Skip to main content
Glama

Openfda

by io.github.pipeworx-io

Fda Faers Trend

fda_faers_trend
Read-onlyIdempotent

Aggregate daily FAERS report counts into calendar months for a drug and optional reaction over a bounded date window. The reaction argument matches one whole MedDRA preferred term, so a broad word like "neuropathy" counts only reports filed under that exact term and not the specific terms containing it. Natural multi-word phrasing is resolved to MedDRA word order and disclosed; a term matching nothing is reported as unresolved rather than as zero reports. Trends reflect reporting activity, publicity, utilization, duplicates, and database updates—not incidence or changing clinical risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugYes
to_dateYes
reactionNo
from_dateYes

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/no-destructive annotations, the description discloses specific behavioral subtleties: exact MedDRA preferred term matching, natural multi-word phrase resolution, unresolved terms reported as unresolved rather than zero, and the caveat that trends reflect reporting activity, publicity, utilization, duplicates, and database updates. This is rich, non-obvious context that significantly aids the agent.

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?

The description is three sentences, front-loaded with the core purpose, and every subsequent clause adds necessary nuance about reaction matching or interpretation. It is appropriately sized for the tool's complexity without any wasted words.

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 covers the tool's purpose, reaction behavior, and interpretation caveats thoroughly. Since there is no output schema, it does not fully explain the return structure, but 'aggregate into calendar months' conveys the expected form. It is adequate for a read-only aggregate tool with strong annotations.

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?

With 0% schema description coverage, the description must compensate. It identifies drug, optional reaction, and a bounded date window (implying from_date/to_date), and thoroughly explains reaction matching semantics. However, it omits details about drug matching (e.g., brand name vs generic) and date format, leaving required parameters only partially clarified.

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 'Aggregate daily FAERS report counts into calendar months for a drug and optional reaction over a bounded date window,' clearly specifying verb, resource, and scope. It distinguishes itself from sibling tools like fda_event_counts or fda_faers_reaction_profile by emphasizing the monthly aggregation and optional reaction filter.

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?

It gives clear context: use for monthly trends, with an optional reaction, and cautions that trends reflect reporting activity, not incidence or clinical risk. However, it does not explicitly name alternatives or state situations where this tool should not be used, so it stops short of full exclusionary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Openfda toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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