Zooq

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

Name	Required	Description	Default
`normalized_name`	Yes	The institution's normalized name (lowercase, hyphenated). Discover it via search_schools.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No	Example value was a string
`url`	No	Example value was a string
`name`	No	Example value was a string
`normalized_name`	No	Example 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 non-destructive behavior. The description adds beyond this the operation cost ('Costs 10 Zooq credits') and the exact return fields, which are useful operational details not present in the 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 two tight sentences: the first states purpose and outputs, the second gives the prerequisite and cost. Every sentence earns its place with no redundancy or 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?

With one required parameter, full schema coverage, and an output schema present, the description supplies everything needed to call the tool correctly: input source, return fields, workflow, and cost. No important behavioral or operational context is missing.

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 description coverage is 100%: the schema already explains normalized_name as lowercase and hyphenated and directs users to search_schools. The description reinforces the same information but does not add meaningful new parameter semantics beyond what the schema provides.

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') with a clear resource ('one institution') and states the exact output fields (school name, url, stable inst_id). It also clarifies the normalized-name input, which distinguishes this from sibling g_* lookups like g_skill_lookup and g_title_skills_lookup.

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 an explicit workflow: obtain normalized_name from search_schools first, then use this tool. It clearly implies this is a single-institution resolver rather than a search tool, though it doesn't explicitly name exclusions or alternatives beyond the search_schools precondition.

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

A3.6/5.0

Disambiguation2/5

Many tools have strongly overlapping purposes: companies_name_lookup is explicitly equivalent to search_companies, companies_enrich/companies_info/companies_universal_name_to_id all return company-profile data, and profile_full overlaps with profile_employment_history and profile_enrich. The descriptions are detailed, but an agent would still frequently have to choose between near-duplicate endpoints.

Naming Consistency4/5

Tool names mostly follow a predictable resource-prefixed snake_case pattern, such as companies_*, jobs_*, posts_*, profile_*, and search_*, which makes the set readable and groupable. Minor inconsistencies like jobs_details_v2, g_title_skills_lookup, and mixed noun suffixes (info/details/full/lookup) keep it from a perfect score.

Tool Count2/5

44 tools is well beyond the heavy 25+ band, and several tools appear to be different lookup modes or near-duplicates of the same underlying capability. The broad LinkedIn-style data domain explains much of the size, but the set still feels bloated rather than well-scoped.

Completeness4/5

The API covers the core read-only professional-data workflows well: people, companies, jobs, posts, comments, likes, email discovery/verification, schools, skills, and targeted searches. Minor gaps exist, such as some job filters being unusable and no direct exposure of certain profile alias endpoints, but agents can generally complete end-to-end workflows.

Resources

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