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Opafex

by com.opafex
OverviewDiscussionsAdmin

Find what to automate first

assess_automation_opportunities
Read-onlyIdempotent

Works out what a small business should automate first, from how it runs today, instead of a generic list of AI tools. Use when an owner asks how AI can help their business, where to start, what is worth it versus hype, or how to get good people off admin. Map what the owner has said onto the options and leave out anything they have not said. Returns the best-fitting approaches with the reasons, what to check first, a small first step, and the questions still worth asking.

Input Schema

NameRequiredDescriptionDefault
wandNoWhich task would you never do again? Options: chasing = "Chasing people for information"; writing = "Writing the same document again"; answering = "Answering the same question again"; writeup = "Writing up what happened on site"; checking = "Checking somebody else's work".
sectorNoThe firm's sector. plumbing-electrical-and-other-trades = plumbers, electricians, heating engineers, window fitters and other trades working on customers' jobs; construction = contractors and construction groups bidding for projects; property = estate and letting agents; wealth-management = financial planners and IFAs. Use other if none fits.
outputsNoWhat goes out to your clients? Options: documents = "Reports, proposals and quotes"; answers = "Answers by email, chat and phone"; jobs = "Job sheets and site records"; internal = "Not much leaves the building".
retypingNoWhere does information get retyped? Options: systems = "Between two systems"; paper = "Off paper and photos"; inbox = "Out of the inbox"; none = "Nowhere obvious".
softwareNoWhich systems do you run the business on? Options: mainstream = "Microsoft or Google, plus a CRM"; industry = "An industry system and spreadsheets"; spreadsheets = "Spreadsheets and email"; bespoke = "Something built for us".
teamSizeNoNumber of people in the firm.
producersNoWho produces the work that goes out? Options: founder = "Me, mostly"; professionals = "Fee-earners, each doing their own"; admin = "An admin or operations team"; field = "Whoever is on site".
promptingNoWhat made you start looking? Options: answering = "We answer the same questions daily"; admin = "Good people are doing admin"; field = "Site work comes back on paper"; unsure = "Nobody here knows where AI fits"; specific = "No product does this one job".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / producers / description
      Previous value: -"Who produces them? Options: founder = \"Me, mostly\"; professionals = \"Fee-earners, each doing their own\"; admin = \"An admin or operations team\"; field = \"Whoever is on site\"."New value: +"Who produces the work that goes out? Options: founder = \"Me, mostly\"; professionals = \"Fee-earners, each doing their own\"; admin = \"An admin or operations team\"; field = \"Whoever is on site\"."
    • changedInput schema / properties / software / description
      Previous value: -"What do you run on? Options: mainstream = \"Microsoft or Google, plus a CRM\"; industry = \"An industry system and spreadsheets\"; spreadsheets = \"Spreadsheets and email\"; bespoke = \"Something built for us\"."New value: +"Which systems do you run the business on? Options: mainstream = \"Microsoft or Google, plus a CRM\"; industry = \"An industry system and spreadsheets\"; spreadsheets = \"Spreadsheets and email\"; bespoke = \"Something built for us\"."
    • removedInput schema / properties / volumes
      Removed value: -{
-  "description": "Roughly how many a month? Options: few = \"Fewer than 20\"; some = \"20 to 100\"; many = \"100 to 500\"; lots = \"More than 500\".",
-  "enum": [
-    "few",
-    "some",
-    "many",
-    "lots"
-  ],
-  "type": "string"
-}
    • addedInput schema / properties / wand
      Added value: +{
+  "description": "Which task would you never do again? Options: chasing = \"Chasing people for information\"; writing = \"Writing the same document again\"; answering = \"Answering the same question again\"; writeup = \"Writing up what happened on site\"; checking = \"Checking somebody else's work\".",
+  "enum": [
+    "chasing",
+    "writing",
+    "answering",
+    "writeup",
+    "checking"
+  ],
+  "type": "string"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the description needn't cover safety. It adds genuine behavioral detail beyond that: it returns best-fitting approaches with reasons, what to check first, a small first step, and remaining questions—helping the agent understand output shape even without an output schema.

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?

Front-loaded with the core purpose, then usage triggers, then return content—well-structured and dense with no filler. Slightly long but every sentence carries distinct information.

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 read-only analytical tool with 8 fully documented enum params and annotations covering safety, the description is largely complete. It describes what the output contains (approaches, reasons, checks, first step, questions), which compensates for the absent output schema. Only gap is per-parameter interpretive guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and every one of the 8 parameters has a rich enum description, so the schema already carries full parameter semantics. The description adds no parameter-level meaning at all—it doesn't explain how 'wand', 'prompting', or 'sector' map to the recommendation. Baseline would be 3 given full schema coverage, but the description's complete silence on params while claiming to 'map' inputs means it does nothing to help interpret them, so a 2.

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?

A specific verb+resource: it works out what a small business should automate first, from its current operations rather than a generic tool list. This is clearly distinct from siblings like estimate_automation_payback (calculation) and get_sector_playbook (reference).

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?

Explicitly names triggering situations (owner asks how AI can help, where to start, hype-vs-worth, getting people off admin) and instructs to map only what the owner said, excluding unstated items. This is real when-to-use guidance, though it doesn't explicitly name sibling alternatives.

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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