SiteCheck Web Intel

ShanghaiRanking Global Ranking of Academic Subjects

search_shanghai_gras_rankings

Read-only

ShanghaiRanking Global Ranking of Academic Subjects (GRAS): for a subject (55, e.g. Mathematics, Computer Science, Clinical Medicine, Economics) and year, ranked universities with rank or band, total score, the five indicator scores, country and institution URL. Filter by country or university name. Without a subject, lists the subjects and their codes. Takes about 15 s; allow a 30 s timeout. Related tools on this server, for a different need: search_shanghai_arwu_rankings when you need ShanghaiRanking ARWU 2026 world university ranking: world; search_fifa_team_rankings when you need FIFA men's national team world ranking: position, team. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

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": "search_shanghai_gras_rankings"
  }
}

Input Schema

Name	Required	Description
`year`	No	Ranking year, 2017 to 2026 (default: newest for the subject)
`limit`	No	Rows to return, 1 to 500 (default 50)
`country`	No	Optional country/region as ShanghaiRanking writes it, e.g. United States, China, Türkiye
`subject`	No	Subject name or code, e.g. Mathematics or AS0101 (omit to list subjects)
`university`	No	Optional part of a university name

Output Schema

Name	Required	Description	Default
`code`	No
`year`	No
`ranked`	No
`source`	No
`matches`	No
`subject`	No
`category`	No
`rankings`	No
`checkedAt`	No
`indicators`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses latency (~15s, allow 30s timeout) and the payment model ($0.002 USDC via x402 on Base/Solana/Arc, failed calls not charged), which materially affects how an agent should invoke it. It also describes the response shape, so the agent knows what to expect.

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 core purpose and the subject/omit-subject behavior, followed by filtering, latency, related tools, and price. The sibling-tool sentence is dense but each element still helps; nothing is redundant, though the pricing and latency tails could be slightly tighter.

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?

For a five-parameter, open-world rankings tool with an output schema, the description covers scope, alternatives, latency budget, and billing terms. Nothing an agent needs in order to call it correctly 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 every parameter (year range, limit 1-500, country, subject, university) is already documented in the schema, and the baseline of 3 applies. The description adds the count of 55 subjects and example names plus the 'rank or band' output implication, but gives no syntax detail beyond what the schema provides.

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 names a specific verb and resource ('ShanghaiRanking GRAS' ranked universities) and enumerates the returned fields: rank/band, total score, five indicator scores, country, institution URL. It also distinguishes itself from the sibling ARWU 2026 world-university ranking and the FIFA ranking, which clarifies the tool's scope.

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?

It explicitly states when to use the tool: give a subject and year to get ranked universities, filter by country or university name, and omit the subject to list subject codes. It also names the specific alternatives for different needs (search_shanghai_arwu_rankings, search_fifa_team_rankings), so the routing decision is explicit.

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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