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Openfda

by io.github.pipeworx-io

Fda Food Recalls

fda_food_recalls
Read-onlyIdempotent

Search FDA FOOD recall / enforcement reports (openFDA /food/enforcement) — product recalls, reasons, classification, recalling firm, distribution, and status. Use for food-safety / recall-history questions (distinct from fda_drug_recalls which covers drugs).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoPagination offset (default 0) — the reported total can exceed one page of 100.
limitNoNumber of results (1-100, default 10)
queryNoOpenFDA search query (optional — omit for most recent recalls). Examples: 'reason_for_recall:"Listeria"', 'state:"CA"', 'classification:"Class I"'.

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": "Pagination offset (default 0) — the reported total can exceed one page of 100.",
+  "type": "number"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "reason_for_recall:\"Listeria\""
+  },
+  {
+    "limit": 30,
+    "query": "state:\"CA\""
+  }
+]
  3. Added

TDQS

A4.2/5.0
Behavior3/5

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, so the safety profile is covered. The description adds the data source and queryable fields but no additional behavioral traits like pagination behavior or response limits. With annotations handling the burden, a mid score is appropriate; there is no contradiction.

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 filler. The core action, resource, content fields, and sibling differentiation are packed into a compact, front-loaded structure. Every clause earns its place.

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 simple read-only search tool with three optional parameters and no output schema, the description covers the endpoint, result content, usage context, and sibling distinction. The only minor gap is that it doesn't describe default behavior when query is omitted, but the schema already states that. This is complete enough for an agent to call correctly.

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%, with each parameter (skip, limit, query) already documented including examples. The tool description adds context about what can be searched but doesn't add parameter-level semantics beyond the schema. Baseline 3 applies since the schema carries the load.

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 uses a specific verb ('Search'), identifies the resource ('FDA FOOD recall / enforcement reports (openFDA /food/enforcement)'), and lists the covered content fields (product recalls, reasons, classification, recalling firm, distribution, status). It explicitly differentiates from the sibling fda_drug_recalls, making the tool's scope unmistakable.

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?

It explicitly states when to use the tool ('Use for food-safety / recall-history questions') and names the alternative ('distinct from fda_drug_recalls which covers drugs'), giving the agent a clear decision rule. This is direct, actionable guidance rather than implied context.

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