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by com.greencalculus

explain_absence

Ask why a factor is NOT in the corpus. The reasoned counterpart to coverage: where a country reports zero rows for an inventory family, this says whether that is the world's limit or our backlog. Five classifications: "structural" (no publisher issues this anywhere — stop looking, and do not silently substitute another country), "not_yet_sourced" (a publisher exists and is named; it is our backlog), "refused" (we found it and declined — the reason is stated), "held_not_counted" (we DO hold it — see we_hold for the key), "coupled" (empty only because another family is empty). Each record names the publisher checked, the route tried, a confidence and a review date, and reports stale:true once past review. These are authored judgements about what the world publishes, not values derived from our data. Call this before concluding that a gap is permanent, and before telling a user to look elsewhere.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
familyNoInventory family, e.g. "water", "wtt", "travel", "spend", "heat".
countryNoISO-3166 alpha-3 code, e.g. "can".
classificationNoFilter: structural | not_yet_sourced | refused | held_not_counted | coupled.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries full burden and thoroughly discloses behavior: it explains the five classification types, what each record contains (publisher, route, confidence, review date, stale flag), and stresses that these are 'authored judgements', not derived from data. This goes beyond typical transparency.

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?

The description is dense but every sentence contributes: it defines the tool, gives context, lists classifications with meanings, describes return record fields, and provides usage guidance. It is not overly verbose, though the classification list adds length; it remains efficiently structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description must explain what the tool returns, and it does: it enumerates the fields in each record (publisher, route, confidence, review date, stale). It also covers when to use and the data's authoritative nature, making it fully self-contained for an agent.

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

Parameters4/5

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

All three parameters are already described in the schema (100% coverage), so the baseline is 3. The description adds substantial semantic meaning to the 'classification' parameter by explaining each of its five values, and connects family/country to the zero-row scenario, enhancing understanding.

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 immediately states 'Ask why a factor is NOT in the corpus', a specific verb and resource, and distinguishes it from sibling calculation/lookup tools by focusing on explaining absence. It also positions it as the 'reasoned counterpart to coverage', making its unique scope clear.

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?

It explicitly says 'Call this before concluding that a gap is permanent, and before telling a user to look elsewhere', giving a clear when-to-use directive. It does not name specific alternative tools or provide when-not-to-use conditions, but the context is strong enough to infer.

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

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct role: search, lookup single, lookup batch, resolution, absence explanation, and separate calculation methods for distinct scopes. Even the discovery tools can be told apart by whether the input is a key, text, natural language, or a purpose. Domain calculators are cleanly separated by type of activity, so an agent should not confuse them.

Naming Consistency5/5

All tools follow a consistent verb_nonn pattern: calculate_*, lookup_factor(s), search_factors, resolve_factor, explain_absence. The naming clearly signals both action and object, and even the singular/plur lookup distinction matches the batch versus single-key semantic.

Tool Count5/5

Twelve tools is well-sopened for a broad emissons-factor API: six calculators, four discovery/lookup/resolution tools, one batch lookup, and one edge-case explainer. Each tool appears to serve a necessary purpose rather than adding redundant surface area.

Completeness5/5

The set covers the full workflow: discover factors, resolve amiguous plain-language queries, look them up individually or in batch, perform domain-relevant calculations, and even explain why a factor is absent. The main GHG scopes are covered by dedicated calculators while the generic calculate_activity fills gaps for any other factor data.

Resources