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Query records

well_query_records
Read-only

Query records from Well's database.

⚠️ WORKFLOW:

  1. To SHOW the user a table of a record type, just omit fields. You never choose columns for presentation: the table the user sees is ALWAYS the root's display view in the Well web app's column order, trimmed on the widest roots to what fits a chat-width table.

  2. To answer a targeted question, call well_get_schema(root) FIRST to discover available fields, then name in fields ONLY the extra values you need (5-15 typically). They are ADDED to the display view in the payload you read — they do not replace, reorder, or trim the columns the user sees.

ROOTS (read-only — all 33): companies, people, connectors, invoices, documents, transactions, accounts, payment_means, workspace_connectors, memberships, cards, checks, ledger_accounts, journals, journal_entries, tax_rates, exchange_rates, invoice_transactions, categories, account_balances, tasks, workspaces, invoice_payment_means, chat_conversations, blueprint_runs, workspace_connector_sync_logs, media, emails, phones, web_links, locations, invoice_items, billing_events (The accounting graph — ledger_accounts, journals, journal_entries — and balances/rates are read-only projections owned by the sync/posting pipelines; query them for financial context, you cannot create/update them here. Sub-resources like emails/phones/locations are usually richer when read via their parent company/person.)

CATEGORY CATALOGS: "categories" holds two independent taxonomies, separated by category_type. Always filter on it — an unfiltered read mixes them:

  • whereClause: { category_type: { _eq: "company" } } is the COMPANY-CATEGORY catalog: the industry labels a counterparty carries, and the ids well_update_company({ category_ids }) accepts. There is no curated allowlist — the labels are minted during enrichment — so read them here rather than inventing a taxonomy.

  • whereClause: { category_type: { _eq: "transaction" } } is the management/transaction taxonomy.

CONNECTED TOOLS: do NOT use this tool to show the user what they have connected — call well_list_connectors instead. It owns that job: connection status, and an install link for anything not connected yet. Query root "workspace_connectors" here only for genuine RECORD-level needs — reading sync timestamps, filtering connections, joining them with other roots. ("connectors" is the installable catalog; "workspace_connector_sync_logs" is per-sync history.)

Well already syncs the providers' data into the roots above — invoices, transactions, accounts, the accounting graph. ALWAYS read it from here. well_invoke_connector_tool and a provider's own tools are for an ACTION the user explicitly asked to take on that provider (e.g. "create this record in Attio"), never a way to fetch data Well already holds.

EXAMPLE - show the user their invoices (no fields, ever): well_query_records({ root: "invoices", limit: 50 })

EXAMPLE - answer "how much is still owed on the unpaid invoices?": well_query_records({ root: "invoices", fields: [["invoices", "balance_due"]], whereClause: { "payment_status": { "_in": ["unpaid", "partial"] } } }) // balance_due arrives in the rows for you to total up; the user still sees the // standard invoices table, with its identity, counterparty and status columns.

⚠️ RULES:

  • fields is ADDITIVE — it widens the data you receive, never the table the user sees

  • Omitting fields (default view) or naming a few extras both beat allFields

  • Field paths from schema: "invoices.issuer.name" → ["invoices", "issuer", "name"]

  • Default 50 records per request, max 500.

ONE CALL IS THE ANSWER — do not walk the root: Every response already carries totalCount (ALL matches, not just this page) and records_url (the full web-app table, with your filter and sort already applied). So a request to see a record type is ONE call: the user gets a table of the first page, the count tells them how many there are, and the link takes them to the rest. "Show me all my invoices" is answered by one call + the link — NOT by fetching 483 rows into this conversation.

  • A non-null nextCursor is NOT a to-do. It means more rows exist, which totalCount already told you and the link already covers.

  • Never paginate to compute a total, count, average or breakdown: aggregate over the filtered set instead. Summing a paginated sample produces a wrong number.

  • Never paginate to "be thorough". Large roots will exhaust the output limit mid-walk, and the user ends up with nothing legible.

  • Paginate ONLY for per-row work over every match that no aggregate can express, and tell the user the cost before starting. Then: pass the returned nextCursor as cursor; nextCursor: null is the last page.

FILTERING (whereClause):

  • Uses Hasura-style operators on field names.

  • Safe operators (work on ALL field types): _eq, _neq, _in, _nin, _is_null

  • Numeric/date only: _gt, _gte, _lt, _lte

  • Text only: _like, _ilike

  • When unsure of a field's type, prefer _eq or _in (they always work).

  • Combine with _and, _or, _not

  • For relationship fields, use nested syntax: { "issuer": { "company_id": { "_eq": "" } } }

  • NEVER select the workspace's OWN records by matching a company name. One legal entity appears under several labels — a registered name, a trade name, a bank-issued label — so a name filter silently drops rows and the total reads as complete. On the invoices root, pass partyScope instead: it resolves the workspace's own side on the server, so this query needs no id lookup and no extra call. Call well_get_own_company for the id only when a root has no partyScope and you must filter on issuer_pk / receiver_pk or the nested company_id yourself.

  • Match a counterparty by id too whenever you have one. Reach for _ilike on a name only to DISCOVER candidates to show the user, never to compute a figure you will report. Examples: { "status": { "_eq": "unpaid" } } { "grand_total": { "_gt": 1000 } } { "local_currency": { "_eq": "EUR" } } { "_and": [{ "status": { "_eq": "unpaid" } }, { "grand_total": { "_gte": 500 } }] } { "issuer": { "company_id": { "_eq": "" } } }

SORTING (orderBy):

  • Sort by any field: { field: "grand_total", direction: "desc" }

  • Default sort is by primary key ascending.

