Search MFG Calcs' library of 5,000+ manufacturing calculators (machining, molding, welding, OEE, cost estimating, energy, quality, maintenance, and more). Returns matching calculators with their tool slug for run_calculator. Try: {"query":"OEE"}
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, default 10 | |
| query | Yes | What to calculate, e.g. 'OEE' or 'injection molding cycle time' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It establishes the tool as a non-destructive search operation, describes that it returns matching calculators with slugs, and gives a realistic example. It does not mention pagination or empty-result handling, but those are minor for this tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with the main purpose, and every sentence serves a real end-user need. The example JSON snippet is useful without bloating the description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with only two parameters, full schema descriptions, and no output schema, the description provides enough: what is searched, the scope of search, the return payload's key fields, and a concrete query example. Nothing critical is missing for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema itself documents both query and limit. The description adds a useful example and a list of content categories but does not meaningfully extend parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool searches MFG Calcs' library of 5,000+ manufacturing calculators and lists broad categories. It also explains the key output—matching calculators with tool slugs for run_calculator—which distinguishes it from sibling tools like get_calculator and run_calculator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: this is the discovery/search entry point, and its result is meant to feed run_calculator. The example query 'OEE' gives an agent a concrete model for invocation. However, it does not explicitly state when to prefer this over get_calculator or search_site.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but several clusters overlap: ask/brief/get_index all answer questions, get_revisions/get_vintages both cover historical data, and get_provenance/get_receipts/get_citation all support verification. Descriptions clarify some boundaries, but an agent could easily misselect between ask and get_index for tariff or cost questions.
The naming pattern is largely consistent verb_noun with a strong get_ prefix (get_freshness, get_vintages, run_calculator, search_calculators). However, ask and brief break the convention as bare verbs, and lookup_tariff/optimize_sourcing use different verbs, creating minor but noticeable deviations.
17 tools is at the high end of reasonable for a broad domain covering calculators, live data series, tariffs, sourcing optimization, and verification. It feels slightly heavy but each tool has a real function, and the count is justifiable given the breadth.
The tool surface covers the full research workflow: search, lookup, calculate, optimize, verify, cite, and monitor data freshness/revisions. Minor gaps include the lack of a direct series browser (search_site covers it) and the index family being collapsed into a single get_index tool rather than exposed individually.
