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Costable — Parametric Should-Cost Analysis

add_process_to_model

Add a manufacturing process step to an existing cost model. Searches the process database by name query — no need for exact IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelIdYesCost model ID returned by create_cost_model.
stepOrderNoStep sequence number (1 = first). Defaults to 1.
skillLevelYesWorker skill level. Options: cnc_machinist, machine_operator, welder, composite_tech, assembly_tech, quality_inspector, toolmaker, engineer, general_labour, painter, electrician, plumber, driver, warehouse, admin, manager.
countryCodeYesCountry where this process step is performed (ISO 3166-1 alpha-2).
cycleTimeHrsYesCycle time per unit in decimal hours (e.g. 0.5 = 30 minutes).
processQueryYesProcess name to search for, e.g. 'CNC 3-Axis', 'Injection Moulding', 'TIG Welding'.

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description must carry the full transparency burden. It adds the useful behavioral detail of a name-based fuzzy search, which is beyond what the schema states. However, it doesn't mention what happens on no match or multiple matches, nor the output of the operation, which would be helpful for a mutating tool.

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?

Two sentences, each earning their place. The core action and the key differentiator (search by name) are front-loaded. No fluff or repetition.

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?

For a mutation operation with no output schema, the description covers the basic operation and search behavior. However, it omits details that would be valuable for an agent, such as return values, error conditions (e.g., model not found, no process match), or whether this modifies an existing step or appends. Given the tool's 6 parameters, a bit more context would improve completeness.

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%, so the schema already documents all six parameters. The description's mention of 'name query' aligns with processQuery but doesn't add new parameter-level semantics beyond what's in the schema. The description adds no extra insight for other parameters like modelId or stepOrder.

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 clearly states a specific action ('Add a manufacturing process step') and target ('existing cost model'). It also explains the search-by-name mechanism, which differentiates it from sibling tools like 'add_material_to_model' and 'search_processes'. This is a concise, unambiguous purpose.

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

Usage Guidelines4/5

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

The phrase 'Searches the process database by name query — no need for exact IDs' implies when this tool is appropriate (when you have a name, not an ID). While it doesn't explicitly name alternatives or exclusions, it provides enough context for an agent to avoid using ID-based tools. It stops short of a full comparison with siblings.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target a distinct action and resource: search vs. add vs. calculate vs. compare vs. review. The only mild ambiguity is between estimate_part and calculate_cost_model, but their descriptions clearly distinguish a quick natural-language estimate from running a full saved model.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using imperative verbs like create, add, calculate, compare, search, get, and review. This makes the tool surface predictable and easy to navigate.

Tool Count5/5

Ten tools is well-scoped for a should-cost analysis domain. Each tool covers a clear stage in the workflow: model creation, component/process lookup, cost calculation, country comparison, and quote review.

Completeness4/5

The core should-cost workflow is covered well: create a model, add materials and processes, calculate, compare locations, benchmark quotes, and perform quick estimates. Minor gaps exist around updating or removing model contents and around broader lifecycle management, but agents can work around these.

Resources