Rekvira: EU regulation pinpoints for compliance AI
Official EU regulation text with article and recital pinpoints over MCP. Keyless trial.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"rekvira": {
"url": "https://mcp.rekvira.com/mcp"
}
}
}Remote endpoints
https://mcp.rekvira.com/mcpstreamable-httpWhat it can do
Tool inventory
Tools (17)
🟢connect
Connect keylessly — no email, no API key. Returns trial_id for record_assessment / list_assessments and private workflow tools in this client. Call this first, then start_here or list_regulations.
Input Schema
{
"type": "object",
"properties": {},
"title": "connectArguments"
}🟢start_here
Orientation for this corpus right now — which regulations are loaded, unit counts, data_as_of, what is not held, and first-call hints per module. Generated from list_loaded(), not static prose. Cheap: one call, no quota. Then list_regulations, search_regulation, or list_workflows.
Input Schema
{
"type": "object",
"properties": {},
"title": "start_hereArguments"
}🟢whoami(trial_id)
Who is calling: tier, trial state, corpus load count. Pass trial_id from connect() to confirm the handle used for assessments and private workflows. After connect, call start_here or list_regulations.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional trial_id from connect() — echoed when present",
"title": "Trial Id"
}
},
"title": "whoamiArguments"
}🟢record_assessment(trial_id, regulation, pinpoint, verdict, reason)
Record one structured applicability verdict on a regulation pinpoint for this keyless trial_id (from connect). Verdicts: applicable | not_applicable | needs_review | deferred. Optional reason codes only — never free-text notes. Re-recording the same regulation+pinpoint overwrites. Assessments annotate; they never hide search or read results. Call after the officer states applicability.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "trial_id returned by connect()",
"title": "Trial Id"
},
"regulation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Regulation id, e.g. eu-ai-act",
"title": "Regulation"
},
"pinpoint": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Pinpoint string, e.g. Article 6(1)",
"title": "Pinpoint"
},
"verdict": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "applicable | not_applicable | needs_review | deferred",
"title": "Verdict"
},
"reason": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional structured reason: wrong_entity_type | below_threshold | already_covered_elsewhere | awaiting_vendor_answer | confirmed_obligation | revisit_after_guidance",
"title": "Reason"
}
},
"title": "record_assessmentArguments"
}🟢list_assessments(trial_id, limit)
List this trial_id's recorded assessments, newest first. Pass the trial_id from connect(). Use when resuming a multi-week DPIA or applicability review. Never invent rows for another trial.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "trial_id returned by connect()",
"title": "Trial Id"
},
"limit": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": 50,
"description": "Max rows (1–100, default 50)",
"title": "Limit"
}
},
"title": "list_assessmentsArguments"
}🟡submit_feedback(kind, message, authored_by, confirmed)
Send feedback about REKVIRA ITSELF to the operator: a bug, feature request, friction, or praise. **ASK FIRST, EVERY TIME.** You may offer to send feedback, but only the user may decide to send it. Say what you would write and set confirmed=true only after they explicitly approve it. The moment something fails is when an agent is least neutral, so never file an opinion on the user's behalf. Set authored_by='user' for their own words and 'agent_drafted' only when you wrote the draft and they approved it. Keyless trial has no account or usage history: no prior-call context is attached. Never paste query text into message.
Input Schema
{
"type": "object",
"properties": {
"kind": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "bug | feature_request | friction | praise",
"title": "Kind"
},
"message": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The explicitly approved feedback (3–2000 characters)",
"title": "Message"
},
"authored_by": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": "user",
"description": "user | agent_drafted; preserve whose words these are",
"title": "Authored By"
},
"confirmed": {
"default": false,
"description": "true only after the user explicitly approved this message",
"title": "Confirmed",
"type": "boolean"
}
},
"title": "submit_feedbackArguments"
}🟢list_regulations
List regulations in the registry and whether corpus JSON is loaded. Then search_regulation, search_regulations, or list_workflows for a named process.
