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Import openLCA project

lca_import_project

Import an openLCA project: assess each variant and save one comparison. project is the project's name as engine_search(kind='project') lists it, or a unique part of it, in the database named by engine, else this connection's default, which must be writable. Each enabled variant's product system is imported into workspace as an s<N> (one already there is refreshed) and assessed once as an a<N> with the variant's amount, unit and allocation and the project's method and normalization/weighting set; the variants are saved as one comparison c<N>, returned as primary_ref. openLCA's API cannot calculate a project; this reproduces it variant by variant, each at its own amount (functional equivalence is not checked). Without confirm: true the call returns a preview and starts nothing. A run within 45 seconds returns its result; a longer one returns a task_id for lca_get_task_status. Refused: fewer than 2 or more than 8 enabled variants, or parameter redefinitions. Disabled variants are skipped.

Input Schema

NameRequiredDescriptionDefault
engineNoName of the engine database, as `lca_get(kind='engines')` lists it (case-insensitive). Omitted: this connection's default engine.
confirmNoRun the import: the systems, one assessment per variant and the comparison. Without it the call returns a preview of what would be imported and starts nothing.
projectYesThe openLCA project's name as `engine_search(kind='project')` lists it (backticks optional), or a unique part of it.
workspaceYesName 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: preview-only without confirm, the ~45s synchronous window versus task_id escalation to lca_get_task_status, the s<N>/a<N>/c<N> materialization and refresh semantics, per-variant functional-equivalence caveat, and who is skipped or refused. The non-idempotent, non-destructive hints are neither repeated nor contradicted.

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 purpose, then constraints, then refusal conditions — a sensible order for a complex tool. It is dense and parenthetical-heavy, but nearly every clause carries a fact an agent needs; only minor tightening is possible.

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 mutating multi-step import with no output schema, the description covers everything material: return value (primary_ref), async escape hatch, confirmation semantics, failure modes, and skip behavior. An agent can call it correctly without further probing.

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 already 100%, so the baseline is 3, but the description still contributes: project may be a unique substring, the engine default must be writable, and the workspace/element naming conventions (s<N>, a<N>, c<N>) clarify what the parameters actually produce. It does not restate syntax the schema omits nothing of.

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 precise verb+resource ('Import an openLCA project') and immediately states the composite outcome: assess each enabled variant and save one comparison. The scope (project variants, not a single product system) cleanly distinguishes it from the sibling lca_import_system.

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?

Gives strong operational context: it names engine_search(kind='project') and lca_get(kind='workspaces') for resolving parameters, states the confirm gate, and lists explicit refusal conditions (<2 or >8 enabled variants, parameter redefinitions). It never explicitly contrasts itself with lca_import_system or lca_run_assessment, so the alternative-selection guidance is implied rather than stated.

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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