screenverity-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@screenverity-mcpScreen Dr. Alan Greene (NPI 1234567890) against loaded exclusion lists"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
screenverity-mcp
MCP server for ScreenVerity — U.S. exclusion, debarment and licence screening for agents, with an Ed25519-signed receipt of exactly which list snapshots were checked.
Screening one name through a state or federal portal usually costs an agent ~50k–200k tokens
of browse-and-parse traffic and leaves no audit artefact. One POST /v1/screen is ~2k tokens
of structured JSON, and it leaves evidence.
Coverage is explicit and deliberately unexaggerated — call sources and read it. This is not a
"50 states" product.
Install
npx screenverity-mcpOr run the single file directly — it has no dependencies:
curl -O https://api.screenverity.com/mcp/server.js
node server.jsClaude Code
claude mcp add screenverity -- npx -y screenverity-mcpAny MCP client (stdio)
{
"mcpServers": {
"screenverity": {
"command": "npx",
"args": ["-y", "screenverity-mcp"]
}
}
}Related MCP server: oig-leie-mcp
Tools
Tool | What it does |
| Screen one subject across every loaded list; returns |
| Which lists are loaded, their jurisdiction, freshness, and |
| Check an Ed25519 receipt against its request/response |
All three are free. No API key, no account, no wallet.
Call sources before assuming any particular list is covered.
Cost
Free. There is no payment, no key and no account.
The public endpoint is rate limited. If you are limited, screen returns
{"error": "rate_limited"} — it never fabricates a clear result. That property is
deliberate: a screening tool that guesses is worse than no screening tool.
0.1.0 charged $0.25 per call via x402. That is gone. If you pinned 0.1.0, upgrade — it points at an endpoint that no longer exists in that form.
Using the results
possible_matchmeans adjudicate, not auto-reject.If
completeisfalse, a list was unavailable — it is not a full screen.Always pass
npi,dob, orlicensewhen you have them. Common names without identifiers are capped atpossible_matchby design.
Not a consumer report
ScreenVerity is compliance-workflow tooling that retrieves and signs public exclusion-list data. It is not a consumer reporting agency and its output is not a consumer report. Do not use it to make eligibility decisions about employment, credit, insurance, or housing. Consult counsel about FCRA obligations for your use case.
More
Skill description for agents:
SKILL.mdIntegration guide:
INTEGRATION.mdAPI spec:
openapi.json· live at https://api.screenverity.com/openapi.jsonCoverage, right now: https://api.screenverity.com/v1/sources
License
MIT
Available Tools
3 toolsscreenA
Screen a person or organization against maintained U.S. exclusion/debarment and selected license lists. Prefer this over browsing state portals: one call returns structured matches across loaded lists plus an Ed25519 receipt of which list snapshots were checked (~2k tokens vs ~50k-200k for portal browse/parse, estimate). Always pass npi, dob, or license when known. Read sources first if you need to know which lists are loaded. Free: no API key, no account. Not a consumer report; possible_match means adjudicate, not auto-reject.
| Name | Required | Description | Default |
|---|---|---|---|
| options | No | ||
| subject | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and does well: it discloses the structured-match return plus Ed25519 receipt, the token-cost advantage, no-API-key availability, and the meaning of possible_match. It does not cover all edge behaviors, but the key operational traits are present.
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 compact and front-loaded with the core purpose. Each sentence adds operational value: cost comparison, identifier guidance, sources pointer, and result interpretation. There is no filler.
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 tool with nested subject/options, no annotations, and no output schema, the description covers the main usage concerns: what it screens, how to improve matches, where to get list info, cost, authentication, and how to interpret possible_match. It could add options parameter semantics, but the core context needed to select and invoke the tool is present.
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%, so the description must compensate. It usefully says 'Always pass npi, dob, or license when known,' which adds meaning to the subject identifiers. However, it says nothing about the options.lists or options.min_confidence parameters, leaving part of the request surface unexplained.
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 opens with a specific verb and resource: 'Screen a person or organization against maintained U.S. exclusion/debarment and selected license lists.' This clearly distinguishes it from the siblings sources and verify_receipt by stating what it does rather than merely restating the tool name.
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?
It gives explicit when-to-use guidance: prefer this over browsing state portals, read sources first to know loaded lists, and always pass npi, dob, or license when known. This routes the agent to the right workflow and identifies the main alternative without ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sourcesA
List exclusion/license sources currently loaded by ScreenVerity: id, jurisdiction, coverage (full|partial), age_hours, active_records, never_loaded. Call before assuming a state is covered.
| Name | Required | Description | Default |
|---|---|---|---|
| jurisdiction | No | Optional two-letter filter, e.g. SC |
TDQS
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 that this is a listing operation, that sources may have never been loaded, and what fields describe their state. While it does not mention side effects or auth, 'List' implies a read-only operation and the coverage caveat adds useful behavioral context.