Input Schema
{
"type": "object",
"properties": {},
"title": "list_regulationsArguments"
}🟢search_regulation(regulation, query, kinds)
Search held regulation text. Returns pinpoints and excerpts, not summaries. Requires regulation id (e.g. eu-ai-act) and query string. Then read_unit on the best pinpoints; verify_citation if checking someone else's cite.
Input Schema
{
"type": "object",
"properties": {
"regulation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Regulation id from list_regulations (required)",
"title": "Regulation"
},
"query": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Search terms in English (required)",
"title": "Query"
},
"kinds": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional filter: recital, article, paragraph, annex, section",
"title": "Kinds"
}
},
"title": "search_regulationArguments"
}🟢search_regulations(query, regulations, kinds)
Search all loaded regulation modules in one call. Use when the question names more than one act (e.g. DORA and the AI Act) or spans ICT risk and AI rules. Each hit carries its regulation id — then read_unit per regulation.
Input Schema
{
"type": "object",
"properties": {
"query": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Search terms in English (required)",
"title": "Query"
},
"regulations": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional subset of regulation ids; default = all loaded corpus",
"title": "Regulations"
},
"kinds": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional filter: recital, article, paragraph, annex, section",
"title": "Kinds"
}
},
"title": "search_regulationsArguments"
}🟢lookup_obligations(regulation, role, query, system_class)
Articles whose official title names a role as an obligation addressee (e.g. role='deployer', optional system_class='high-risk'). Heading index, not annotated metadata — then read_unit the article. role accepts the word a title uses (deployer, provider, importer, operator, authorised representative, controller, processor, joint controller, data subject, or supervisory authority on gdpr). notified body on eu-ai-act returns Arts 34/45. Read also_named (Arts 31 through 38) for related titles without 'obligation'. When result_count is 0, read also_named before concluding none match (e.g. Arts 22/54 on eu-ai-act authorised representative; Arts 63/94 on eu-ai-act operator; Arts 29/39 on eu-ai-act conformity assessment body; Arts 76/85 on eu-ai-act market surveillance authority; Art 100 on eu-ai-act union institution; Arts 53/55/88 on eu-ai-act GPAI provider / general-purpose AI provider / provider of general-purpose AI models; Arts 13/14/21/22 on cra manufacturer / manufacturer of products with digital elements; when also_named is empty read heading_note and use search_regulation; Arts 24/26/27/29/79 on gdpr controller; Arts 27/28/29/79 on gdpr processor; Art 26 on gdpr joint controller; Arts 12/13/14/15/34/80 on gdpr data subject; Arts 31/33/51/53/54/56/60/62/77/78 on gdpr supervisory authority; Art 32 on nis2 essential entity; Arts 3/33/34 on nis2 important entity; Arts 23/31 on dora provider; Art 31 on dora ict third-party service provider; on dora financial entity or operator also_named is empty — read heading_note and use search_regulation for body mentions (Arts 28/30 for ICT third-party context); Art 18 on cra authorised representative). Then read_unit each article — the index is not a complete duty set.
Input Schema
{
"type": "object",
"properties": {
"regulation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Regulation id from list_regulations (required)",
"title": "Regulation"
},
"role": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Addressee in the article title, e.g. deployer",
"title": "Role"
},
"query": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Alias for role when agents pass search-style query= instead of role=",
"title": "Query"
},
"system_class": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional class in the same title, e.g. high-risk",
"title": "System Class"
}
},
"title": "lookup_obligationsArguments"
}🟢verify_citation(citation, cite, quote, regulation)
Verify a citation someone else produced — does the pinpoint exist in held text, and optionally does the quote appear. Pass the cite as written (e.g. 'Article 6(1) EU AI Act') plus optional quote=. Then read_unit for full text. regulation= disambiguates when the cite omits the act name; returns regulation_mismatch when the cite embeds YYYY/NNNN for a different act.
Input Schema
{
"type": "object",
"properties": {
"citation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Citation as written, e.g. Article 6(1) EU AI Act",
"title": "Citation"
},
"cite": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Alias for citation when agents pass cite= instead of citation=",
"title": "Cite"
},
"quote": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional sentence to check against held text",
"title": "Quote"
},
"regulation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Regulation id when the cite string alone is ambiguous",
"title": "Regulation"
}
},
"title": "verify_citationArguments"
}🟢read_unit(regulation, kind, number, label, article, ...)
