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ai_tools

Read-onlyIdempotent

AI models, apps and open-source projects, updated daily: every new model on OpenRouter (with prices per million tokens), trending Hugging Face models and apps, fast-growing GitHub AI / agent / MCP projects, and the big assistants with their newest models. query: optional words (name, use or vendor, e.g. "video", "claude", "mcp server"). category: all, llm, image, video, audio, code, writing, design, 3d, data, agents, education. sort: latest (newest releases first), trending (most used or starred) or name. limit: 1-50.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ai_tools"
  }
}

Input Schema

Name	Required	Description	Default
`sort`	No	Order: latest (newest first), trending (most used or played), name (A-Z) or rating.	latest
`limit`	No	How many results to return, 1-50. Values outside the range are clamped.
`query`	No	Optional key words: a name, use, vendor or developer. Empty lists everything.
`category`	No	all, llm, image, video, audio, code, writing, design, 3d, data, agents or education.	all

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedNov 12, 2026

addedInput schema / properties / category / description

Added value: +"all, llm, image, video, audio, code, writing, design, 3d, data, agents or education."

addedInput schema / properties / limit / description

Added value: +"How many results to return, 1-50. Values outside the range are clamped."

addedInput schema / properties / query / description

Added value: +"Optional key words: a name, use, vendor or developer. Empty lists everything."

addedInput schema / properties / sort / description

Added value: +"Order: latest (newest first), trending (most used or played), name (A-Z) or rating."

First observedNov 11, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and open-world behavior, so the safety profile is covered. The description adds genuinely new traits: the dataset refreshes daily, prices are expressed per million tokens, and results span trending/growth signals rather than a static catalog. It does not mention result caps or cost/latency, so it is not exhaustive.

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 opening sentence front-loads what the tool returns and its freshness, then the parameters are walked through compactly. It is dense and list-heavy but every clause carries information; the only waste is repeating category and sort values already present in the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema and full annotation coverage, the description does not need to explain return shapes, and it covers content scope, freshness and all four inputs. The remaining gap is disambiguation from adjacent tools in the same catalog, which an agent needs in order to pick this over software or news_search.

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 coverage is 100%, so all four parameters are already documented, and the description largely restates the category and sort vocabularies. The one real addition is query examples ("video", "claude", "mcp server"), which clarify that vendor, use and name all match. It also lists sort as latest/trending/name while the schema allows "rating," an omission that slightly muddies the semantics rather than enriching them.

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 concrete scope: "AI models, apps and open-source projects, updated daily," and enumerates the four sources (OpenRouter, Hugging Face, GitHub AI/agent/MCP projects, big assistants). That is much more specific than the name alone and separates it from siblings like software, news and research_papers by content domain. It never names a sibling explicitly, which keeps it short of a 5.

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 through the parameter walkthrough ("query: optional words...", category and sort lists), which tells an agent how to narrow a lookup. There is no explicit when-to-use or when-not-to-use guidance, and no routing hint against the closest siblings such as software or research_papers, so the agent must infer the boundary.

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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