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proxy_locations

Read-onlyIdempotent

Discover valid geo-targeting values for a proxy plan type before calling generate_proxies: countries, states, cities, ASNs, or the full location tree (countries → regions → cities → ISPs). Use level 'tree' for Residential Premium / Mobile V2 slugs and ISP codes, or for the static datacenter gateway list; note the tree can be large.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
levelNocountries (default) | states (needs country) | cities (needs country) | asns | tree (full location tree: residentialpremium, mobile/mobile_v2, datacenter)
stateNoCities only: filter by state
countryNoCountry code, required for states/cities, optional filter for asns
planTypeYesThe plan type to look up (same value as list_proxies planType)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds genuine behavioral context beyond annotations: the tree returns a hierarchy (countries → regions → cities → ISPs), is tied to specific plan categories, and can be large. This helps the agent anticipate response size and structure.

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 sentences, front-loads the core purpose, and every clause earns its place: the value categories, the generate_proxies relationship, the tree-level guidance, and the size warning. No redundancy or filler.

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?

Given the rich schema, helpful annotations, and the tool's read-only lookup nature, the description is largely complete. It states what the tool returns, when to call it, and calls out the large-tree caveat. The absence of an output schema is partially mitigated by the description naming the returned value categories.

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 parameters. The description adds useful high-level context about level values and the tree's purpose, but it does not materially deepen parameter semantics beyond what the schema already provides. Baseline 3 is appropriate.

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 ('Discover') and a clear resource ('valid geo-targeting values for a proxy plan type'), and explicitly ties the tool to a downstream sibling ('before calling generate_proxies'). It also enumerates the exact kinds of values returned, which distinguishes it from proxy-generation and proxy-listing siblings.

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 tells the agent when to use the tool ('before calling generate_proxies') and gives concrete sub-guidance on when to choose level 'tree' for specific plan types and for the datacenter gateway list. It does not explicitly name excluded cases or alternative sibling tools, but the usage context is clear enough.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: single scrape, batch scrape, crawl, search, dataset creation, parser lifecycle, proxy management, and SEO audit. Even the five status pollers are clearly differentiated by job type and their descriptions explicitly state which job they poll, so an agent can reliably select the right tool.

Naming Consistency4/5

Most names follow a verb-first pattern (create_dataset, generate_parser, run_collector, save_parser_preset, whitelist_ip) and listing tools consistently use the 'list_' prefix. However, a few are noun-first (parser_preset_stats, proxy_locations, collector_run_status) and the status polling tool for collectors breaks the otherwise consistent '<job>_status' convention ('collector_run_status' instead of 'run_collector_status').

Tool Count3/5

At 25 tools, the set is at the upper edge of the 'heavy' range. The tools all serve distinct functions, reflecting a broad platform covering scraping, crawling, search, datasets, parsers, proxies, and SEO, but the count borders on overwhelming for an agent, and some consolidation (e.g., a generic async job status endpoint) could reduce the surface.

Completeness4/5

The tool surface covers the core data-extraction lifecycle well: discovery (map, search), acquisition (scrape, batch, crawl), structured extraction (generate_parser, save_parser_preset, parser stats/heal), proxy management, and result aggregation (datasets, collectors). Notable gaps are the absence of any cancellation/abort mechanism for long-running async jobs and no way to delete a parser preset, but these are minor for most workflows.