Skip to main content
Glama

Browse leads

browse_leads
Read-onlyIdempotent

Browse leads — rows for a shape or a saved list, or (summary=True) its counts, facets and price.

Two ways to say which records, one contract underneath:
  * a saved list — `list_id`, the 8-char id in `#browse?list=<id>`. Its states,
    filters, sort and inactive-or-holding toggle are read from the list;
    pass nothing else about the shape.
  * an inline shape — `filters` plus `state` (one state) or `states`
    (several); omit both for every live state (CO, CT, FL, NY, TX, VA).

`summary=True` returns the summary contract instead of rows — the same
numbers the buying surface shows, from the same code path: `matching`, `sellable`, `verified_one`, `verified_both`, `no_channel`, `unnamed`,
`facets`, `prices`, `quote` (present when `lane` or `cap` is given),
`exact`, `computed_at`, `quote_valid_until`, `per_state`. **Only `sellable`
— a matching record whose filing names a person — is ever billed or
delivered; never quote `matching` as a price.** name and address $0.25 per record · plus one verified phone or email $0.50 · plus both $0.70 (price rule v1; live prices always come from the summary call's `prices` block). Lanes: `all` / `best` / `contact`
(`all` = every sellable record at the name-and-address price; `best` =
each record at its own grade, verified first; `contact` = only records
with a verified phone or email). Cap: `{"type": "count|budget", "value"}`
— records for count, cents for budget. To buy, hand the same list / shape,
lane and cap to `create_checkout`.

Speak the canonical vocabulary — it is the same across every state:
`status` and `entity_type` take canonical values (`"active"`, `"LLC"`),
so `entity_type = "LLC"` matches Colorado's raw `DLLC`, Florida's `FLAL`,
and New York's spelled-out form alike; state-specific raw codes live
behind `status_raw` / `entity_type_raw` if you ever need them.
"Contacts" is the buyer's word for the records themselves — every record
names a person, so "contacts for new salons" needs no extra filter.
"Decision-makers" = filter
`contact_relevance_tier in ["Decision Maker", "Likely Decision Maker"]` —
our scored is-this-the-right-person opinion, available in EVERY state;
apply it when the buyer asks for decision-makers, never silently.
(`role_is_decision_maker: true` is the stricter, title-attested variant:
it means the state's own filing lists an authority title. Several states —
Colorado and New York among them — publish no officer titles at all, so
filtering on it there returns zero and silently drops real decision-makers.
Layer it on top only when you specifically want title-attested records.)
When a filter touches a (field, value) the requested state never populates
by design (e.g. `entity_type="SOLE_PROP"` in TX), the payload additionally
carries `zero_reasons` — machine-readable notes saying WHY the count is
zero and the nearest alternative; the key is absent otherwise. A
`formation_date` window that holds nothing explains itself the same way
on a summary: whether it ends before our earliest matching record, and
what the same filters match without the date limit.
Add `has_phone` / `has_email` for reachable ones. Worked example — active
LLC decision-makers with a phone, across all states, excluding two sectors:

    browse_leads(filters=[
        {"field": "status", "op": "eq", "value": "active"},
        {"field": "entity_type", "op": "eq", "value": "LLC"},
        {"field": "contact_relevance_tier", "op": "in",
         "value": ["Decision Maker", "Likely Decision Maker"]},
        {"field": "has_phone", "op": "eq", "value": true},
        {"field": "industry_sector", "op": "not_in",
         "value": ["Real Estate", "Finance"]},
    ])

`filing_kind` says what the state filing did. The default is Just started
(`formation`) — a business that did not exist before its filing — so a plain
call never returns an existing business that the state gave a new document
number. Widen with `include_existing=True` (every existing business with a
new filing at once) or by name: `filing_kind in ["registration", "conversion",
"name_change", "reinstatement", "address_change"]` returns existing businesses
the state published a fresh event about; `lead_class` on every row carries
the answer in the buyer's words — `Just started`, `New to <state>` (the
record's own state), `Established business, new entity`, `New trade name`,
`Back in business`, `Moved`. Never mix the two in one order — they are priced and
sold as separate lists. Closed businesses (`dissolution`) are excluded unless
named or `include_non_operating=True`. Every row also carries `last_event`
and `last_event_date` — the most recent thing the state published and the day
it published it.

Same owner: `cluster_size >= 2` is every business whose owner filed more
than one, so one call reaches the set; `cluster_code` is the record's place
in that group (`XF-O` / `XP-O` / `XM-O` an operating business, the `-V` codes
a holding company built around one). Blank on a single, so a filter on either
never matches a business with no related filing. `new_business_tier`
(`Confirmed new` … `Established`, newest first) is how sure we are the
business is genuinely new — filter on it, never sort by it; an empty result on
a fresh cohort means the score has not reached it yet.

