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mcp-clinical-doc-agent

mcp-clinical-doc-agent

by Aditya-Khadye
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

extract_entities

Extract clinical entities like drugs, conditions, interventions, endpoints, and populations from trial documents. Optionally filter by document ID to retrieve entities from a specific document or combine all.

Instructions

Extract clinical entities (drugs, conditions, interventions, endpoints, populations).

If ``document_id`` is omitted, runs across every document and returns the combined set.
Each entity carries its source ``document_id`` so callers can group results.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "extract_entities"
  }
}

Input Schema

NameRequiredDescriptionDefault
document_idNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses behavior: extraction, scope control via document_id, and result grouping capability. No destructive implications mentioned, but it is 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences front-loading purpose, then usage detail. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single optional parameter and existence of output schema, the description covers main behavior and result structure. Minor omissions like pagination or limits do not detract significantly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds context about omitting document_id leading to all-document search, but does not explain its type or format. Schema coverage is 0%, so description partially compensates but leaves gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Extract' and the resource 'clinical entities' with explicit types (drugs, conditions, interventions, endpoints, populations). It distinguishes from sibling tools like cluster, list, and summarize.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context on when to omit document_id (run across all documents) and that each entity carries source document_id. Lacks explicit when-not or alternatives but offers sufficient guidance for typical use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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