Context Studios MCP

Get Featured Glossary Terms

get_featured_glossary_terms

Read-only

Get a curated list of featured AI terms.

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": "get_featured_glossary_terms"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of results to return.
`language`	No	Preferred content language. Supported values are de, en, fr, and it. Some source catalogs may fall back to de or en.	en
`responseFormat`	No	Response shape. Use v2 for agent-friendly {success,data,meta,error}; legacy remains available for older clients.	legacy

Output Schema

Name	Required	Description
`data`	No	Structured result payload. Present for responseFormat='v2' tools.
`meta`	No	Result metadata such as count, locale fallback, cache scope, or response format.
`count`	No	Number of returned records for list and search tools.
`error`	No	Human-readable error message when success is false.
`success`	No	Whether the tool call completed successfully.

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 5, 2026

TDQS

B3.3/5.0

Behavior2/5

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

Annotations already signal read-only and closed-world behavior. The description adds only 'curated,' which is practically synonymous with 'featured' in the tool name. It does not disclose what criteria define 'featured,' how language fallback works (though schema covers this), or what response shape differences exist.

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?

A single sentence that is front-loaded with the key action and resource. No filler, no redundancy, and every word contributes to meaning. It is concise and easy to parse.

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?

Given the presence of a rich input schema, annotations, and an output schema, the description is minimally adequate. However, it omits any guidance on when to choose this over the many glossary-related siblings, making it slightly incomplete for an agent operating in a large toolset.

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?

The input schema provides 100% description coverage for all three parameters, each with clear descriptions, defaults, and examples. The description itself adds no additional parameter context, so the baseline score of 3 is appropriate.

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 identifies a distinct resource: 'curated list of featured AI terms.' This clearly distinguishes it from sibling tools like search_glossary (search) or get_glossary_term (single term), as 'featured' implies a pre-selected subset.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives such as search_glossary or get_glossary_by_tech_stack. The description does not mention scenarios or exclusions, leaving the agent to infer usage from the name alone.

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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