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Glama

Openfda

by io.github.pipeworx-io

Fda Novel Approvals

fda_novel_approvals
Read-onlyIdempotent

Most recent novel drug approvals — the newest drugs approved by the FDA. Use for "what is the most recent novel drug the FDA approved", "latest FDA drug approvals", "recently approved new drugs or biologics this month/year". Returns original NDA/BLA approvals (generics excluded by default) sorted newest-first, with approval date, brand name, active ingredients, sponsor, and application number. Not for label text (fda_drug_labels) or searching a specific known drug (fda_drug_approvals).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLook-back window in days (1-365, default 90)
skipNoOffset into the newest-first results (default 0). total_in_window can exceed the 50-result cap on one call — pass skip=50 to page further into the window instead of assuming the window is fully covered.
limitNoMax approvals to return (1-50, default 10)
include_genericsNoAlso include original ANDA (generic) approvals (default false — novel means NDA/BLA only)

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": "Offset into the newest-first results (default 0). total_in_window can exceed the 50-result cap on one call — pass skip=50 to page further into the window instead of assuming the window is fully covered.",
+  "type": "number"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "days": 90,
+    "limit": 10
+  },
+  {
+    "days": 30,
+    "include_generics": false,
+    "limit": 5
+  }
+]
  3. Added

TDQS

A4.5/5.0
Behavior4/5

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds substantive behavior beyond annotations: results are 'sorted newest-first', it 'Returns original NDA/BLA approvals', and it notes 'generics excluded by default'. This clarifies the operational scope and default filtering behavior, which is valuable for correct invocation.

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 cover purpose, usage context, return content, ordering, default behavior, and exclusions — every sentence earns its place. The main purpose is front-loaded and query examples are grouped into one sentence, making it efficient and scannable.

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 tool's moderate complexity, four optional schema-documented parameters, rich annotations, and no output schema, the description is complete. It states what is returned, how results are ordered, the default filtering, and the boundary versus sibling tools. An agent has everything needed to decide when and how to call it.

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%, so the schema documents all four parameters in detail. The description adds context around the include_generics default and result ordering, but does not need to repeat parameter syntax. This matches the baseline 3 for high schema coverage where the description provides only marginal parameter-level value.

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 resource: 'Most recent novel drug approvals — the newest drugs approved by the FDA.' It names the concrete outputs (approval date, brand name, active ingredients, sponsor, application number) and explicitly distinguishes itself from siblings: 'Not for label text (fda_drug_labels) or searching a specific known drug (fda_drug_approvals).' An agent can tell exactly what this tool is for.

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?

The description gives explicit usage contexts with natural-language examples: 'Use for "what is the most recent novel drug the FDA approved", "latest FDA drug approvals", "recently approved new drugs or biologics this month/year".' It also states exclusions and names the alternative tools by name, leaving no ambiguity about when not to use 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