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Openfda

by io.github.pipeworx-io

Fda Drug Approvals

fda_drug_approvals
Read-onlyIdempotent

Find FDA-approved drugs by brand name, active ingredient, or application number. To find generic versions of a drug, search by active ingredient using products.active_ingredients.name (NOT openfda.generic_name — that field only works in the label endpoint). IMPORTANT: drug names in the FDA database are stored in UPPERCASE — always pass ingredient and brand names in uppercase (e.g. "APIXABAN" not "apixaban") or you will get 0 results. Returns approval status, sponsor, application number (ANDA = generic, NDA = brand), and application details. There is NO queryable "application_type" field — never add application_type:"ANDA" (it returns 0 results). To limit to generics, search by active ingredient and read the ANDA/NDA prefix on application_number in the results. This endpoint has NO indication/disease field — for "drugs approved to treat " use fda_drug_labels (which searches indications_and_usage); an indication phrase passed here is silently ignored and matches by drug name only. The response total can exceed the 100-row cap on a single page — use skip to page past it, and sort to get genuinely-newest-first ordering ("most recently approved" is not the default order).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoOffset for pagination (default 0). The reported `total` is often larger than one page — pass skip=100 to reach the 101st+ result of a large query instead of assuming the first page is everything.
sortNoOrder results by each drug's original FDA approval date instead of openFDA's default (unordered/relevance) order. approval_date_desc puts the genuinely most-recently-approved drug first — needed for "most recent approval" questions, which are wrong without it. Computed client-side from each record's own ORIG+approved submission (openFDA's native sort on this field is unreliable — it reads any submission in a drug's history, so an old drug with a recent label supplement can outrank a real new approval). Sorting is exact over up to 1000 matching records per call; a `sort_note` in the response says if the query matched more than that.
limitNoNumber of results (1-100, default 10)
queryYesOpenFDA drugsfda search query. Drug names MUST be UPPERCASE in quotes. To find all generics for a drug: 'products.active_ingredients.name:"APIXABAN"' (returns all ANDA + NDA approvals). By brand: 'openfda.brand_name:"KEYTRUDA"'. By original submission: 'submissions.submission_type:"ORIG"'. Do NOT use application_type (no such field — returns 0); ANDA vs NDA is read from the application_number prefix in the results, not filtered in the query. NOTE: use products.active_ingredients.name (not openfda.generic_name which is a label-API field and returns 0 here).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesTotal count of matching FDA-approved drugs
resultsYesArray of drug approval objects

