Skip to main content
Glama

Openfda

by io.github.pipeworx-io

Fda Drug Recalls

fda_drug_recalls
Read-onlyIdempotent

Search FDA drug recalls and enforcement actions by drug name or reason — returns recall classification, date, reason and enforcement status. A multi-word search is tried as ALL terms first and falls back to ANY term when nothing matches all of them; the response says which happened in match_mode, so a loose match is never mistaken for a precise one. Recall reasons are free narrative text and often name the contaminant rather than the harm ("NDMA impurity", not "cancer"), so search the substance when a symptom finds nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoPagination offset (default 0) — the reported total can exceed one page of 100.
limitNoNumber of results (1-100, default 10)
queryYesOpenFDA search query. Examples: 'openfda.brand_name:"VALSARTAN"', 'classification:"Class I"', 'reason_for_recall:"contamination"'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesTotal count of matching drug recalls
resultsYesArray of drug recall/enforcement 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
    • changedInput schema / examples
      Previous value: -[
-  {
-    "query": "openfda.brand_name:\"VALSARTAN\""
-  },
-  {
-    "limit": 25,
-    "query": "classification:\"Class I\"+AND+reason_for_recall:\"contamination\""
-  }
-]New value: +[
+  {
+    "limit": 10,
+    "query": "metformin contamination"
+  },
+  {
+    "query": "openfda.brand_name:\"VALSARTAN\""
+  },
+  {
+    "limit": 25,
+    "query": "classification:\"Class I\"+AND+reason_for_recall:\"contamination\""
+  }
+]
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "results": {
+      "description": "Array of drug recall/enforcement objects",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Total count of matching drug recalls",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total",
+    "results"
+  ],
+  "type": "object"
+}
  4. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "openfda.brand_name:\"VALSARTAN\""
+  },
+  {
+    "limit": 25,
+    "query": "classification:\"Class I\"+AND+reason_for_recall:\"contamination\""
+  }
+]
  5. First observed

TDQS

A4.2/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral nuances: the ALL-terms-first-then-ANY fallback, the match_mode indicator in responses, and the free-narrative nature of recall reasons. These details materially change how an agent should interpret results and are not available from annotations.

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 sentences with no filler. The first sentence states what the tool does and returns; the second warns about match interpretation; the third gives a practical search tip. Each sentence earns its place and the most important information is front-loaded.

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?

With a full input schema, an output schema, and strong annotations, the description fills the remaining gaps: query fallback semantics, ambiguity handling, and domain-specific search strategy. Pagination and limits are already documented in the schema, so their omission is not a gap.

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. The description adds value beyond the schema by explaining the multi-word matching fallback and advising users to search for the contaminant rather than the harm. This helps agents craft better queries than the raw schema examples alone would suggest.

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 verb ('Search'), the resource ('FDA drug recalls and enforcement actions'), and the search keys ('by drug name or reason'), plus the returned fields. It is distinct from siblings like fda_food_recalls by subject matter, though it does not explicitly name an alternative or contrast itself with any specific sibling.

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 gives strong guidance on how to search effectively: multi-word queries fall back to ANY-term matching, match_mode disambiguates, and users should search for contaminants rather than symptom names. However, it never directly addresses when to choose this tool over siblings like fda_food_recalls or fda_warning_letters; the usage context is implied rather than explicit.

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

All Openfda toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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