fda_application_historyRetrieve one Drugs@FDA NDA, ANDA, or BLA application and return its products and chronological submission-action history. Submission codes require regulatory interpretation and do not by themselves establish approval scope, exclusivity, or current marketing.
| Name | Required | Description | Default |
|---|---|---|---|
| application_number | Yes | NDA, ANDA, or BLA followed by digits. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds the caveat that submission codes require interpretation and don't establish approval scope/exclusivity/marketing, which is valuable behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, purpose-first, with no wasted words. The regulatory caveat is useful and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read-only tool with thorough annotations and no output schema, the description is sufficient. It explains what the tool returns and includes a critical interpretation warning, though it doesn't detail the return structure or pagination, which is acceptable given the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter `application_number` is fully described in the schema with format 'NDA, ANDA, or BLA followed by digits.' The description repeats the application types but adds no additional syntax or semantics beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a single Drugs@FDA application (NDA, ANDA, or BLA) and returns products plus chronological submission-action history. This distinguishes it from sibling tools like fda_drug_approvals or fda_crl_detail, which address different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives context about regulatory interpretation but does not explicitly state when to use this over alternatives or list exclusions. The use case is implied as retrieving application history, but no sibling alternatives are mentioned, so guidance is limited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.