Pharma Intel

Pharma Drug Profile

pharma_drug_profile

Read-onlyIdempotent

Look up a drug's FDA approval status, dosage forms, interactions, and active trials. Returns approval dates, formulations, known drug interactions, and ongoing trial details. E.g., search "ozempic" or "metformin".

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description
`rxnorm`	Yes
`analysis`	Yes	Analysis type identifier
`drug_name`	Yes	Drug name queried
`fda_labels`	Yes	FDA drug labels
`fda_approvals`	Yes	FDA drug approvals data
`adverse_events`	Yes	FDA adverse events data
`active_clinical_trials`	Yes	Active recruiting clinical trials for the drug

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": {
+    "active_clinical_trials": {
+      "description": "Active recruiting clinical trials for the drug",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "adverse_events": {
+      "description": "FDA adverse events data",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "analysis": {
+      "description": "Analysis type identifier",
+      "enum": [
+        "drug_profile"
+      ],
+      "type": "string"
+    },
+    "drug_name": {
+      "description": "Drug name queried",
+      "type": "string"
+    },
+    "fda_approvals": {
+      "description": "FDA drug approvals data",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "fda_labels": {
+      "description": "FDA drug labels",
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "rxnorm": {
+      "properties": {
+        "interactions": {
+          "description": "RxNorm drug interactions or null",
+          "type": [
+            "object",
+            "null"
+          ]
+        },
+        "properties": {
+          "description": "RxNorm drug properties or null",
+          "type": [
+            "object",
+            "null"
+          ]
+        },
+        "search": {
+          "description": "RxNorm search results or null",
+          "type": [
+            "object",
+            "null"
+          ]
+        }
+      },
+      "type": "object"
+    }
+  },
+  "required": [
+    "analysis",
+    "drug_name",
+    "rxnorm",
+    "fda_approvals",
+    "fda_labels",
+    "adverse_events",
+    "active_clinical_trials"
+  ],
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026

addedInput schema / examples

Added value: +[
+  {
+    "drug_name": "ozempic"
+  },
+  {
+    "drug_name": "metformin"
+  }
+]

First observedApr 22, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds behavioral context by listing the types of data returned (approval dates, formulations, interactions, trial details), which is useful beyond the annotations. No contradictions.

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 sentences with an example, no wasted words. Front-loaded with key actions and data types. Every sentence earns its place.

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 output schema exists, the description does not need to explain return values. It covers the main categories of data (approval, dosage, interactions, trials) and provides examples. It is reasonably complete for a lookup 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%, so the parameter is fully documented in the schema. The description does not add extra meaning beyond the schema's description ('Drug name (brand or generic)'), but the examples provide practical guidance. Baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool looks up FDA approval status, dosage forms, interactions, and active trials, with examples. It specifies the resource (drug) and the verb (look up). However, it does not explicitly differentiate from sibling pharma tools like pharma_pipeline_catalysts or pharma_safety_report, which is a minor gap.

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 looking up drug profiles and provides example queries ('ozempic', 'metformin'), but it does not explicitly state when to use this tool versus alternatives. No when-not-to-use or exclusion criteria are mentioned.

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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