Aevia

Run impact assessment

lca_run_assessment

Run LCIA on a target and save the result as a durable assessment (a<N>). ref is a saved workspace system (s<N>), or an engine-catalog process (p<N>) or system (e<N>) from engine_search; it is calculated on the database it belongs to. Runs the calculation with the given parameters and returns the impact result; a run that finishes within 45 seconds returns its result directly, and a longer one returns a task_id for lca_get_task_status; the assessment is always saved into the workspace named by workspace, with no separate save step. A catalog e<N> is assessed as a one-off result and not imported as a tracked system (lca_import_system does that). nw_set names one of the method's normalization/weighting sets, applied after the calculation for a single score. lca_analyze_contributions breaks a result down. The assessment_interpretation skill (load_skill) documents what a result means and comparison discipline.

Input Schema

TableJSON Schema

Name	Required	Description
`ref`	Yes	Target ref: a saved system `s<N>`, or a catalog process `p<N>` / engine system `e<N>` from `engine_search`.
`amount`	No	How many of the target's DECLARED functional units to assess (N). The calc scales by N x the declared amount, the SAME way for every target kind — a saved `s<N>` (composed, linked, or still mirrored to its remote openLCA copy), an imported `e<N>`, or a catalog process `p<N>`. Omit it to assess exactly one declared functional unit (a catalog `p<N>`/`e<N>` records no declared FU, so its declared amount is 1 reference unit and N is the whole scalar). Must be > 0 and finite.
`nw_set`	No	Name of one of the method's normalization/weighting sets (e.g. 'EF 3.0 normalization and weighting set'), applied after the calculation: adds normalized and weighted results and a single score beside the per-category table. Omit for characterized results only; the result lists the sets the method offers.
`workspace`	Yes	Name of the workspace to work in, as `lca_get(kind='workspaces')` lists it (backticks optional). Named on every call: several conversations can share one connection, and each names its own workspace. The connection's default is often an empty sandbox rather than the user's work.
`allocation`	No	Allocation for multi-output processes. Applies to EVERY target kind — a saved `s<N>` (composed or linked) as much as a catalog `p<N>`/`e<N>`. Omit it (or pass `as_declared`) to apply each dataset's OWN declared allocation procedure, resolved per process by openLCA — the same thing openLCA desktop does, and the default here. Naming any other value overrides every dataset in the system, which is a methodological choice you must report.
`method_preference`	No	Preferred LCIA method (e.g., 'ReCiPe', 'CML'). Default: ReCiPe 2016.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 5, 2026

removedInput schema / properties / engine

Removed value: -{
-  "description": "Name of the engine database, as `lca_get(kind='engines')` lists it (case-insensitive). Omitted: this connection's default engine.",
-  "maxLength": 255,
-  "minLength": 1,
-  "type": "string"
-}

Changed1 schema field changedOct 4, 2026

addedInput schema / properties / nw_set

Added value: +{
+  "description": "Name of one of the method's normalization/weighting sets (e.g. 'EF 3.0 normalization and weighting set'), applied after the calculation: adds normalized and weighted results and a single score beside the per-category table. Omit for characterized results only; the result lists the sets the method offers.",
+  "maxLength": 200,
+  "type": "string"
+}

Changed1 schema field changedSep 28, 2026

changedInput schema / properties / workspace / description

Previous value: -"Name of the workspace to work in, as `lca_get(kind='workspaces')` lists it (backticks optional). Named on every call: several conversations can share one connection, and each names its own workspace."New value: +"Name of the workspace to work in, as `lca_get(kind='workspaces')` lists it (backticks optional). Named on every call: several conversations can share one connection, and each names its own workspace. The connection's default is often an empty sandbox rather than the user's work."

Changed2 schema fields changedSep 27, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

removedInput schema / additionalProperties
```
Removed value: -false
```

Changed3 schema fields changedSep 24, 2026

addedInput schema / properties / engine

Added value: +{
+  "description": "Name of the engine database, as `lca_get(kind='engines')` lists it (case-insensitive). Omitted: this connection's default engine.",
+  "maxLength": 255,
+  "minLength": 1,
+  "type": "string"
+}

addedInput schema / properties / workspace

Added value: +{
+  "description": "Name of the workspace to work in, as `lca_get(kind='workspaces')` lists it (backticks optional). Named on every call: several conversations can share one connection, and each names its own workspace.",
+  "maxLength": 255,
+  "minLength": 1,
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "ref"
-]New value: +[
+  "workspace",
+  "ref"
+]

First observedSep 23, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the annotations: discloses the sync/async split (runs under 45s return the result directly, longer runs return a `task_id` for `lca_get_task_status`) and the persistence semantics (the assessment is always saved into `workspace` with no separate save step). These are the behaviors an agent most needs and none are in the structured fields.

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 action and outcome, then the ref taxonomy, then async behavior, then sibling routing — a sensible priority order with no filler. The sentences are long and clause-heavy (semicolon-chained), which costs a little readability.

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?

For a complex 6-parameter mutation-style tool with no output schema, the description covers the return shape (impact result or `task_id`), persistence, target kinds, and where to go for interpretation via the `assessment_interpretation` skill. Nothing an agent needs to invoke it correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: the ref 'is calculated on the database it belongs to', `nw_set` is applied after the calculation for a single score, and the assessment lands in the named workspace. The `amount` and `allocation` parameters are left to the schema, but the added ref/nw_set semantics justify a bump.

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?

Opens with a specific verb+resource+outcome: 'Run LCIA on a target and save the result as a durable assessment (`a<N>`)'. It clearly separates this from siblings like `lca_import_system` (one-off vs tracked system) and `lca_analyze_contributions` (breakdown, not run).

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 states which ref kinds are valid (`s<N>`, `p<N>`, `e<N>` from `engine_search`) and when to use alternatives: 'a catalog `e<N>` is assessed as a one-off result and not imported as a tracked system (`lca_import_system` does that)' and '`lca_analyze_contributions` breaks a result down'. It even routes agents to `load_skill` for interpretation.

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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