Pharma Intel

Pharma Safety Report

pharma_safety_report

Read-onlyIdempotent

Check adverse event frequency, severity patterns, and contraindications for a drug. Returns safety profiles, risk data, and recall history. E.g., search "aspirin".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`drug_name`	Yes	Drug name (brand or generic)

Output Schema

TableJSON Schema

Name	Required	Description
`recalls`	Yes	Drug recalls data
`analysis`	Yes	Analysis type identifier
`drug_name`	Yes	Drug name queried
`top_reactions`	Yes	Top adverse reactions by frequency
`adverse_events`	Yes	FDA adverse events data
`drug_interactions`	Yes	Known drug interactions

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedMay 8, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "adverse_events": {
+      "description": "FDA adverse events data",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "analysis": {
+      "description": "Analysis type identifier",
+      "enum": [
+        "safety_report"
+      ],
+      "type": "string"
+    },
+    "drug_interactions": {
+      "description": "Known drug interactions",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "drug_name": {
+      "description": "Drug name queried",
+      "type": "string"
+    },
+    "recalls": {
+      "description": "Drug recalls data",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "top_reactions": {
+      "description": "Top adverse reactions by frequency",
+      "type": [
+        "object",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "analysis",
+    "drug_name",
+    "adverse_events",
+    "top_reactions",
+    "recalls",
+    "drug_interactions"
+  ],
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026

addedInput schema / examples

Added value: +[
+  {
+    "drug_name": "aspirin"
+  },
+  {
+    "drug_name": "warfarin"
+  }
+]

First observedApr 22, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already indicate idempotent, read-only, non-destructive behavior. The description adds that it returns safety profiles, risk data, and recall history, which supplements but does not significantly extend beyond what annotations convey.

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?

Two concise sentences with an example. Front-loaded with the core action and returns. No superfluous text.

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

Completeness5/5

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

Given the tool's simplicity (1 param, output schema present), the description covers purpose, returns, and provides an example. It is fully adequate for an agent to understand and invoke the tool.

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?

Schema coverage is 100% with a clear description for 'drug_name'. The description reinforces with an example but adds no extra semantic nuance beyond the schema.

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 tool's purpose: checking adverse event frequency, severity patterns, and contraindications for a drug. It uses a specific verb ('check') and resource ('adverse events') and distinguishes itself from siblings like 'pharma_drug_profile' by focusing on safety data.

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

Usage Guidelines3/5

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

The description implies usage for safety-related queries about a drug but does not explicitly mention when to avoid using it or provide alternatives among siblings. The example hints at common use but no direct guidance.

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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TDQS

A4.1/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose, even within the same domain (e.g., ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta are differentiated by groundedness/beta status; polymarket_edges vs polymarket_arbitrage vs polymarket_fill_risk each target discovery vs arbitrage vs execution risk). The descriptions are highly detailed, eliminating ambiguity about when to use each.

Naming Consistency4/5

All tool names use snake_case consistently, and most follow a verb-first pattern (ask_, compare_, discover_, search_, validate_), but a few are noun-first (entity_profile, polymarket_edges, recent_alerts). The style is readable and predictable, though not perfectly uniform in the verb_noun convention.

Tool Count2/5

With 37 tools, the server is heavily over-scoped, especially given the 'Pharma Intel' name that suggests a focused pharma domain. Many tools are general-purpose (prediction markets, memory, subscription management, feedback) unrelated to the server's apparent purpose, making it feel like a grab bag rather than a cohesive set.

Completeness4/5

The pharma-specific tools cover drug profiles, safety, pipeline scans, catalysts, indication landscapes, and sponsor diligence – a solid lifecycle coverage. The broader data/query/prediction-market tools also feel complete for their respective sub-domains. The only minor gaps are niche operations (e.g., updating a subscription), but these are not critical to the core workflows.

Resources

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