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AgentRank

by io.github.superlowburn
OverviewDiscussionsAdmin

recommend

Get quality-ranked recommendations for a use case. Returns the top tools/skills sorted by AgentRank score. Use this when a user needs a tool for a specific purpose (e.g. "I need a database MCP server", "what's good for web scraping?").

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": {
      "use_case": "<use_case>"
    },
    "name": "recommend"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoNumber of recommendations (default 5)
use_caseYesWhat the user needs, e.g. "database", "web scraping", "slack integration", "vector search"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden and does reveal the key behavior: returning a ranked list sorted by AgentRank score. However, it does not disclose data source, freshness, scoring basis details, or any side-effect/read-only guarantee beyond the implied read-only return.

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?

Two focused sentences: the first states the action and output, the second gives usage context with illustrative examples. Every sentence earns its place and the key behavior is front-loaded.

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?

For a simple recommendation query with no output schema, the description sufficiently conveys purpose, return content, and ranking order. Minor gap: it does not describe the exact return structure or any caveats, but nothing critical is missing.

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 both parameters are already documented. The description reiterates use_case with examples but adds little beyond the schema; no new parameter-level semantics are provided.

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 ('Get') and resource ('recommendations for a use case'), and clarifies the output ('top tools/skills sorted by AgentRank score'). This clearly separates it from siblings like search and lookup by emphasizing quality ranking rather than retrieval or evaluation.

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 explicitly states when to use the tool: 'when a user needs a tool for a specific purpose,' with concrete example queries. It does not explicitly list exclusions or alternatives, but the intended trigger 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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