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datasets_github_users_nearby

Search nearby GitHub users. Searches enriched GitHub users near a coordinate, sorted by distance, in dataset id enum value github-users. influence_tier enum: nano, micro, mid, macro, mega.

Instructions

Search nearby GitHub users. Searches enriched GitHub users near a coordinate, sorted by distance, in dataset id enum value github-users. influence_tier enum: nano, micro, mid, macro, mega. Billing: this endpoint costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude
lonYesLongitude
pageNoPage number, defaults to 1
radius_mYesRadius in meters, max 50000
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
reachableNoFilter by any public contact channel
min_followersNoMinimum follower count
influence_tierNoFollower-tier enum: nano, micro, mid, macro, mega

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / influence_tier / enum
      Added value: +[
+  "nano",
+  "micro",
+  "mid",
+  "macro",
+  "mega"
+]
  2. Addedv1.2.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose a real behavioral trait: credit-based billing with a per-record cost, a 2-credit minimum, and concrete examples (empty = 2, full page of 100 = 100). It also notes distance sorting. It omits auth/permission requirements and rate-limit behavior, so it is partial rather than complete.

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?

Three sentences, front-loaded with the core purpose before secondary details (dataset enum, tier enum, billing). The enum restatement is mildly redundant with the schema, but the billing sentence earns its place and nothing is bloated.

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?

For a geo-search tool with no output schema, the description covers purpose, ordering, filter enums, and cost, which is most of what an agent needs to invoke it correctly. It leaves result-shape and pagination semantics to the schema, which is acceptable given there is no output schema to lean on.

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%, so the schema already documents all eight parameters, and the baseline is 3. The description restates the influence_tier enum values, which the schema already lists, and adds no syntax or format detail beyond that, so it does not meaningfully exceed the schema.

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 states a specific verb and resource ('Search nearby GitHub users') and adds scope ('near a coordinate, sorted by distance'), which is enough to tell it apart from a keyword-based sibling like datasets_github_users_search. It stops short of naming the sibling it is not, so it lands just below the top tier.

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?

Usage is implied by 'nearby ... near a coordinate,' which suggests this tool is for proximity queries rather than attribute search. However, there is no explicit when-to-use vs when-not, and no mention of alternatives such as datasets_github_users_search, so the agent must infer the routing.

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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