A
licenseNot graded
qualityB
maintenanceEnables querying FDA medical device regulatory databases including 510(k) clearances, PMA approvals, recalls, and adverse events through natural language.
5 npm
MIT
FDA Approvals
Server Details
FDA drug approvals, 510(k) device clearances, recalls and adverse-event reports.
Glama couldn't complete the latest health check. If this server requires authentication, missing or expired test credentials may be the cause. A test profile lets Glama authenticate for health checks and discover tools; it is separate from your personal connections.
If you are the author, claim ownership, then add or update a test profile under Admin → Test Profile.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
- Repository
- guptaprakhariitr/fda-approvals-mcp
- GitHub Stars
- 1
- Server Listing
- fda-approvals-mcp
TDQS
A3.7/5.0
Scored across 4 tools
Disambiguation5/5
Each tool targets a distinct FDA data domain: adverse events, drug approvals, labels, and recalls. No semantic overlap exists between them.
Naming Consistency5/5
All tools follow a consistent 'fda_<domain>_<action>' pattern, using underscores and clear descriptive names. The two drug-related tools share the 'fda_drug_' prefix.
Tool Count5/5
Four tools are well-suited for the FDA Approvals domain, covering the most common requests (approvals, labels, recalls, adverse events) without being overwhelming.
Completeness4/5
Core FDA drug information is covered, but clinical trial data or enforcement reports are missing. Agents can work around the minor gap.
Available Tools
4 toolsfda_adverse_eventsAInspect
Aggregate FDA adverse-event reports (FAERS) for a drug. Deduplicates by safetyreportid (bug fixed in 0.2.1). Returns total reports, unique reaction count, and top 20 reactions by frequency in the requested window.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| drug | Yes | Generic drug name. | |
| limit | No | ||
| date_to | No | ||
| date_from | No |
TDQS
A3.9/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It mentions deduplication and a bug fix, plus the returned metrics. This is relatively transparent, though it could note that data is aggregate and not individual reports.
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 wasted words, front-loaded with purpose. Highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and only 25% parameter schema coverage. Description explains outputs but omits parameter details. For a simple aggregate tool, it is moderately complete but could be improved by describing parameters.
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 coverage is 25% (only 'drug' described). The description adds context about requesting a time window but does not specify date format or limit parameters. It partially compensates for low schema coverage.
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 clearly states it aggregates FDA adverse-event reports (FAERS) for a drug, with specific outputs: total reports, unique reaction count, top 20 reactions. It is distinct from sibling tools (drug approval, label, recall searches).
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 implied usage is for adverse events, but no explicit guidance on when to use or alternatives. It lacks when/not-to-use directions, making it adequate but not outstanding.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fda_drug_approval_searchAInspect
Search FDA drug approvals from Drugs@FDA. Filter by sponsor, brand name, generic name, or approval date range. Returns the application number, sponsor, brand/generic name, and approval date for each approval (NDA / BLA / supplemental).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | ||
| brand | No | ||
| limit | No | ||
| date_to | No | ISO YYYY-MM-DD. | |
| generic | No | Generic ingredient, e.g. 'semaglutide'. | |
| sponsor | No | Company/sponsor name, e.g. 'Pfizer'. | |
| date_from | No | ISO YYYY-MM-DD. |
TDQS
A3.9/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the data source, filterable fields, and return types (NDA/BLA/supplemental). However, it does not mention pagination behavior (skip, limit) or rate limits. Overall, it provides decent transparency for a read-oriented search tool.
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?
The description is two sentences long with no redundant words. It front-loads the action ('Search FDA drug approvals'), then enumerates filters and return fields efficiently. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters and no output schema, the description covers main filters and returns but omits pagination details, default ordering, and handling of empty results. It is adequate for a straightforward search but lacks completeness for edge cases.
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?
The description lists filter criteria (sponsor, brand, generic, date range) which adds context to the schema's parameters. However, 57% schema coverage means some parameters (skip, limit) are not described in the schema or the narrative. The description complements but does not fully compensate for missing parameter details.
