Crypto, web, finance and app data tools

University Lookup

university_lookup

Universities worldwide by name and/or country, with official websites and email domains. Use to verify academic email addresses, fill forms or build education lists.

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": "university_lookup"
  }
}

Input Schema

Name	Required	Description	Default
`name`	No
`limit`	No
`country`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 22, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It implies a read-only lookup and reveals two useful data outputs (websites, email domains), but says nothing about matching behavior (exact vs. fuzzy name matching), pagination, or source/freshness of the records.

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 short sentences with no filler. The data scope is front-loaded and the usage hint follows immediately, so an agent can decide quickly.

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?

The output schema exists, so return values needn't be described. But with no annotations, no parameter descriptions, and an unexplained limit parameter, the definition leaves an agent guessing about matching behavior and result volume for a tool that could return large result sets.

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 0%, so the description has to compensate. The phrase 'by name and/or country' does add real meaning: it conveys that both filters are optional and combinable, which the unannotated schema does not. However, the limit parameter and default of 20 are left completely unexplained.

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 names a specific verb and resource (university lookup) plus the scope (worldwide) and what it returns (official websites and email domains). It distinguishes itself from siblings like email_validate and domain_whois, though it does so implicitly via the suggested use case rather than by naming them.

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 gives three concrete use cases (verifying academic email addresses, form filling, building education lists), which tells an agent when the tool is a fit. It does not state exclusions or name alternatives such as email_validate, so it stops short of full when/when-not guidance.

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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