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query_contacts_at_company

Resolve a company name to the canonical entity and return all contacts linked via works_at. Uses exact-normalized then fuzzy matching, is read-only, and never creates a company when no match exists.

Instructions

Answer "who do we have connected at company X": resolve company_name to the canonical company entity (exact-normalized match first, then a conservative fuzzy pass — the same resolution order used when a contact's organization field auto-links to a company at store time) and return every contact linked to it via a live works_at edge. Read-only: never creates a company entity — when no company matches, returns company: null and an empty contacts list. Optional owner_user_id scopes the search to a specific partner's network (must equal the authenticated user's id today; no cross-tenant admission yet); omit to search the authenticated user's own graph.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of linked contacts to return. Applied after the full works_at edge set is resolved, so `total_contacts` on the response reflects the true pre-limit count.
company_nameYesFree-text company name to resolve, e.g. "Northgate" or "North Gate". Matched exact-normalized first, then fuzzy (see COMPANY_FUZZY_MATCH_THRESHOLD in src/services/company_resolution.ts).
owner_user_idNoOptional user_id override scoping the query to a specific partner's network (which partner's contacts/company graph to search), matching the `user_id` override pattern on /list_relationships. Must equal the authenticated user's id today (the server has no cross-tenant admission yet); supplying a different value is rejected. When omitted, the authenticated user's own graph is searched.
Behavior5/5

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

With no annotations provided, the description fully carries the behavioral burden. It explicitly discloses read-only behavior ('never creates a company entity'), the no-match return shape ('company: null and an empty contacts list'), the owner_user_id restriction ('must equal the authenticated user's id today'), and the resolution algorithm. This is exemplary transparency.

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 a single dense paragraph, but every clause earns its place: purpose, resolution order, read-only guarantee, no-match behavior, and owner scoping. It is front-loaded with the purpose and uses a clear colon to separate the core question from details. No redundant wording.

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

Completeness4/5

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

The description covers resolution, read-only behavior, no-match scenarios, and owner override thoroughly. Since there is no output schema, it briefly mentions response elements (company:null, empty contacts, total_contacts in schema) but does not fully enumerate the response fields. Still, for a query tool of this complexity, it is largely complete.

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

Parameters3/5

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

Schema coverage is 100% for all three parameters, and the schema already explains limit, company_name matching, and owner_user_id semantics. The description adds context about resolution order and the live edge, but does not significantly enrich parameter-level meaning beyond what the schema provides. Baseline 3 is appropriate.

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 the exact question it answers ('who do we have connected at company X') and clearly states it returns every contact linked to the resolved company via a live works_at edge. This specifies the verb (query/return), resource (contacts at a company), and scope (canonical company entity), and differentiates it from sibling tools like list_relationships by focusing on company resolution.

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

Usage Guidelines4/5

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

The description gives clear context: use when you need contacts linked to a company, with optional owner scoping. It explains the resolution order (exact-normalized then fuzzy) and the read-only nature, but does not explicitly name alternative tools or state when not to use this tool. This meets 'clear context, no exclusions'.

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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