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 clearly states the tool searches FDA drug approvals from Drugs@FDA, lists specific filter criteria (sponsor, brand, generic, date range), and specifies return fields (application number, sponsor, names, approval date). It clearly differs from sibling tools (adverse events, labels, recalls) by focusing solely on approvals.
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 description implies usage for searching approvals but does not explicitly state when to use this tool over siblings (e.g., fda_adverse_events, fda_drug_label). No direct comparison or exclusions are provided, leaving the agent to infer from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fda_drug_labelBInspect
Fetch the FDA-approved drug label for a brand or generic name. Includes indications, dosage, contraindications, warnings, adverse reactions.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| drug_name | Yes | Brand or generic, e.g. 'Ozempic' or 'semaglutide'. |
TDQS
B3.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose behavioral traits like error handling, rate limits, or authentication needs, leaving gaps for a fetch operation.
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 concise sentences, front-loaded with purpose and content list, no wasted words.
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 tool with one parameter and no output schema, the description covers the main content but does not explain output format or error cases, leaving minor gaps.
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 coverage is 100% and the description for 'drug_name' in the schema already explains it; the description adds no additional meaning beyond the schema's parameter description.
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 clearly states 'Fetch the FDA-approved drug label' specifying the action and resource, and distinguishes from sibling tools like fda_adverse_events by focusing on label content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives or when not to use it; lacks context for choosing among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fda_recall_searchCInspect
Search drug, device, or food recall events. Filter by classification (Class I = most serious) and date range. Returns recall number, classification, reason, recalling firm, status.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| domain | No | drug | |
| date_to | No | ||
| date_from | No | ||
| product_class | No |
TDQS
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description must disclose behavioral traits. It mentions return fields but not important details like default domain, date format requirements, pagination limits, or rate limits. The description is minimal beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is four sentences, concise and front-loaded with the action. No redundancy, though could be slightly more structured by linking parameters explicitly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without schema descriptions, output schema, or annotations, the description is severely lacking. It does not explain parameters, default domain, date constraints, or limit behavior. Incomplete for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 5 parameters with 0% description coverage. The description only generically mentions 'classification' and 'date range' without mapping to specific parameter names, default values, or usage details. It fails to add value beyond the schema's enum labels.
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?
Description clearly states the tool searches recall events for drugs, devices, or foods. It lists filtering options and return fields, distinguishing it from sibling tools like fda_adverse_events (adverse events) and fda_drug_approval_search (drug approvals).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides general purpose but no guidance on when to use this tool versus siblings, nor any exclusion criteria. It does not mention alternatives or specific use cases, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
- First observed
fda_adverse_events - First observed
fda_drug_approval_search - First observed
fda_drug_label - First observed
fda_recall_search
Related MCP Connectors
Official FDA recalls, drug labels, adverse events and clinical trials, with cited sources.
Regulatory compliance, FDA recalls, federal register, enforcement actions & comment deadlines.
FDA medical-device regulatory intelligence from keyless openFDA datasets.
FDA 510(k)s, PMAs, recalls & trials, linked: predicate search and review-time stats for AI agents.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to access FDA and ClinicalTrials.gov data for medical device compliance, adverse event monitoring, and regulatory due diligence.-
- AlicenseAqualityDmaintenanceProvides LLM-optimized access to FDA data through the OpenFDA API and direct regulatory decision document retrieval, covering all 21 OpenFDA endpoints plus 510(k) summaries, De Novo decisions, and PMA approvals.41MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI clients to query FDA medical-device data through public keyless APIs, including 510(k) clearances, predicates, MAUDE adverse events, recalls, warning letters, PMA approvals, registrations, and UDI records, with tools for postmarket health checks and raw FDA endpoint access.MIT
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd
Glama MCP Gateway
Add one secure layer between your agents and this server.