Read one citable unit by kind and number or label. Articles return aggregated paragraph text. Paragraphs repeat across articles — pass article= (e.g. 6 or '6') to disambiguate. number, label and article accept JSON numbers or strings. read_unit also takes a result row's pinpoint verbatim: pinpoint='Article 6(1)' resolves kind and number for you. Then verify_citation if checking someone else's pinpoint.
Input Schema
{
"type": "object",
"properties": {
"regulation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Regulation id (required)",
"title": "Regulation"
},
"kind": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "recital | article | paragraph | annex | section",
"title": "Kind"
},
"number": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Unit number within kind (string or JSON number)",
"title": "Number"
},
"label": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Exact label e.g. (47) or 1. (string or JSON number)",
"title": "Label"
},
"article": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Required for paragraphs: parent article number (e.g. 6 or '6')",
"title": "Article"
},
"pinpoint": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "A pinpoint string from a result row, e.g. 'Article 6(1)' — read instead of kind/number",
"title": "Pinpoint"
},
"cite": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Alias for pinpoint",
"title": "Cite"
},
"citation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Alias for pinpoint",
"title": "Citation"
}
},
"title": "read_unitArguments"
}🟢list_workflows(trial_id)
Playbook library for compliance-officer workflows — high-risk triage, DORA ICT review, cross-reg scans, vendor questionnaires. Read a playbook before improvising a process. Pass trial_id from connect to list your own private drafts beside the shipped library. Then call get_workflow with workflow_id from this list.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional trial_id from connect for this client private drafts",
"title": "Trial Id"
}
},
"title": "list_workflowsArguments"
}🟢get_workflow(workflow_id, id, name, trial_id)
Full step-by-step playbook for one workflow id from list_workflows. Follow the named tools (search_regulation, read_unit, verify_citation, …) rather than summarising the playbook.
Input Schema
{
"type": "object",
"properties": {
"workflow_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Id from list_workflows, e.g. high-risk-triage",
"title": "Workflow Id"
},
"id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Alias for workflow_id",
"title": "Id"
},
"name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Alias for workflow_id",
"title": "Name"
},
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional trial_id from connect to read your private draft",
"title": "Trial Id"
}
},
"title": "get_workflowArguments"
}🟡save_workflow(trial_id, workflow_id, title, summary, body)
Save or update one private compliance playbook for this keyless trial. It stays inside this trial_id; call publish_workflow only to mark it ready here, never to share it with another user.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "trial_id returned by connect()",
"title": "Trial Id"
},
"workflow_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Lowercase id for this private workflow, e.g. vendor-ai-review",
"title": "Workflow Id"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Plain title for this private playbook",
"title": "Title"
},
"summary": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "One sentence saying when this private playbook is useful",
"title": "Summary"
},
"body": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The steps and tool calls for this private playbook",
"title": "Body"
}
},
"title": "save_workflowArguments"
}🟡publish_workflow(trial_id, workflow_id, published)
Mark one of this trial_id private playbooks ready or draft. Publishing is private state only; it never adds content to the shipped library or another trial.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "trial_id returned by connect()",
"title": "Trial Id"
},
"workflow_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Id of your private workflow",
"title": "Workflow Id"
},
"published": {
"default": true,
"description": "true marks it ready in this trial; false returns it to draft",
"title": "Published",
"type": "boolean"
}
},
"title": "publish_workflowArguments"
}🔴delete_workflow(trial_id, workflow_id)
Delete one private workflow for this trial_id. It cannot delete a shipped playbook or a draft owned by another trial.
Input Schema
{
"type": "object",
"properties": {
"trial_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "trial_id returned by connect()",
"title": "Trial Id"
},
"workflow_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Id of your private workflow to delete",
"title": "Workflow Id"
}
},
"title": "delete_workflowArguments"
}Community
Evidence