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Glama

Openfda

by io.github.pipeworx-io

Fda Drug Events

fda_drug_events
Read-onlyIdempotent

Search FAERS adverse event reports by drug name, MedDRA reaction term, or date range. Returns report counts, reaction types, seriousness levels, and dates. Natural multi-word reaction phrasing is retried against MedDRA preferred-term word order before an empty result is reported. FAERS reports do not establish incidence or causality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoOffset for pagination (default 0)
limitNoNumber of results (1-100, default 10)
queryYesOpenFDA search query. Filter drugs on patient.drug.medicinalproduct — the report's own drug-name field, present on every record and matching brand or generic. The patient.drug.openfda.* fields are enrichment that is missing for many newer drugs (semaglutide/OZEMPIC among them) and silently match nothing; this tool retries them against medicinalproduct, but naming it directly costs one call instead of two. Examples: 'patient.drug.medicinalproduct:"OZEMPIC"', 'patient.drug.medicinalproduct:"semaglutide"+AND+serious:1', 'receivedate:[20240101+TO+20241231]'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipYesOffset used in pagination
limitYesNumber of results returned
totalYesTotal count of matching adverse event reports
resultsYesArray of adverse event report objects
reaction_resolvedNoThe MedDRA preferred term actually used for the filter
reaction_requestedNoThe reaction term as the caller supplied it, uppercased
reaction_resolutionNoHow the supplied reaction term mapped onto a MedDRA preferred term. not_a_meddra_preferred_term means the filter matched nothing — counts are zero because the term missed, not because no reports exist
reaction_resolution_hintNoPresent when the term matched nothing; names a working way to discover the real preferred terms
reaction_resolution_noteNoPresent when word order was corrected; states the substitution made

