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datasets_github_users_facets

Aggregate GitHub user counts by facets like influence tier, country, company, activity, and contact reachability to analyze user distributions.

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, is_org, is_bot. influence_tier enum: nano, micro, mid, macro, mega.

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, is_org, is_bot
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
Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavioral traits. It reveals that it returns 'terms aggregation counts' and lists enums, but fails to mention important details such as pagination, maximum results, response structure, or that it only returns counts rather than full user records. Significant gaps exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively concise with two sentences and a list. It front-loads the core purpose. The list format is clear but could be slightly more structured (e.g., grouping related enums). Overall efficient with minimal waste.

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?

Given the tool has 28 parameters, no output schema, and no annotations, the description is insufficiently complete. It lacks details about response format, aggregation behavior, and how filters affect results. An agent would need to infer much. For a complex faceting tool, this is inadequate.

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?

Since schema description coverage is 100%, the baseline is 3. The description adds value by explaining that the tool returns aggregation counts and listing the facet and influence_tier enum values, which are not fully defined in the schema descriptions. However, it does not provide additional semantics for the many filter parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it facets the GitHub users dataset and returns terms aggregation counts. It lists the available facet enums and influence_tier enums, making the purpose specific. However, it does not explicitly differentiate it from similar sibling tools like datasets_github_users_search, which could cause confusion for an AI agent.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. It does not mention that this tool is for aggregations rather than searching individual users, nor does it reference any siblings. An agent would have to infer usage from the name and description alone.

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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