fda_postmarket_risk_profileCombine a drug’s FAERS reporting profile, label warning fields, and FDA recall records for review routing. This is not a validated safety comparison, causal assessment, incidence estimate, or substitute for FDA communications and clinical review.
| Name | Required | Description | Default |
|---|---|---|---|
| drug | Yes | ||
| to_date | No | ||
| from_date | No | ||
| recall_limit | No | ||
| reaction_limit | No |
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. The description adds meaningful context by naming the combined data sources and warning that the output is not a validated safety assessment, which goes beyond the annotations and helps manage expectations.
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?
The description is composed of two concise sentences: the first states the action and purpose, the second adds essential caveats. There is no redundant wording or unnecessary detail.
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?
There is no output schema, and the description does not cover parameter details or output format. It provides a strong high-level purpose and limitations, but agents need more information about date ranges and limits to use the tool effectively, especially given the parameter count of 5.
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?
Schema description coverage is 0%, and the description does not explain any of the five parameters. While 'drug' is implied, to_date, from_date, recall_limit, and reaction_limit are left undefined, leaving the agent without semantic guidance for required inputs.
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's function: combining a drug's FAERS reporting profile, label warning fields, and FDA recall records for review routing. This distinguishes it from sibling tools like fda_faers_reaction_profile, fda_drug_labels, and fda_drug_recalls, which focus on individual data sources.
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 identifies the intended context ('for review routing') and explicitly lists what it is not for (validated safety comparison, causal assessment, etc.), providing clear when-not guidance. It does not name alternative tools for those cases, so it falls short of a 5.
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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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.