Filter grammar (rendered from the schema — `list_filterable_fields(section="grammar")` is the full contract): a leaf is `{"field", "op", "value"}`; the top-level filters list is an implicit `and` group; group nodes `{"op": "and", "filters": [...]}` and `{"op": "or", "filters": [...]}` nest one or more children, `{"op": "not", "filters": [<one leaf or group>]}` negates exactly one. Operators by field type — text: eq, neq, in, not_in, contains, does_not_contain, exists, missing; number: eq, neq, gt, gte, lt, lte, between, in, not_in, exists, missing; date: eq, neq, gt, gte, lt, lte, between, in, not_in, exists, missing; boolean: eq, neq; geo: within. Narrower pseudo-fields — `run_manifest_id` eq; `cluster_ref` eq; `missing_stage` eq; `created_at` gt, gte, lt, lte, between; `geo_polygon` within; `geo_radius` within; `has_phone_or_email` eq; `filing_kind` eq, neq, in, not_in; `new_business_tier` eq, neq, in, not_in. `neq`, `not_in`, `does_not_contain`, `not` keep rows where the field has no value. `exists` / `missing` take no value; `in` / `not_in` take a non-empty list; `between` takes `[start, end]`, both required. A (field, op) pair outside its type's row is a 422 naming the row, never a 500. Records appear here the morning after the state posts them — speed is measured from publication, never from filing.

Args:
    state: Two-letter state code (e.g. `FL`, `CO`) for one state.
    states: Several state codes (rows or summary). Omit both `state` and
        `states` for every live state.
    list_id: A saved list id. Mutually exclusive with `state` / `states` /
        `filters` — the list already carries them.
    filters: Filter clauses in the grammar above (leaves and `and` / `or` /
        `not` groups). Use `list_filterable_fields` to discover the
        85 fields, each one's enforced operators and allowed values.
    page: 1-based page number.
    page_size: Rows per page (1–200 with a key; capped at 25 on the free
        tier, default 50).
    sort: `[{"field", "dir"}]`, one or more keys over any of the 76 sortable fields (`asc` / `desc`); a bare field name still works with `sort_dir`. Tier fields sort by rank (reachability_tier On Fire > Very Hot > Hot > Warm > Cold; contact_relevance_tier Decision Maker > Likely Decision Maker > Probable Contact > Uncertain Contact > Unlikely Decision Maker; contact_confidence_tier Verified Contact > Likely Contact > Possible Contact > Uncertain Contact; industry_confidence_tier confirmed > likely > possible > unknown; new_business_tier Confirmed new > Likely new > Uncertain > Likely established > Established); lead_ref ASC is always appended (total order). An unknown field or direction is a 422 listing the sortable fields — never a silent fallback. Default `reachability_score` descending.
    sort_dir: `asc` or `desc` (default `desc`) — used when `sort` is a bare field name.
    include_non_operating: Include inactive or holding businesses
        (default False — only the records we sell). A saved list's own
        toggle wins when `list_id` is given.
    include_existing: Include existing businesses with a new filing —
        opening a location here, formed in another state, back in
        business, a new entity, moved, a new trade name (default False —
        Just started only). Naming a `filing_kind` implies it.
    summary: Return the summary contract (counts, facets, prices, quote)
        instead of rows. Implied when `lane` or `cap` is given.
    lane: `all` / `best` / `contact` — asks the summary for a `quote`.
    cap: `{"type": "count|budget", "value": <int>}` — the dial the quote is
        solved against (records for count, cents for budget).

Returns:
    Rows: `{"items": [...], "total", "page", "page_size", "pages", "access_level",
    "_meta"}`. `_meta` is the provenance block every read carries:
    `schema_version` (the read-contract version — pin migrations to it),
    `freshness.data_refreshed_at` (when this state's data was last worked),
    `source` (public registry + derived-field attribution), `score_versions`,
    and `access_level` (preview = masked contacts, full = keyed). Keyless
    callers see `contact_name`, `email_primary`, `phone_primary` masked and may not filter the summary on them (422).
    Summary: the contract described above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
capNo
laneNo
pageNo
sortNo
stateNo
statesNo
filtersNo
list_idNo
summaryNo
sort_dirNodesc
page_sizeNo
include_existingNo
include_non_operatingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / include_existing
      Added value: +{
      +  "default": false,
      +  "title": "Include Existing",
      +  "type": "boolean"
      +}
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses billing rules, free-tier masking, 422 error behavior, freshness timing, zero_reasons, default exclusions, and sort order guarantees. It also warns that quote matching is never a price and that decision-maker filters should never be applied silently. No contradiction with annotations exists.

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?

The description is long, but it is organized into labeled sections and every block carries operational meaning. Some redundancy exists, such as repeating filing_kind examples and the same filter semantics in prose and grammar, but given the tool's complexity and zero schema coverage, the length is justified.

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?

The description covers all 13 parameters, both row and summary return contracts, error semantics, pricing, privacy restrictions, provenance metadata, and domain-specific vocabularies. It is sufficient for an agent to call the tool correctly without any additional documentation.

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

Parameters5/5

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

Schema coverage is 0%, and the description fully compensates: every parameter is explained, including the shape of cap, lane values, sort field/direction syntax, page_size limits, and the filter grammar. It even defines named values like contact_relevance_tier and canonical entity_type values, which the loose schema cannot express.

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 opening line states the exact function: browse leads rows for a shape or saved list, or a summary with counts, facets, and price. It introduces the two mutually exclusive input modes and explicitly separates row retrieval from summary retrieval, making the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives detailed when-to-use guidance: saved list vs inline shape, when to omit state filters, when summary is implied by lane/cap, and when to use include_existing vs default filing_kind. It also points to list_filterable_fields for field discovery and to create_checkout for the buying path, explicitly routing to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources