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datasets_github_users_facets

Aggregate GitHub user data by facets like influence tier, country, company, and activity status to analyze user distribution patterns.

Instructions

Facet the GitHub users dataset. Returns terms aggregation counts for the GitHub users dataset. Facet enum: influence_tier, type, country, country_code, state, city, domains, company, reachable, has_email, has_twitter, has_blog, active_90d, hireable, is_org, is_bot, is_suspected_automation. influence_tier enum: nano, micro, mid, macro, mega. Suspected-automation records are excluded by default unless is_suspected_automation is set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over login, name, company, bio and location, max 256 characters
latNoLatitude for radius filtering
lonNoLongitude for radius filtering
cityNoExact geocoded city filter, max 128 characters
sortNoSort enum: relevance, rank_score_desc, followers_desc, account_age_desc, account_age_asc, distance_asc
facetYesFacet enum: influence_tier, type, country, country_code, state, city, domains, company, reachable, has_email, has_twitter, has_blog, active_90d, hireable, is_org, is_bot, is_suspected_automation
loginNoExact login filter, max 128 characters
stateNoExact geocoded state filter, max 128 characters
domainNoInterest-domain tag filter, max 128 characters
is_botNoBot filter
is_orgNoOrganization filter
companyNoExact normalized-company filter, max 128 characters
countryNoExact geocoded country filter, max 128 characters
has_blogNoFilter by public blog/website presence
hireableNoFilter by the GitHub available-for-hire flag
radius_mNoRadius in meters, 1 through 50000; requires lat and lon when supplied
has_emailNoFilter by public email presence
min_reposNoMinimum public repository count
reachableNoFilter by any public contact channel
active_90dNoFilter by activity within the last 90 days
has_twitterNoFilter by public Twitter/X handle presence
country_codeNoExact ISO country-code filter, max 128 characters
max_followersNoMaximum follower count
min_followersNoMinimum follower count
influence_tierNoFollower-tier enum: nano, micro, mid, macro, mega
min_rank_scoreNoMinimum composite rank score
max_account_age_yearsNoMaximum account age in years
min_account_age_yearsNoMinimum account age in years
is_suspected_automationNoSuspected automation filter; omitted these are hidden by default
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that suspected-automation records are excluded by default unless the parameter is set, but lacks details on rate limits, authentication, output format, or other behavioral traits.

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 three concise sentences, front-loaded with purpose. Every sentence adds value, with no wasted words.

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

Completeness2/5

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

For a tool with 29 parameters and complex filtering, the description is minimal. It covers the main facet enums but lacks explanation of output structure, pagination, or other defaults, leaving gaps for an AI agent.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. The description adds value by listing the facet and influence_tier enum values and explaining the default exclusion behavior, providing context beyond the schema.

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 clearly states that it facets the GitHub users dataset and returns terms aggregation counts. It lists the specific facet enums, distinguishing it from search or item retrieval tools in the sibling list.

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

Usage Guidelines3/5

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

The description implies usage for aggregation counts but does not explicitly contrast with alternatives like datasets_github_users_search or datasets_github_users_item. No when-to-use or when-not-to-use guidance is provided.

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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