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "query": "openfda.brand_name:\"KEYTRUDA\""
-  },
-  {
-    "limit": 20,
-    "query": "products.active_ingredients.name:\"pembrolizumab\""
-  },
-  {
-    "limit": 5,
-    "query": "products.active_ingredients.name:\"AMOXICILLIN\"",
-    "skip": 100
-  },
-  {
-    "limit": 10,
-    "query": "products.active_ingredients.name:\"AMOXICILLIN\"",
-    "sort": "approval_date_desc"
-  }
-]New value: +[
+  {
+    "query": "openfda.brand_name:\"KEYTRUDA\""
+  },
+  {
+    "limit": 10,
+    "query": "products.active_ingredients.name:\"SEMAGLUTIDE\"",
+    "sort": "approval_date_desc"
+  },
+  {
+    "limit": 20,
+    "query": "products.active_ingredients.name:\"pembrolizumab\""
+  },
+  {
+    "limit": 5,
+    "query": "products.active_ingredients.name:\"AMOXICILLIN\"",
+    "skip": 100
+  },
+  {
+    "limit": 10,
+    "query": "products.active_ingredients.name:\"AMOXICILLIN\"",
+    "sort": "approval_date_desc"
+  }
+]
  2. Changed3 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "query": "openfda.brand_name:\"KEYTRUDA\""
-  },
-  {
-    "limit": 20,
-    "query": "products.active_ingredients.name:\"pembrolizumab\""
-  }
-]New value: +[
+  {
+    "query": "openfda.brand_name:\"KEYTRUDA\""
+  },
+  {
+    "limit": 20,
+    "query": "products.active_ingredients.name:\"pembrolizumab\""
+  },
+  {
+    "limit": 5,
+    "query": "products.active_ingredients.name:\"AMOXICILLIN\"",
+    "skip": 100
+  },
+  {
+    "limit": 10,
+    "query": "products.active_ingredients.name:\"AMOXICILLIN\"",
+    "sort": "approval_date_desc"
+  }
+]
    • addedInput schema / properties / skip
      Added value: +{
+  "description": "Offset for pagination (default 0). The reported `total` is often larger than one page — pass skip=100 to reach the 101st+ result of a large query instead of assuming the first page is everything.",
+  "type": "number"
+}
    • addedInput schema / properties / sort
      Added value: +{
+  "description": "Order results by each drug's original FDA approval date instead of openFDA's default (unordered/relevance) order. approval_date_desc puts the genuinely most-recently-approved drug first — needed for \"most recent approval\" questions, which are wrong without it. Computed client-side from each record's own ORIG+approved submission (openFDA's native sort on this field is unreliable — it reads any submission in a drug's history, so an old drug with a recent label supplement can outrank a real new approval). Sorting is exact over up to 1000 matching records per call; a `sort_note` in the response says if the query matched more than that.",
+  "enum": [
+    "approval_date_asc",
+    "approval_date_desc"
+  ],
+  "type": "string"
+}
  3. Changed1 schema field changed
    • changedInput schema / properties / query / description
      Previous value: -"OpenFDA drugsfda search query. Drug names MUST be UPPERCASE in quotes. To find all generics for a drug: 'products.active_ingredients.name:\"APIXABAN\"' (returns all ANDA + NDA approvals). By brand: 'openfda.brand_name:\"KEYTRUDA\"'. By application type: 'submissions.submission_type:\"ORIG\"'. NOTE: use products.active_ingredients.name (not openfda.generic_name which is a label-API field and returns 0 here)."New value: +"OpenFDA drugsfda search query. Drug names MUST be UPPERCASE in quotes. To find all generics for a drug: 'products.active_ingredients.name:\"APIXABAN\"' (returns all ANDA + NDA approvals). By brand: 'openfda.brand_name:\"KEYTRUDA\"'. By original submission: 'submissions.submission_type:\"ORIG\"'. Do NOT use application_type (no such field — returns 0); ANDA vs NDA is read from the application_number prefix in the results, not filtered in the query. NOTE: use products.active_ingredients.name (not openfda.generic_name which is a label-API field and returns 0 here)."
  4. Changed1 schema field changed
    • changedInput schema / properties / query / description
      Previous value: -"OpenFDA search query. Examples: 'openfda.brand_name:\"KEYTRUDA\"', 'products.active_ingredients.name:\"pembrolizumab\"', 'submissions.submission_type:\"ORIG\"'"New value: +"OpenFDA drugsfda search query. Drug names MUST be UPPERCASE in quotes. To find all generics for a drug: 'products.active_ingredients.name:\"APIXABAN\"' (returns all ANDA + NDA approvals). By brand: 'openfda.brand_name:\"KEYTRUDA\"'. By application type: 'submissions.submission_type:\"ORIG\"'. NOTE: use products.active_ingredients.name (not openfda.generic_name which is a label-API field and returns 0 here)."
  5. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "results": {
+      "description": "Array of drug approval objects",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Total count of matching FDA-approved drugs",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total",
+    "results"
+  ],
+  "type": "object"
+}
  6. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "openfda.brand_name:\"KEYTRUDA\""
+  },
+  {
+    "limit": 20,
+    "query": "products.active_ingredients.name:\"pembrolizumab\""
+  }
+]
  7. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Discloses numerous non-obvious behaviors beyond the readOnly/idempotent annotations: uppercase-only name matching with silent 0-result failures, indication phrases being silently ignored, application_type returning 0, the native sort being unreliable (reads any submission in history), and the 100-row page cap. None of this contradicts the annotations; it materially extends them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Long (~220 words) but unusually dense — nearly every sentence carries a distinct operational fact with zero filler. Core purpose is front-loaded before caveats. Slight redundancy with the already-excellent schema descriptions (uppercase requirement, sort rationale appear in both) prevents a 5.

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?

For a tool with high behavioral quirkiness, this is complete: it names all three query modes, what the response contains, the ANDA/NDA interpretation rule, the sibling for condition queries, and the pagination/sorting caveats. Output schema exists, so return-value explanation is not the description's job. Nothing an agent needs to call it correctly 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 baseline is 3. The description adds strategic value beyond the schema: the application_type trap, the generic_name field trap, and the rationale for pairing skip with sort on large result sets. Some content (uppercase warning, skip behavior) duplicates the rich schema descriptions, so the added increment is real but not maximal.

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 a precise verb+resource statement: "Find FDA-approved drugs by brand name, active ingredient, or application number." It explicitly differentiates from fda_drug_labels by stating this endpoint has no indication/disease field, and clarifies its own output scope (approval status, sponsor, application number, details). The ANDA/NDA distinction further sharpens what the tool returns.

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?

Gives explicit when-to-use and when-not-to-use guidance: use active ingredient search for generics, use fda_drug_labels for condition-based queries, never use application_type, never use openfda.generic_name. It also tells the agent when to add skip (large totals) and sort (genuinely newest-first), naming the exact alternative tool and the exact conditions that route to it.

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