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Glama

Fda Devices

by io.github.pipeworx-io

Fda Device Event Counts

fda_device_event_counts
Read-onlyIdempotent

Aggregate MAUDE reports for a device query by event type, manufacturer, product code, or receive date. Counts reflect reporting and database artifacts—not event rates or causal risk—and must not be compared without exposure denominators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoBuckets (1-100, default 20).
queryNoOptional raw openFDA MAUDE filter, e.g. 'date_received:[20250101+TO+20261231]'. Omit to rank across all reports.
count_fieldYesWhat to rank by. For "which DEVICES had the most reports" use device.generic_name.exact (device type) or device.brand_name.exact (specific product).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYes
sourceYes
bucketsYes
returnedYes

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / count_field / enum
      Previous value: -[
-  "device.generic_name.exact",
-  "device.brand_name.exact",
-  "device.openfda.device_name.exact",
-  "device.manufacturer_d_name.exact",
-  "event_type.exact",
-  "manufacturer_name.exact",
-  "device.device_report_product_code.exact",
-  "date_received"
-]New value: +[
+  "device.generic_name.exact",
+  "device.brand_name.exact",
+  "device.openfda.device_name.exact",
+  "device.manufacturer_d_name.exact",
+  "event_type.exact",
+  "device.device_report_product_code.exact",
+  "date_received"
+]
  2. Changed5 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "count_field": "event_type.exact",
-    "limit": 10,
-    "query": "device.device_report_product_code:MDS"
-  }
-]New value: +[
+  {
+    "count_field": "device.generic_name.exact",
+    "limit": 10
+  },
+  {
+    "count_field": "device.brand_name.exact",
+    "limit": 10
+  },
+  {
+    "count_field": "event_type.exact",
+    "limit": 10,
+    "query": "device.device_report_product_code:MDS"
+  }
+]
    • addedInput schema / properties / count_field / description
      Added value: +"What to rank by. For \"which DEVICES had the most reports\" use device.generic_name.exact (device type) or device.brand_name.exact (specific product)."
    • changedInput schema / properties / count_field / enum
      Previous value: -[
-  "event_type.exact",
-  "manufacturer_name.exact",
-  "device.device_report_product_code.exact",
-  "date_received"
-]New value: +[
+  "device.generic_name.exact",
+  "device.brand_name.exact",
+  "device.openfda.device_name.exact",
+  "device.manufacturer_d_name.exact",
+  "event_type.exact",
+  "manufacturer_name.exact",
+  "device.device_report_product_code.exact",
+  "date_received"
+]
    • changedInput schema / properties / query / description
      Previous value: -"Required raw openFDA MAUDE filter."New value: +"Optional raw openFDA MAUDE filter, e.g. 'date_received:[20250101+TO+20261231]'. Omit to rank across all reports."
    • changedInput schema / required
      Previous value: -[
-  "query",
-  "count_field"
-]New value: +[
+  "count_field"
+]
  3. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already mark the tool read-only and idempotent. The description adds valuable interpretive context that counts reflect reporting/database artifacts and are not causal risk, which is beyond the structured hints. It doesn't over-explain return behavior, but the caveat is substantive.

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: the first states the action and grouping options, the second provides a crucial interpretive warning. Zero filler, front-loaded, every 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?

For a simple aggregation tool with a rich input schema, output schema, and four annotations, the description is complete: it covers what is aggregated, by which dimensions, and warns about interpretation. No major gaps relative to complexity.

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% and the count_field enum includes detailed descriptions. The tool description's grouping dimensions loosely map to parameters but adds little beyond the schema, so the baseline 3 is appropriate.

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 uses a specific verb ('Aggregate') and names the resource (MAUDE reports) and grouping dimensions (event type, manufacturer, product code, receive date). It is clear but does not explicitly distinguish itself from sibling fda_device_adverse_events, which might also cover MAUDE data.

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 implies usage—when aggregated counts by dimension are needed—and includes an important caveat about not comparing counts without denominators. However, it provides no explicit when-to-use/when-not-to-use guidance or mention of alternatives among the FDA sibling tools.

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

A3.5/5.0
Disambiguation2/5

The FDA-specific tools are distinct, but they are mixed with many generic Pipeworx tools (e.g., ask_pipeworx variants, deep_research, entity_profile) that have overlapping purposes. This creates significant ambiguity for an agent trying to choose the right tool for FDA-related queries.

Naming Consistency2/5

Tool names follow two inconsistent patterns: FDA tools use 'fda_device_*' (consistent), while generic tools use various patterns like 'ask_pipeworx', 'deep_research', 'remember', etc. The mix of snake_case, camelCase, and descriptive phrases lacks coherence.

Tool Count2/5

With 37 tools, the count is high for what is intended as an FDA devices server. Only 6 tools are directly FDA-related; the rest are generic and dilute the purpose. The scope is mismatched, making the tool count inappropriate.

Completeness3/5

The FDA tools cover key areas: 510k search, adverse events, PMA, recalls, company profiles. However, the server is incomplete for its name because it lacks many tools that a comprehensive FDA devices server would have, and the generic tools don't fill those gaps.

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