Returns { rows, totalCount, nextCursor, success }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootYesThe entity type to query — any of the 33 read-only roots (companies, people, connectors, invoices, documents, transactions, accounts, payment_means, workspace_connectors, memberships, cards, checks, ledger_accounts, journals, journal_entries, tax_rates, exchange_rates, invoice_transactions, categories, account_balances, tasks, workspaces, invoice_payment_means, chat_conversations, blueprint_runs, workspace_connector_sync_logs, media, emails, phones, web_links, locations, invoice_items, billing_events). Call well_get_schema(root) first to discover fields.
limitNoMax records to return (default 50, max 500)
cursorNoOpaque cursor for the next page. Omit for the first page, then pass nextCursor from the previous response.
fieldsNoEXTRA field paths to add to the root's display view, for values you need to reason about. Each path is an array whose first segment is the root's table name — use the paths well_get_schema(root) returns verbatim, which is the root name for every root except people (whose table is peoples); a path opening with any other segment is dropped. Additive only: they widen the payload you receive, and the columns the user sees stay the root's display view (the ones the Well web app shows) no matter what you pass here. A scalar a composite renders comes back AS that composite — asking for grand_total gets you composite_total_amount_currency, with grand_total inside it — so read `columns` for what was actually materialized. Omit unless you need a value the display view does not carry.
orderByNoSort results by a field. Example: { field: "grand_total", direction: "desc" }
allFieldsNoIf true, automatically fetches all scalar fields from schema. No need to specify fields.
partyScopeNoWhich side of an invoice the workspace itself occupies, resolved from its own company rather than a party name. `invoices` root only. "purchase" = the workspace owes it (payables); "sales" = the workspace is owed (receivables); "intra_self" = both parties are companies the workspace owns; "unattributed" = Well cannot place it on either side. The four partition every invoice, so report the "unattributed" count beside any payable total rather than dropping it — an unattributed invoice may still be owed. Prefer this over hand-writing an issuer/receiver filter.
whereClauseNoHasura-style filter object. Operators: _eq, _neq, _gt, _gte, _lt, _lte, _like, _ilike, _in, _nin, _is_null. Example: { "status": { "_eq": "unpaid" } }
workspace_idNoTarget workspace. Omit to query every authorized workspace at once; each row comes back tagged with the workspace it belongs to.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesQuery results
errorNo
columnsNoThe materialized columns in display order, with each composite substituted in place of the source fields it consumed. A row object's key order does not preserve this — the flattener appends reconstructed composites last — so a UI that wants the web app's column order must read it from here.
successYes
returnedYesNumber of rows returned
columnMetaNoPer-column field meaning, keyed by the same column paths as the rows. `context` = what the field means; `enrichment` = how the value is sourced (e.g. Bank sync, AI extraction). Only documented columns appear. Read this to interpret the returned values.
nextCursorNoCursor for the next page. null means last page.
totalCountYesTotal matching records
records_urlNoLogin-gated deep link to the FULL web-app records table for this root (real DataTable: composites, inline editing, resize/pin), carrying this call's `whereClause` and `orderBy` so it opens on the same rows. Hand it to the user for everything past this page — it is the answer to 'show me all of them', not pagination. Null when no workspace is in context or no web page serves the root.

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description strongly reinforces read-only behavior. It adds substantial behavioral detail beyond the annotations: `fields` is additive and never changes what the user sees, every response carries totalCount and records_url, nextCursor should not trigger pagination for aggregates, and same-entity name filtering can silently drop rows.

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 long but tightly structured with clear sections: WORKFLOW, ROOTS, CATEGORY CATALOGS, CONNECTED TOOLS, RULES, ONE CALL, FILTERING, SORTING. It front-loads the most important workflow guidance and every section carries operational value rather than filler or restatement of the schema.

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?

Given 9 parameters, nested objects, an output schema, and a large sibling set, the description is exceptionally complete. It covers root selection, field discovery, filtering operators, sorting, pagination semantics, return shape, anti-patterns, and sibling-tool routing, leaving no critical ambiguity for an agent selecting or invoking this tool.

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 100%, but the description adds meaning well beyond the schema. It explains field-path conversion ('invoices.issuer.name' → ['invoices', 'issuer', 'name']), safe vs type-restricted whereClause operators, the default limit of 50 and max of 500, the semantics of partyScope, and the additive behavior of `fields`.

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 opens with 'Query records from Well's database', giving a specific verb and resource, and goes further by enumerating all 33 roots. It explicitly differentiates from sibling tools: 'do NOT use this tool to show the user what they have connected — call well_list_connectors instead' and contrasts it with well_invoke_connector_tool for provider actions.

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 explicit when-to-use and when-not-to-use guidance: use it for data Well already syncs, never for provider actions, and never for showing connection status. It also prescribes a concrete workflow: call well_get_schema(root) first, then add only needed fields via `fields`, with examples for both display and targeted-answer use cases.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: CRUD for companies, people, and invoices; financial analytics (cash, cost, runway, holdings); connector management and invocation; schema discovery; querying; reconciliation; and contact channel management. No two tools could be confused for the same action.

Naming Consistency5/5

All tools follow the `well_verb_noun` pattern with consistent verb choices (create, get, list, update, delete, add, remove, run, resolve, query, invoke). The naming is predictable and makes the tool's purpose immediately clear.

Tool Count4/5

With 26 tools, the set is slightly above the ideal 3-15 range, but every tool earns its place given the breadth of the domain (CRM, invoicing, financial analytics, reconciliation, connector management). The count is well-scoped and not excessive.

Completeness4/5

The tool surface covers core CRUD, financial KPIs, reconciliation, and connector management. Minor gaps exist (e.g., no direct tool to update contact channels or manage accounts), but the query and schema tools allow agents to work around them, and the primary workflows are fully supported.

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