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Glama

Openfda

by io.github.pipeworx-io

Fda Faers Reaction Profile

fda_faers_reaction_profile
Read-onlyIdempotent

Summarize the most frequently co-reported FAERS reactions plus serious/non-serious report counts for a drug. Counts are spontaneous reports with duplicates, co-medications, and reporting bias—not event rates or proof of causation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugYes
limitNo
to_dateNoOptional. YYYY-MM-DD. OMIT unless from_date is also set. Must be after from_date.
from_dateNoOptional. YYYY-MM-DD. OMIT for all-time counts (the common case). Only specify if the question is about a specific date range — and never set from_date equal to to_date (a zero-width window returns 0).

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / from_date / description
      Added value: +"Optional. YYYY-MM-DD. OMIT for all-time counts (the common case). Only specify if the question is about a specific date range — and never set from_date equal to to_date (a zero-width window returns 0)."
    • addedInput schema / properties / to_date / description
      Added value: +"Optional. YYYY-MM-DD. OMIT unless from_date is also set. Must be after from_date."
  2. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "drug": "metformin"
-  },
-  {
-    "drug": "Ozempic",
-    "from_date": "2025-01-01",
-    "limit": 20,
-    "to_date": "2025-12-31"
-  }
-]New value: +[
+  {
+    "drug": "metformin"
+  },
+  {
+    "drug": "semaglutide"
+  },
+  {
+    "drug": "Ozempic",
+    "from_date": "2025-01-01",
+    "limit": 20,
+    "to_date": "2025-12-31"
+  }
+]
  3. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "drug": "Ozempic",
-    "from_date": "2025-01-01",
-    "limit": 20,
-    "to_date": "2025-12-31"
-  }
-]New value: +[
+  {
+    "drug": "metformin"
+  },
+  {
+    "drug": "Ozempic",
+    "from_date": "2025-01-01",
+    "limit": 20,
+    "to_date": "2025-12-31"
+  }
+]
  4. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, and the description adds critical behavioral caveats beyond those annotations: the counts are spontaneous reports with duplicates, co-medications, and reporting bias. This is exactly the kind of caveat an agent needs before interpreting the output as an event rate or proof of causation. It also implies the tool aggregates a 'reaction profile' for a drug, which helps set 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?

Two sentences, no waste: the first sentence states the function and the output, the second sentence delivers the essential caveat about data interpretation. The caveat is front-loaded in the second sentence, not buried in a wall of text. This is an ideal size for this kind of tool.

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?

The description is complete enough for a read-only aggregate tool with no output schema. It covers purpose, the main parameter, and the key interpretative caveats. The only missing context is the behavior/semantics of 'limit' (does it cap the number of reaction rows? the number of total reports?) and any explicit note about date-range filtering behavior, which the schema partially covers. Overall, strong.

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 description coverage is 50%, so the description has some burden. The description adds meaning for the main 'drug' parameter by calling it a drug identifier, but it doesn't explain the 'limit' parameter. The schema itself provides good guidance for from_date/to_date, including the warning about zero-width windows. The description's caveat about 'co-reported reactions' helps the agent understand what the drug parameter drives, but the limit parameter remains undocumented anywhere.

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?

The description states a specific verb ('Summarize'), a specific resource ('FAERS reactions'), and the output contents ('most frequently co-reported reactions plus serious/non-serious report counts'). It clearly distinguishes itself from the sibling fda_faers_signal_summary and fda_faers_trend by focusing on a per-drug reaction profile rather than a signal or trend.

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

Usage Guidelines4/5

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

The description doesn't explicitly name an alternative tool, but the explicit caveat about spontaneous reports, duplicates, co-medications, and reporting bias effectively tells the agent when NOT to use this tool: when the question asks about causation, event rates, or validated safety signals. It also implicitly frames the tool as suitable for exploratory per-drug reaction profiling. Slight gap: it doesn't name a sibling for signal-focused queries.

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