Fda Drug Competition

Orange Book Loss Of Exclusivity Profile

orange_book_loss_of_exclusivity_profile

Read-onlyIdempotent

Build an FDA Orange Book loss-of-exclusivity evidence profile for a drug. Returns reference applications, approved ANDA counts and timing, plus active and elapsed listed patent and regulatory-exclusivity date ranges for LOE diligence.

Input Schema

TableJSON Schema

Name	Required	Description
`drug`	Yes	Ingredient or trade name.
`limit`	No	Rows per evidence list (1-100, default 50).
`as_of_date`	No	YYYY-MM-DD cutoff for active versus elapsed listings (default today).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`drug`	Yes
`source`	Yes
`patents`	Yes
`as_of_date`	Yes
`dataset_note`	No
`dataset_stale`	No
`exclusivities`	Yes
`interpretation`	Yes
`dataset_updated`	No
`dataset_age_days`	No
`matched_products`	Yes
`listed_patent_range`	Yes
`earliest_anda_approval`	Yes
`reference_applications`	Yes
`listed_exclusivity_range`	Yes
`approved_anda_applications`	Yes

Schema Changelog

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

Changed3 schema fields changedSep 2, 2026
- addedOutput schema / properties / dataset_age_days
```
Added value: +{
+  "type": [
+    "number",
+    "null"
+  ]
+}
```
- addedOutput schema / properties / dataset_note
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / dataset_stale
```
Added value: +{
+  "type": "boolean"
+}
```
AddedAug 6, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral scope by specifying 'approved ANDA counts' (excludes unapproved/tentative) and distinguishing 'active and elapsed' date ranges, which informs the agent about what the tool filters or computes. No contradiction with annotations.

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 tightly written sentences with no redundancy. The first sentence front-loads the action and resource; the second lists the return contents. Every clause contributes useful information.

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?

With a full input schema, an output schema, and annotations covering safety, the description is sufficient for correct invocation. It clearly states what the tool does and what it returns. It is slightly incomplete for tool selection because it does not explicitly distinguish among the many Orange Book siblings, but overall it is adequate.

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 all parameters are already documented (drug, limit, as_of_date). The tool description does not add new parameter meaning beyond the schema; it only restates the overall purpose. 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 and resource: 'Build an FDA Orange Book loss-of-exclusivity evidence profile for a drug.' It also lists concrete outputs such as reference applications, approved ANDA counts, and patent/exclusivity date ranges. However, it does not explicitly differentiate this tool from siblings like orange_book_exclusivity_expirations or orange_book_patent_expirations, relying on the unique 'loss-of-exclusivity evidence profile' wording.

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 phrase 'for LOE diligence' gives a clear intended context, and the output list implies when an agent would need this tool. But there is no explicit when-to-use-versus-alternatives guidance or exclusions, which is a notable gap given many similar Orange Book siblings such as orange_book_competition_profile and orange_book_generic_entry_timeline.

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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