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Research which tools agents use

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Shows which tools AI agents use for one job — web scraping, web search, social media data, lead generation, browser automation — as four platforms that run or route agent tools count it: Apify (runs in 30 days), Monid (calls in a week, and the accounts making them), Smithery (uses of each MCP server) and Docker (pulls of each MCP server image). The answer is drawn as a card with a tab per source.

Each source counts only its own platform and in its own unit: compare them side by side, never add them up, and never call a figure a market share. Free; needs the agentrank program on (programs enable agentrank in the shell). To ask ChatGPT, Gemini and other assistants what THEY recommend, use agentrank answers in the shell (billed).

Input Schema

NameRequiredDescriptionDefault
limitNoRows per source, 3 to 20. Default 10.
topicYesThe job, in a few words: 'web scraping', 'web search', 'instagram', 'lead generation', 'browser'.
contextNoExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelNoThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds valuable behavioral context: it requires the agentrank program enabled, and it specifies interpretation rules ('compare them side by side, never add them up, and never call a figure a market share'). This goes beyond what annotations provide.

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 front-loaded with the main purpose and includes essential constraints in a logical order. It is somewhat dense but every sentence—platform details, interpretation rules, prerequisite, and alternative—earns its place.

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 tool's complexity (multi-platform data aggregation), the description covers purpose, interpretation caveats, prerequisites, and an alternative. It could mention rate limits or output format specifics, but with annotations covering safety and no output schema, it is sufficiently complete for an agent to decide to use it.

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 each parameter (topic, limit, context, llm_model) is already documented in the schema. The description adds no additional parameter-level detail beyond what the schema provides, so the baseline of 3 applies.

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 states a specific verb ('Shows which tools AI agents use') and resource ('for one job'), and enumerates the four platforms. It clearly distinguishes the tool from siblings, which are unrelated workbook and shell tools.

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?

It names an alternative tool (`agentrank answers`) for a related but different purpose (asking assistants for recommendations), and states a prerequisite ('needs the agentrank program on'). However, it does not explicitly say when not to use this tool or describe exclusions beyond that 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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