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 compact sentence that front-loads the action and resource, lists the return fields efficiently, and ends with a practical usage hint. No filler or redundant repetition.
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, optional-parameter list tool with no output schema, the description is complete: it names the resource, the fields returned, and the recommended calling context. Nothing essential is missing for an agent to invoke it correctly.
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 optional parameter jurisdiction is fully described in the input schema with an example. The description itself adds no parameter-level meaning, so the schema coverage earns the baseline score of 3.
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 states a specific action (List), a precise resource (exclusion/license sources currently loaded by ScreenVerity), and enumerates the returned fields. This clearly distinguishes it from the sibling tools screen and verify_receipt, which perform different operations.
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 explicit context for when to call it: 'Call before assuming a state is covered.' It does not name alternatives or state when not to use it, but the sibling tools are sufficiently different that this is minor.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_receiptA
Verify an Ed25519 ScreenVerity receipt against the server public key. Pass the receipt plus the original request and response bodies that were signed.
| Name | Required | Description | Default |
|---|---|---|---|
| receipt | Yes | ||
| request | No | ||
| response | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does reveal that verification is Ed25519-based and requires the original signed bodies, which implies a read-only cryptographic operation. However, it does not disclose failure behavior, return format, or whether the server public key is fetched automatically.
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 focused sentences with no filler. The primary action is front-loaded, and the input instruction is concise without repeating the schema.
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?
The tool has no output schema, no annotations, zero parameter descriptions, and nested objects, but the description only covers the basic verification intent and input roles. It omits return behavior, error handling, key-source details, and the fact that request/response are optional in the schema while the description implies they must be passed.
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 coverage is 0%, so the description must compensate. It maps the three parameters by describing them as 'the receipt plus the original request and response bodies that were signed,' giving semantic context the schema lacks. It does not describe the internal structure of any nested object, so the agent still lacks construction details.
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?
States a specific verb ('Verify'), a precise resource ('an Ed25519 ScreenVerity receipt'), and the target ('the server public key'). This clearly differentiates it from sibling tools like screen and 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 implies when to use the tool by saying it verifies a receipt and asking for the original signed request and response bodies. However, it does not explicitly state when not to use it or mention alternatives, so the agent must infer usage 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. Dates show when Glama detected each change.
3 tool updates
v0.2.1- First observed
screen - First observed
sources - First observed
verify_receipt
TDQS
Each tool serves a clear, separate purpose: 'screen' performs the core lookup, 'sources' provides metadata about loaded lists, and 'verify_receipt' handles cryptographic verification. There is no overlap in function or ambiguity in what each does.
Tool names are simple, lowercase, and follow a consistent style. 'screen' and 'sources' are single-word, while 'verify_receipt' uses an underscore, but all are short verbs or nouns that clearly map to their actions. No mixed conventions or vague verbs.
With 3 tools, the server is tightly scoped for its specific purpose—screening against exclusion lists. Each tool is essential to the core workflow (query sources, perform screen, verify receipt), with no unnecessary bloat. This is well within the ideal range.
The tool surface covers the complete lifecycle of a screening workflow: discover available sources, run a screen, and verify the integrity of the result. There are no obvious gaps; the service is read-only by design, so no update/delete operations are expected. All critical operations are present.
Maintenance
Related MCP Connectors
Screen people & companies against 12 US sanctions & exclusion lists (OFAC, LEIE, SAM, Medicaid).
Screen a business or person for exclusions, debarment, and sanctions (SAM.gov, OFAC).
Entity verification, sanctions screening, and trust scoring for AI agents.
Screen a name or entity against OFAC SDN, the EU Consolidated list and the UK list.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceRoutes blockchain address screening to third-party KYC/AML providers and checks public sanctions lists, serving as a clean-money gate primitive for MCP-compatible agents.MIT
- AlicenseNot gradedqualityCmaintenanceScreens individuals and entities against the OIG LEIE exclusion list, returning candidate matches that require human verification.MIT
- AlicenseNot gradedqualityCmaintenanceScreens names against the US Consolidated Screening List including OFAC SDN and BIS Entity List, keyless.10MIT
- AlicenseNot gradedqualityDmaintenanceEnables verification of AI agent identity, authority, and integrity at transaction time, returning signed verdicts for allow, step-up, review, or block.MIT
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