thinkhard.si prompt tools

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search_prompts

Read-onlyIdempotent

Find ready-made, expert prompt templates for a work task in the thinkhard.si repository (300+ prompts for email, meetings, presentations, job search, marketing, sales, support, HR, finance, legal, spreadsheets, research and more). Use it whenever the user wants to write, plan, summarize, analyze or draft something at work, before writing a prompt from scratch. Then call get_prompt with the slug.

Input Schema

Name	Required	Description
`limit`	No
`model`	No	Optional: only prompts laid out for this kind of model.
`query`	Yes	What the user wants to do, in plain words, for example "cover letter" or "summarize a long contract". Everyday words, abbreviations (CV, 1:1, P&L, SOP) and typos are understood, and results come best match first.
`collection`	No	Optional: limit to one collection (see list_collections).

Output Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 24, 2026

changedInput schema / properties / query / description

Previous value: -"What the user wants to do, in plain words, for example \"cover letter\" or \"summarize a long contract\"."New value: +"What the user wants to do, in plain words, for example \"cover letter\" or \"summarize a long contract\". Everyday words, abbreviations (CV, 1:1, P&L, SOP) and typos are understood, and results come best match first."

Changed1 schema field changedOct 23, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "prompts": {
+      "description": "Best match first. Empty when nothing matches.",
+      "items": {
+        "properties": {
+          "collection": {
+            "description": "The collection it belongs to.",
+            "type": "string"
+          },
+          "id": {
+            "description": "Registry number, such as CAR-002.",
+            "type": "string"
+          },
+          "laid_out_for": {
+            "description": "The kind of model it is laid out for.",
+            "type": "string"
+          },
+          "score": {
+            "description": "Its own grade out of 10.",
+            "type": "integer"
+          },
+          "slug": {
+            "description": "Pass it to get_prompt.",
+            "type": "string"
+          },
+          "title": {
+            "type": "string"
+          },
+          "url": {
+            "description": "Its page on thinkhard.si.",
+            "type": "string"
+          },
+          "use": {
+            "description": "When to use it.",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "slug",
+          "title",
+          "use",
+          "collection",
+          "laid_out_for",
+          "score",
+          "url"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "prompts"
+  ],
+  "type": "object"
+}

First observedOct 23, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description adds genuine behavioral context beyond that: the repository's size and domain breadth, and the two-step search-then-fetch workflow with get_prompt. It doesn't discuss result ordering or limit behavior here, which keeps it at a 4.

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?

Three sentences, front-loaded with the core purpose, then domain scope, then usage trigger and next step. The parenthetical domain list is long but earns its place as retrieval vocabulary that helps the agent match user tasks.

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?

An output schema exists, so return-value explanation isn't needed, and annotations cover the safety profile. The description supplies the missing workflow linkage to get_prompt. Only minor gaps remain, such as how the limit interacts with result breadth.

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 coverage is 75% and the query, model and collection parameters already carry descriptive text (including the 'best match first' and plain-language/typo tolerance notes). The description adds no parameter-level detail the schema doesn't already provide, so the baseline 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?

States a specific verb and resource ('Find ready-made, expert prompt templates') plus the concrete repository, and quantifies scope (300+ prompts across named domains). An agent can immediately tell this apart from get_prompt, list_collections, or grade_prompt without opening a schema.

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?

Explicit trigger condition ('whenever the user wants to write, plan, summarize, analyze or draft something at work, before writing a prompt from scratch') plus an explicit next step ('Then call get_prompt with the slug'). This routes the agent through the sibling workflow with no inference required.

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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