate from sibling tools like 'list_products' or 'search_cdisc', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search_cdisc' or 'list_products'. It doesn't mention prerequisites, exclusions, or specific contexts for usage, leaving the agent with minimal direction beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_productsA
List all available CDISC standards and their published versions. Use this first to discover available versions before querying specific content.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation by using 'List' and 'discover', which is consistent with typical listing behavior, but does not disclose details like pagination, rate limits, or authentication needs. The description adds some context about its role in a workflow but lacks comprehensive behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste: the first states the purpose, and the second provides usage guidance. It is front-loaded with the core function and efficiently adds context without unnecessary details, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, output schema exists), the description is complete enough for its purpose. It explains what the tool does and when to use it, which is sufficient for a listing tool. However, without annotations, it could benefit from more behavioral details, but the output schema likely covers return values, reducing the need for further explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose and usage, which aligns with the baseline for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('CDISC standards and their published versions'), making the purpose specific and unambiguous. It distinguishes from siblings like 'list_ct_packages' or 'search_cdisc' by focusing on standards overview rather than specific content or search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides when to use this tool ('Use this first to discover available versions before querying specific content'), offering clear guidance on its role as a discovery step. This helps differentiate it from sibling tools that retrieve detailed content, such as 'get_sdtm_domains' or 'get_codelist'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_cdiscB
Search across all CDISC standards by keyword.
Args: query: Search keyword, e.g. "adverse event", "AEDECOD", "demographics"
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches by keyword but doesn't describe how results are returned (e.g., format, pagination), what happens on no matches, or any limitations (e.g., rate limits, authentication needs). For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a concise 'Args' section with a clear example. Every sentence earns its place by providing essential information without redundancy or fluff, making it easy to scan and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is moderately complete. It covers the basic purpose and parameter semantics but lacks behavioral details and usage guidelines. For a simple search tool, this is adequate but leaves room for improvement in guiding the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema, which has 0% description coverage. It explains the 'query' parameter as a 'Search keyword' and provides examples ('adverse event', 'AEDECOD', 'demographics'), clarifying its purpose and usage. With only one parameter and no schema descriptions, this compensates well, though it doesn't detail constraints like length or special characters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search across all CDISC standards by keyword.' This specifies the verb (search), resource (CDISC standards), and scope (all standards). It distinguishes from siblings like get_codelist or get_sdtm_domains by emphasizing cross-standard search rather than retrieving specific structured data. However, it doesn't explicitly contrast with potential similar search tools (none listed), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over sibling tools like get_codelist (for code lists) or get_sdtm_domains (for specific domain data), nor does it specify prerequisites or exclusions. Usage is implied by the search functionality, but explicit context is lacking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
12 tool updates
v0.1.0- First observed
get_adam_datastructures - First observed
get_adam_variable - First observed
get_cdash_domain_fields - First observed
get_cdash_domains - First observed
get_codelist - First observed
get_codelist_terms - First observed
get_sdtm_domain_variables - First observed
get_sdtm_domains - First observed
get_sdtm_variable - First observed
list_ct_packages - First observed
list_products - First observed
search_cdisc
TDQS
Each tool has a clearly distinct purpose targeting specific CDISC standards (ADaM, CDASH, SDTM, Controlled Terminology) with no overlap. The tools are organized by standard type and operation level (list domains vs. get specific variables), making them easily distinguishable.
All tools follow a consistent verb_noun pattern with clear prefixes indicating the standard (get_adam_, get_cdash_, get_sdtm_, get_codelist_, list_, search_). The naming is highly predictable and follows the same snake_case convention throughout.
12 tools is well-scoped for a CDISC standards lookup server, covering multiple standards (ADaM, CDASH, SDTM, CT) with appropriate granularity. Each tool serves a specific purpose without redundancy, and the count supports comprehensive querying without being overwhelming.
The toolset provides excellent coverage for querying CDISC standards metadata, with list operations for discovery and get operations for detailed information. Minor gaps exist (e.g., no update/create/delete tools, but that's appropriate for a read-only reference server), and the search tool helps bridge any remaining gaps.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Connect AI clients to biomedical data and tools.
NIH Clinical Tables: ICD-10/9, RxTerms, LOINC, NPI, conditions search. Keyless.
Clinical trial search and status from ClinicalTrials.gov
Versioned documentation registry and semantic search for AI tools and coding assistants.
Related MCP Servers
AlicenseAqualityAmaintenanceProvides AI agents with instant access to 10M+ OMOP medical vocabulary concepts for searching, mapping, and navigating clinical codes across SNOMED, ICD-10, RxNorm, LOINC, and more.111166MIT
snowstorm-mcp-serverofficial
AlicenseNot gradedqualityBmaintenanceEnables AI assistants to look up, search, validate, navigate hierarchies, and expand value sets for SNOMED CT clinical terminology through the Model Context Protocol.8Apache 2.0- AlicenseNot gradedqualityDmaintenanceProvides comprehensive access to CDISC clinical data standards (SDTM, ADaM, CDASH, CT) through 15 MCP tools, resources, and prompts.1MIT
- FlicenseBqualityDmaintenanceEnables AI agents to query free biomedical and pharmaceutical APIs for clinical trials, drug data, molecular structures, adverse events, and research literature.14-
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Teninq/cdisc-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server