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Glama

Openfda

by io.github.pipeworx-io

Fda Faers Signal Summary

fda_faers_signal_summary
Read-onlyIdempotent

Calculate report-level FAERS disproportionality routing metrics for one drug/reaction pair using a 2×2 reporting table. 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. ROR/PRR are screening statistics—not incidence, causality, comparative drug safety, or an FDA safety conclusion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugYesBrand or generic drug name.
to_dateNoOptional YYYY-MM-DD.
reactionYesMedDRA preferred term, matched whole rather than as a substring. Natural English word order is accepted and corrected ("ischaemic optic neuropathy" resolves to "OPTIC ISCHAEMIC NEUROPATHY").
from_dateNoOptional YYYY-MM-DD.

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: -[
-  {
-    "drug": "Ozempic",
-    "from_date": "2025-01-01",
-    "reaction": "Nausea",
-    "to_date": "2025-12-31"
-  }
-]New value: +[
+  {
+    "drug": "semaglutide",
+    "reaction": "ischaemic optic neuropathy"
+  },
+  {
+    "drug": "Ozempic",
+    "from_date": "2025-01-01",
+    "reaction": "Nausea",
+    "to_date": "2025-12-31"
+  }
+]
    • changedInput schema / properties / reaction / description
      Previous value: -"MedDRA preferred-term text."New value: +"MedDRA preferred term, matched whole rather than as a substring. Natural English word order is accepted and corrected (\"ischaemic optic neuropathy\" resolves to \"OPTIC ISCHAEMIC NEUROPATHY\")."
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses several important behaviors: whole-term matching only, natural word order resolution and disclosure, unresolved terms reported as unresolved rather than zero, and the screening-statistic nature of ROR/PRR. This adds substantial context beyond the structured fields.

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 concise (three sentences) and front-loaded: it starts with the core calculation, then adds matching nuance, then statistical caveats. Every sentence earns its place with no filler.

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?

For a tool without an output schema, the description covers the purpose, matching behavior, and interpretation boundaries well. It could explicitly state the return format (e.g., a table with ROR/PRR values), but the description is largely complete for an analysis tool with robust annotations.

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 coverage is 100%, so the schema already documents all parameters. The description adds meaningful detail by explaining the neuropathy example for whole-term matching and the unresolved-not-zero behavior, which enriches the reaction parameter semantics beyond the schema text.

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 opens with a specific verb and resource: 'Calculate report-level FAERS disproportionality routing metrics for one drug/reaction pair using a 2×2 reporting table.' This clearly distinguishes the tool from sibling FDA tools like fda_faers_reaction_profile by emphasizing a single pair and the 2×2 table methodology.

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 provides clear context for when to use this tool: it is for a single drug/reaction pair and requires exact MedDRA term matching, with explicit notes on how broad words like 'neuropathy' are handled. It also clarifies statistical limitations (not incidence/causality/safety conclusion), which helps rule out inappropriate use, though it does not name specific alternative 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

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