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "query": "patient.drug.openfda.brand_name:\"OZEMPIC\""
-  },
-  {
-    "limit": 50,
-    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1"
-  },
-  {
-    "limit": 10,
-    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+patient.reaction.reactionmeddrapt.exact:\"ISCHAEMIC OPTIC NEUROPATHY\""
-  }
-]New value: +[
+  {
+    "query": "patient.drug.medicinalproduct:\"OZEMPIC\""
+  },
+  {
+    "limit": 50,
+    "query": "patient.drug.medicinalproduct:\"semaglutide\"+AND+serious:1"
+  },
+  {
+    "limit": 10,
+    "query": "patient.drug.medicinalproduct:\"semaglutide\"+AND+patient.reaction.reactionmeddrapt.exact:\"OPTIC ISCHAEMIC NEUROPATHY\""
+  }
+]
    • changedInput schema / properties / query / description
      Previous value: -"OpenFDA search query. Examples: 'patient.drug.openfda.brand_name:\"OZEMPIC\"', 'patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1', 'receivedate:[20240101+TO+20241231]'"New value: +"OpenFDA search query. Filter drugs on patient.drug.medicinalproduct — the report's own drug-name field, present on every record and matching brand or generic. The patient.drug.openfda.* fields are enrichment that is missing for many newer drugs (semaglutide/OZEMPIC among them) and silently match nothing; this tool retries them against medicinalproduct, but naming it directly costs one call instead of two. Examples: 'patient.drug.medicinalproduct:\"OZEMPIC\"', 'patient.drug.medicinalproduct:\"semaglutide\"+AND+serious:1', 'receivedate:[20240101+TO+20241231]'"
  2. Changed6 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "query": "patient.drug.openfda.brand_name:\"OZEMPIC\""
-  },
-  {
-    "limit": 50,
-    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1"
-  },
-  {
-    "limit": 10,
-    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+patient.reaction.reactionmeddrapt.exact:\"OPTIC ISCHAEMIC NEUROPATHY\""
-  }
-]New value: +[
+  {
+    "query": "patient.drug.openfda.brand_name:\"OZEMPIC\""
+  },
+  {
+    "limit": 50,
+    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1"
+  },
+  {
+    "limit": 10,
+    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+patient.reaction.reactionmeddrapt.exact:\"ISCHAEMIC OPTIC NEUROPATHY\""
+  }
+]
    • addedOutput schema / properties / reaction_requested
      Added value: +{
+  "description": "The reaction term as the caller supplied it, uppercased",
+  "type": "string"
+}
    • addedOutput schema / properties / reaction_resolution
      Added value: +{
+  "description": "How the supplied reaction term mapped onto a MedDRA preferred term. not_a_meddra_preferred_term means the filter matched nothing — counts are zero because the term missed, not because no reports exist",
+  "enum": [
+    "exact_match",
+    "word_order_corrected",
+    "not_a_meddra_preferred_term"
+  ],
+  "type": "string"
+}
    • addedOutput schema / properties / reaction_resolution_hint
      Added value: +{
+  "description": "Present when the term matched nothing; names a working way to discover the real preferred terms",
+  "type": "string"
+}
    • addedOutput schema / properties / reaction_resolution_note
      Added value: +{
+  "description": "Present when word order was corrected; states the substitution made",
+  "type": "string"
+}
    • addedOutput schema / properties / reaction_resolved
      Added value: +{
+  "description": "The MedDRA preferred term actually used for the filter",
+  "type": "string"
+}
  3. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "query": "patient.drug.openfda.brand_name:\"OZEMPIC\""
-  },
-  {
-    "limit": 50,
-    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1"
-  }
-]New value: +[
+  {
+    "query": "patient.drug.openfda.brand_name:\"OZEMPIC\""
+  },
+  {
+    "limit": 50,
+    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1"
+  },
+  {
+    "limit": 10,
+    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+patient.reaction.reactionmeddrapt.exact:\"OPTIC ISCHAEMIC NEUROPATHY\""
+  }
+]
  4. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "limit": {
+      "description": "Number of results returned",
+      "type": "number"
+    },
+    "results": {
+      "description": "Array of adverse event report objects",
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "skip": {
+      "description": "Offset used in pagination",
+      "type": "number"
+    },
+    "total": {
+      "description": "Total count of matching adverse event reports",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total",
+    "skip",
+    "limit",
+    "results"
+  ],
+  "type": "object"
+}
  5. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "patient.drug.openfda.brand_name:\"OZEMPIC\""
+  },
+  {
+    "limit": 50,
+    "query": "patient.drug.openfda.generic_name:\"semaglutide\"+AND+serious:1"
+  }
+]
  6. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already mark this as readOnly, idempotent, and non-destructive, so the barrier is higher, but the description adds valuable behavioral context: returns specific fields, retries natural wording against MedDRA preferred-term order before returning empty, and explicitly cautions that FAERS reports do not establish incidence or causality. This goes well beyond what annotations provide and contains no contradiction.

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 short sentences, each with distinct value: purpose, return contents, retry behavior, and a necessary caveat. No fluff or redundancy; the most important action 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?

Given the output schema exists and annotations are rich, the description provides enough context on scope, results, retry behavior, and limitations. An agent can select and invoke the tool with confidence, and the schema fills in query syntax details.

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 description coverage is 100%, with detailed guidance and examples in the query parameter itself, so the schema carries the load. The tool description only gives high-level search dimensions and does not add parameter-level syntax or additional semantics beyond the schema, which fits the baseline 3.

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 states a specific verb ('Search'), resource ('FAERS adverse event reports'), and search dimensions (drug name, MedDRA reaction term, date range), making the tool's function clear. It does not explicitly differentiate itself from sibling FAERS tools like fda_event_counts or fda_faers_reaction_profile, so it stops short of a 5.

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 intended use is implied: an agent needing adverse event report counts, reaction types, seriousness, or dates would infer this tool is relevant. However, there is no explicit guidance on when to choose this tool over the many FAERS-related siblings, nor exclusions for cases better served by fda_event_counts or fda_faers_signal_summary.

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