Crawlora MCP

datasets_github_users_nearby

Read-only

Search enriched GitHub user profiles near a latitude and longitude, ordered by geographic distance. Costs 1 credit per record returned (minimum 2), so keep page_size to what you need. Use this when location proximity matters; results come from the stored users dataset.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "lat": "<lat>",
      "lon": "<lon>",
      "radius_m": "<radius_m>"
    },
    "name": "datasets_github_users_nearby"
  }
}

Input Schema

Name	Required	Description
`lat`	Yes	Required center latitude, from -90 through 90.
`lon`	Yes	Required center longitude, from -180 through 180.
`page`	No	Result page number, 1-based, default 1; page times page_size must not exceed 10000.
`radius_m`	Yes	Required search radius in meters, from 1 through 50000.
`page_size`	No	Page size, default 20, max 100; page times page_size must not exceed 10000.
`reachable`	No	Optional filter for any public contact channel.
`min_followers`	No	Optional minimum follower count, 0 or greater.
`influence_tier`	No	Optional follower-based tier filter. Allowed values: nano, micro, mid, macro, mega.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 5, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds genuinely useful non-schema behavior: a per-record credit cost with a minimum charge and the practical implication for choosing page_size. It stops short of detailing rate limits, result ordering guarantees at ties, or pagination bounds (those live in the schema).

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?

Three front-loaded sentences: capability, cost/constraint, then usage condition. No filler, and the most decision-relevant fact (what it does) leads.

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?

An output schema exists (so return shape need not be described) and annotations cover the safety profile. For an 8-param search tool, the description supplies the remaining decision-critical context: proximity semantics, cost model, and data provenance.

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%, so the baseline is 3, but the description adds meaning the schema does not: the cost-per-record economics tied directly to page_size and the fact that radius is applied against stored locations. That lifts it above baseline.

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?

States a specific verb ('Search') and resource ('enriched GitHub user profiles') plus the key modality ('near a latitude and longitude, ordered by geographic distance'). This cleanly separates it from the sibling datasets_github_users_search / _item / _facets tools without opening their schemas.

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?

Explicitly tells the agent when this tool is appropriate ('Use this when location proximity matters') and clarifies the data source ('results come from the stored users dataset'). It does not, however, name the alternative tools (e.g. datasets_github_users_search) for keyword-based lookups, so the routing guidance stops short of a full when/when-not contrast.

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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