thinkhard.si prompt tools

Define an AI term

define_ai_term

Read-onlyIdempotent

Plain-English definitions of 102 AI words from the thinkhard.si glossary (token, context window, hallucination, temperature, system prompt, RAG, fine-tuning, agent, MCP and more), written for people who use AI at work rather than build it.

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": {
      "term": "<term>"
    },
    "name": "define_ai_term"
  }
}

Input Schema

Name	Required	Description	Default
`term`	Yes	The word or phrase, such as "context window" or "RAG".
`limit`	No	Optional: how many definitions to return, closest match first (1 to 5, default 3).

Output Schema

Name	Required	Description	Default
`terms`	Yes	Best match first. Empty when nothing matches.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 30, 2026

addedInput schema / properties / limit / description

Added value: +"Optional: how many definitions to return, closest match first (1 to 5, default 3)."

AddedOct 30, 2026

TDQS

A3.7/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety and side-effect profile is fully covered. The description adds real value by disclosing the fixed 102-term corpus boundary and the plain-English, non-technical style, which the annotations cannot convey. It does not say what happens for an unmatched term, which limits it to a 3.

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?

A single front-loaded sentence that puts the resource and glossary scope first. The parenthetical example list is somewhat long but earns its place by demonstrating corpus coverage and vocabulary the agent can match against.

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 two-parameter lookup with a full output schema and complete schema descriptions, the definition covers what is needed to call it correctly. Only the unknown-term fallback behavior is missing, which is a minor gap for a read-only reference tool.

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 both parameters are documented in the schema, including the 'closest match first' ordering and the 1-5 bound on limit. The description adds no parameter-level meaning beyond that, so the baseline of 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?

The description names a specific resource (plain-English definitions of 102 AI words from a named glossary) and lists concrete examples of the covered vocabulary, so an agent immediately understands this is a glossary lookup tool. It is unmistakably distinct from the prompt-library, auditing and cost-estimation siblings.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: it is clearly for defining AI terminology, and the audience framing ('people who use AI at work rather than build it') narrows intent. However, there is no explicit 'use when the user asks what a term means' trigger and no mention of when not to use it or which sibling to prefer for adjacent needs.

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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