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Look Up Who Gets Your Data Records

who_gets_your_data_lookup
Read-onlyIdempotent

Look up a brand, product, company or recipient, or a 5-digit ZIP code, in Who Gets Your Data, a MORVS pilot register built from FTC complaints and orders, Federal Register notices, a Supreme Court opinion, a Sacramento court order, FinCEN's Year in Review and one Geographic Targeting Order, and the California data broker registry as MORVS's Data Broker Register republishes it. Names are matched against the register's published lookup keys (case, accents, punctuation and legal-form words such as Inc. or LLC ignored); a ZIP is matched against the order's list. Returns the matching rows grouped by table, each table with its own coverage line and notes: handoffs (up to 25 edges with their full fields), broker registry answers (up to 25 brokers, each matched registration with the questions it answered yes), legal duties (a ZIP's covered-location row with the order's dates and its terms and exceptions as quoted), and "Bought by the government" (agency, sub-agency, vendor family, recipient, reseller, capability, fiscal year as FY2026 or award PIID keys, and agency abbreviations such as ICE derived from the names: each row with the lane's published obligations by fiscal year, the floor year flagged, or the award's standing), with benefits and identity panel matches in their own group. Each handoff carries its primary document's own legal status with the register's label exactly as published (a court's finding, an allegation in a complaint, a settlement with or without an admission, an order's terms, or a count of requests): an allegation is not a finding, a request is not a disclosure, and old conduct is not current practice. Each handoff gives the source organisations, product and brands, the data type in the document's words, the recipient exactly as worded (named, or the document's own recipient class), the conduct period as worded, the verbatim quote, the document's type, issuer, date and URL, and a settlement, remedy, current status or opt-out only where a document states one. An empty result returns the register's absence note: no documented record in this corpus, which says nothing about what a product or company does now. A ZIP result is a covered business location under one FinCEN Geographic Targeting Order, subject to its exceptions, and carries the register's ZIP note: it says nothing about any resident, account or transaction. Broker rows are each business's own answer to the California registry, with the question as the form words it, and carry the broker note: no broker is accused of anything. The three tables (handoffs, broker registry answers, legal duties) hold different kinds of evidence and are never added together, scored or ranked; no answer gives a total across them. The lane is a separate table: never added to the benefits and identity panel, the context (Palantir, Dataminr Defense Department licences), the excluded amounts or the handoff, broker and legal-duty tables. A pilot: a small, hand-checked set of documented handoffs, the brokers' own registry answers and one legal-duty exhibit, in three separate tables; it is not a census of data sharing. The answer carries the pilot note, lane note, coverage line, as-of date, file dates, legal-status labels, the register's rules, its licence and the source page on morvs.ai. Organisations, companies, agencies and governments only: no individual is named, and there is no bank or personal-report lookup.

Input Schema

NameRequiredDescriptionDefault
queryYesRequired brand, product, company or recipient name, or a 5-digit ZIP code, for example OnStar, Verizon or 78040; at most 80 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds substantial context beyond them: the matching normalization rules, the empty-result absence note and its meaning, per-table notes (ZIP note, broker note, pilot note), the no-summation rule across the three evidence tables, and the legal-status labeling discipline ('an allegation is not a finding, a request is not a disclosure'). This is exactly the kind of interpretive constraint an agent cannot get from structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One ~500-word paragraph. The first sentence front-loads well, but the body sprawls into restated caveats — the separateness of the tables is asserted twice ('never added together, scored or ranked' then 'The lane is a separate table: never added to...'), and the pilot disclaimer is repeated. The information is mostly substantive, but it is not structured into scannable sections, making it costly to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and only one parameter, the description carries the entire burden of describing return shape, and it does: the four row groups, per-table coverage lines and notes, the handoff field list, the legal-status labels, and the trailing metadata (as-of date, licence, source page). An agent can predict the response structure before calling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single 'query' param already documents accepted inputs with examples, so the baseline would be 3. The description earns above baseline by adding matching semantics the schema lacks: names are matched against published lookup keys with case, accents, punctuation and legal-form words ignored, while a ZIP is matched against the order's list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence names a specific verb and resource — "Look up a brand, product, company or recipient, or a 5-digit ZIP code, in Who Gets Your Data" — and immediately frames it as a MORVS pilot register built from FTC complaints, FinCEN GTOs, and the California broker registry. It is clear what the tool does; however, it never names its close siblings (who_gets_your_data_handoffs, who_gets_your_data_government, who_gets_your_data_summary), so the agent must infer the split from the table names alone.

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?

Usage is only implied: the reader infers this is the entry-point lookup, since it returns rows across all three tables and the lane. Scope exclusions are stated ("Organisations, companies, agencies and governments only... no bank or personal-report lookup"), which is useful, but there is no explicit 'use this instead of who_gets_your_data_handoffs when...' routing despite four obvious siblings.

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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