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ZOOQ - LinkedIn Data for AI Agents

g_institution_lookup

Read-onlyIdempotent

Resolve one institution by its normalized name — returns the school name, url, and stable inst_ id. Get the normalized_name from search_schools first. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
normalized_nameYesThe institution's normalized name (lowercase, hyphenated). Discover it via search_schools.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoExample value was a string
urlNoExample value was a string
nameNoExample value was a string
normalized_nameNoExample value was a string

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable context beyond annotations: the cost (10 Zooq credits) and the fact that it returns a stable institution ID, which is useful for downstream operations. This is meaningful added behavioral and practical context.

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 compact: three sentences that front-load the purpose, state the workflow dependency, and provide the cost. Every sentence earns its place, with no redundant fluff or repetition of the schema. This is an exemplary concise structure.

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 one required parameter, a fully documented schema, a rich set of annotations, and an output schema, the description covers all necessary context. It also adds the important operational detail about credit cost. An agent has everything it needs to call this tool correctly.

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?

The input schema already provides a full description of normalized_name, including format ('lowercase, hyphenated') and how to discover it ('via search_schools'). Since schema description coverage is 100%, the description adds little beyond what the schema states. It reinforces the prerequisite but does not meaningfully extend parameter semantics.

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 uses a specific verb ('Resolve') and a clear resource ('one institution by its normalized name'), and specifies the exact return fields (school name, url, stable inst_id). It implicitly distinguishes itself from search_schools by instructing the agent to obtain the normalized_name there first, leaving no ambiguity about this tool's narrow scope.

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 clearly states the prerequisite workflow: 'Get the normalized_name from search_schools first.' This tells the agent when this tool is appropriate—after search_schools has produced the normalized name. It does not explicitly list exclusions or alternative tools for when this tool should not be used, but the intended usage context is clear.

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

A3.7/5.0
Disambiguation3/5

Most tools are separated by domain prefixes and the descriptions are unusually explicit about differences, but there are direct overlaps: companies_name_lookup is the same upstream as search_companies, companies_entity_id vs companies_universal_name_to_id resolve different id spaces, and search_people/search_people_live plus search_companies/search_companies_live cover similar ground. An agent can usually pick correctly, but only after close reading.

Naming Consistency4/5

The set is consistently snake_case with readable domain prefixes like companies_, jobs_, posts_, profile_, and search_. Deviations include the unexplained g_* prefix, jobs_details_v2's version suffix, affiliate_program lacking a resource prefix, and the duplicate naming convention of companies_name_lookup vs search_companies.

Tool Count2/5

45 tools is well above the 25+ threshold and creates a heavy surface for an agent to scan. While the domains are broad, some tools are redundant (companies_name_lookup/search_companies) or tangential (affiliate_program), so the count is not fully justified.

Completeness4/5

The server covers people, companies, jobs, posts, email, schools, and skills with both search and detail endpoints, which is strong for a read-only LinkedIn API. Obvious gaps like a global post search or a company followers list are absent, but the existing paths support most workflows without dead ends.