query_openfdaQuery the FDA FAERS database for adverse event signals for a drug. Returns top adverse event terms, serious case count, and death count.
| Name | Required | Description | Default |
|---|---|---|---|
| drug_name | Yes | Drug name as it appears in FAERS | |
| max_results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the main behavioral outcome—returning top adverse event terms, serious case count, and death count—which is informative for a query tool. However, it omits details like matching behavior (exact vs. partial) or error handling, so it is not fully transparent.
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 a single, focused sentence (24 words) with no filler. It front-loads the purpose and immediately lists expected outputs, making it efficient and easy to scan.
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 simple two-parameter query tool, the description covers the core purpose and outputs, which partially compensates for the lack of an output schema. However, it does not explain input format expectations (e.g., exact drug name matching) or the meaning of max_results, so the description is adequate but not fully comprehensive.
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 50% with only drug_name described; max_results lacks a textual description. The tool description mentions returning 'top adverse event terms' but does not explicitly state how max_results controls the number of results. This leaves max_results semantics partly undefined, though the description adds some context beyond the schema.
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: querying the FDA FAERS database for adverse event signals. It uses a specific verb ('Query') and resource, and lists concrete outputs (top adverse event terms, serious case count, death count). This distinguishes it from sibling tools like query_chembl or query_clinicaltrials, which target different databases.
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 implies use for FDA adverse event data via the mention of FAERS, but does not explicitly state when to prefer this tool over alternatives or provide exclusion criteria. No 'use when' or alternative tool names are given, leaving the agent to infer timing from the sibling list.
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.
Several tools have overlapping responsibilities: search, search_claims, search_preprint_flags, and claidex_claim_risk_matrix all query claim/failure data, while rank_documents_by_embedding and rerank_documents both perform relevance ranking. The compatibility-oriented fetch/search tools add further confusion because their names collide with fetch_research_url and search_claims.
Names are grouped by prefixes (claidex_, query_, search_, run_) but the groups use different conventions, and bare verbs like 'fetch' and 'search' sit alongside prefixed forms like 'fetch_research_url' and 'search_claims'. The pattern is readable but not uniform.
24 tools is at the heavy end for an MCP server; while the breadth reflects many biomedical data sources and utilities, the count includes several meta/compatibility tools that could be consolidated. It is borderline but not unreasonable.
The surface covers the core biomedical workflows: searching claims, retrieving full claim content, querying failure graphs, checking preprints, and looking up drugs/trials/targets/adverse events. Minor gaps exist, such as no direct way to fetch a single clinical trial by ID beyond the search function, and no write/update operations for claims, but these are likely outside the read-only research scope.