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Get top-ranked tools

get_trending
Read-only

Get the highest-ranked tools across the whole AgentIndex directory, for questions like "what are the best/most popular open-source AI tools right now". Ranking is by rank_score (0-100), which combines GitHub stars, how recently the repository was updated and the quality of its documentation; it is not a weekly-growth chart. Use search_tools instead when the user needs a specific kind of tool. Returns an array of {id, name, category, description, stars, rank_score, ai_tags, website, github_url}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of tools to return (1-50, default 10).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changed
    • addedInput schema / properties / limit / default
      Added value: +10
    • changedInput schema / properties / limit / description
      Previous value: -"Number of results (default 10, max 50)"New value: +"Number of tools to return (1-50, default 10)."
    • addedInput schema / properties / limit / maximum
      Added value: +50
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • changedInput schema / properties / limit / type
      Previous value: -"number"New value: +"integer"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "tools": {
+      "items": {
+        "properties": {
+          "ai_tags": {
+            "items": {
+              "type": "string"
+            },
+            "type": [
+              "array",
+              "null"
+            ]
+          },
+          "category": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "description": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "github_url": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "id": {
+            "description": "Tool id for get_tool_detail",
+            "type": "string"
+          },
+          "name": {
+            "type": "string"
+          },
+          "rank_score": {
+            "description": "AgentIndex rank, 0-100",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "stars": {
+            "description": "GitHub stars, e.g. \"12.3k\"",
+            "type": [
+              "string",
+              "number",
+              "null"
+            ]
+          },
+          "website": {
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "id",
+          "name"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "tools"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), so the bar is lower. The description still adds real value by explaining what rank_score measures (GitHub stars, recency, documentation quality) and what it does not measure. It does not mention pagination or result-set behavior, which is the only remaining gap.

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?

Front-loads the purpose, then qualifies usage, then explains ranking, then lists return fields. Every sentence carries information, though enumerating the return fields is somewhat redundant given the output schema exists.

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?

Coverage is complete for this tool's complexity: usage intent, alternative routing, ranking semantics, and return shape are all stated, while annotations carry the safety profile and an output schema documents the return structure.

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% and the single 'limit' parameter is fully documented in the schema (1-50, default 10). The description adds no extra meaning about the parameter, so the baseline 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?

States a specific verb and resource ('Get the highest-ranked tools across the whole AgentIndex directory') and immediately scopes it against the sibling alternatives, so an agent can distinguish it from search_tools without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives the triggering user intent ('what are the best/most popular open-source AI tools right now'), explicitly names the fallback ('Use search_tools instead when the user needs a specific kind of tool'), and clarifies a common misread ('it is not a weekly-growth chart'). When/when-not/alternative are all present.

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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