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Glama

Openfda

by io.github.pipeworx-io

Fda Drug Labels

fda_drug_labels
Read-onlyIdempotent

Get FDA drug labeling (SPL) by drug name OR by INDICATION. This is the tool for "what drugs are approved to treat " — it searches the indications_and_usage text, which the drugsfda approvals endpoint does NOT carry. Query by brand/generic name (openfda.brand_name:"HUMIRA"), or by indicated use (indications_and_usage:"rheumatoid arthritis"). Returns indications, boxed/other warnings, dosage, contraindications, and adverse reactions (each text field capped, set_id preserved for out-of-band full-label fetch). Note: FDA label publication trails approval by weeks, so a just-approved drug may not have a label yet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoPagination offset (default 0) — the reported total can exceed one page of 100.
limitNoNumber of results (1-100, default 5)
queryYesOpenFDA search query. Examples: 'openfda.brand_name:"HUMIRA"', 'openfda.generic_name:"adalimumab"'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesTotal count of matching drug labels
resultsYesArray of drug label objects

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / skip
      Added value: +{
+  "description": "Pagination offset (default 0) — the reported total can exceed one page of 100.",
+  "type": "number"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "results": {
+      "description": "Array of drug label objects",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Total count of matching drug labels",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total",
+    "results"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "openfda.brand_name:\"HUMIRA\""
+  },
+  {
+    "limit": 10,
+    "query": "openfda.generic_name:\"adalimumab\""
+  }
+]
  4. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Even with readOnlyHint and openWorldHint present, the description adds real behavioral context: text fields are 'capped', 'set_id preserved for out-of-band full-label fetch', and label publication trails approval by weeks. These are not derivable from annotations and help set accurate expectations.

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?

Four dense sentences with no filler: purpose, differentiation, return fields with caveats, and data lag. The core scope is front-loaded in the first sentence and every subsequent sentence earns its place.

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?

Combined with a fully covered schema, an output schema, and safety annotations, the description covers purpose, query syntax, return contents, field caps, and freshness caveat. Nothing needed for correct invocation is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds a query pattern not shown in schema examples: searching via indications_and_usage:'rheumatoid arthritis'. This gives field-level guidance beyond the schema's generic 'OpenFDA search query' text.

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?

Opens with 'Get FDA drug labeling (SPL) by drug name OR by INDICATION' — a specific verb and resource. It explicitly differentiates from the approvals sibling: 'which the drugsfda approvals endpoint does NOT carry', making its unique role clear among the many FDA tools.

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

Usage Guidelines5/5

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

Positions the tool as 'the tool for what drugs are approved to treat <condition>' and states the approvals endpoint lacks indications_and_usage, providing an explicit when-and-when-not. The closing lag note tells agents when the tool may not yet have a label.

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

A4/5.0
Disambiguation4/5

Most tools have distinct names and purposes, but the large number of meta-tools (e.g., ask_pipeworx variants, deep_research) and overlapping research/scanning tools (entity_profile, compare_entities, recent_changes) could cause confusion. An agent may need to carefully read descriptions to choose correctly.

Naming Consistency3/5

Snake_case is prevalent but not universal. FDA tools are consistently named with 'fda_' prefix, but there are single-word verbs (remember, recall), camelCase is absent, and some tool names are long and descriptive (scan_competitor_ai_presence). The mix of patterns is readable but not highly consistent.

Tool Count3/5

43 tools is high and includes both dedicated tools and meta-tools that can access thousands more. There is redundancy (e.g., FDA data can be retrieved via fda_drug_approvals or ask_pipeworx). The scope is broad, but many tools could be consolidated. Count feels borderline excessive for the apparent purpose.

Completeness4/5

FDA coverage is excellent with tools for approvals, labels, events, recalls, shortages, warning letters, etc. Other domains (financial, betting, npm) are covered by meta-tools, providing breadth. However, dedicated non-FDA tools are sparse, and the server relies heavily on the universal query tools for completeness.

Resources