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Query Companies

query_companies
Read-only

Columns: id, display_name, domain (normalized bare host), linkedin_company_id, owner_email, data (JSONB), created_at, updated_at. Sliq's CRM is deals and tasks: to see whether a company has a deal or a task, use query_deals (status='all') / query_crm_tasks with company_id. JSONB queries: data->>'some_key' ILIKE '%...%'.

Firmographic columns (captured from company enrichment; blank/NULL until enriched): linkedin_url (bare slug), industry, description, location (HQ), logo_url, employee_count, follower_count, founded_year, company_type, annual_revenue, total_funding, latest_funding_stage.

To list the people you know at a company, take an id from here and call query_people with where_clause="company_profile_id = <id>".

Pass group_by for per-bucket counts over ALL your companies instead of a row list — a whole-set aggregate, never capped at the 200-row limit, so it answers "how many companies per industry/location" without paging. Ignores where_clause. In row mode (group_by omitted) — a dict with count, truncated, and items array (each row {id, display_name, domain, linkedin_company_id, owner_email, linkedin_url, industry, description, location, logo_url, employee_count, follower_count, founded_year, company_type, annual_revenue, total_funding, latest_funding_stage, data, created_at, updated_at}). In aggregate mode (group_by set) — a dict {group_by, groups} where groups is a list of {key, count} ordered by count descending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 50, capped at 200).
offsetNoRows to skip for paging (default 0). When the result is truncated, re-call with offset += limit for the next page.
group_byNoAggregate mode, returned instead of the row list — whole-set per-bucket counts over all your companies (not capped by `limit`), bucketed by "industry" or "location". Omit for the row list.
order_byNoSQL ORDER BY (default: created_at DESC).created_at DESC
where_clauseNoSQL WHERE condition (default: all your companies). Examples: "domain = 'stripe.com'", "display_name ILIKE '%acme%'".1=1

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / group_by
      Added value: +{
      +  "anyOf": [
      +    {
      +      "enum": [
      +        "industry",
      +        "location"
      +      ],
      +      "type": "string"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "description": "Aggregate mode, returned instead of the row list — whole-set per-bucket counts\nover all your companies (not capped by `limit`), bucketed by \"industry\" or \"location\".\nOmit for the row list."
      +}
  2. First observed

TDQS

A5/5.0
Behavior5/5

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

The annotations only provide readOnlyHint=true, so the description carries the burden of explaining behavior. It does so richly: rows accrue from multiple sources, a company may have no linked people yet, group_by ignores where_clause, and aggregate mode is not capped by limit. This goes well beyond the structured annotations.

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 every section earns its place: purpose, sibling differentiation, column inventory, JSONB example, firmographic fields, and return shape. It is well-structured with front-loaded purpose and no redundant filler.

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?

There is no output schema, but the description provides a detailed returns section describing both row mode and aggregate mode shapes. Combined with parameter explanations, CRM routing, and column documentation, it gives an agent everything needed to call and interpret results correctly.

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 still adds meaningful semantics: group_by ignores where_clause, aggregate mode runs over all companies, and JSONB query syntax is demonstrated. These nuances are not fully present in the input schema and materially help the agent construct correct calls.

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 a specific verb and resource ('Query your canonical companies (`company_profiles`)') and immediately defines the deduped nature of the entity. It clearly differentiates itself from query_monitored_companies, so an agent can pick the right tool without inspecting schemas.

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 routing: use query_monitored_companies for the LinkedIn post-monitoring list, query_deals/query_crm_tasks for CRM deal/task questions, and query_people to list people at a company. It also explains when to use group_by mode. This leaves little ambiguity about when this tool should be selected.

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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