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contribute_community_print

Submit a print outcome to the community registry. Only geometric signatures and settings are stored—never file contents, user IDs, or paths.

Instructions

Contribute a print outcome to the community registry.

        Adds an anonymous print record for community aggregation.
        Only geometric signatures and settings are stored — never
        file contents, user IDs, or file paths.

        Args:
            geometric_signature: Geometric signature from fingerprinting.
            printer_model: Printer model name.
            material: Material used.
            settings: Print settings dict.
            outcome: ``"success"``, ``"failed"``, or ``"partial"``.
            quality_grade: Grade from ``"A"`` to ``"F"`` (default ``"B"``).
            failure_mode: Optional failure description.
            print_time_seconds: Print duration in seconds.
            job_id: The print's job id when known — it anchors the
                federation dedupe key, so a print that was also
                watched (or recorded via ``record_print_outcome``)
                ships to the community pool once, not twice.
            geometric_signature_v2: ``fingerprint_model``'s
                ``geometric_signature_v2``.  Pass it: it is what keeps
                this contribution from being averaged into a different
                design that shares the older signature.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNo
outcomeYes
materialYes
settingsYes
failure_modeNo
printer_modelYes
quality_gradeNoB
print_time_secondsNo
geometric_signatureYes
geometric_signature_v2No
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden and does well: it states anonymity, exactly what is stored, what is never stored, and how job_id prevents duplicate federation contributions. It could add return behavior or error semantics, but the privacy and data-flow transparency are strong.

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?

The description is front-loaded with purpose and privacy, then uses a structured Args block. Every sentence and argument explanation serves a functional or safety-relevant purpose, with no filler or redundancy.

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

Completeness4/5

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

For a 10-parameter write tool with no annotations and no output schema, this description is quite complete: it covers purpose, privacy guarantees, dedupe, and all parameter meanings. It is only slightly incomplete in not describing the tool's response/return value or failure behavior.

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

Parameters5/5

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

Schema description coverage is 0%, and the Args section compensates fully by explaining every parameter beyond the schema. It provides outcome enum values, quality grade range and default, optionality, job_id dedupe meaning, and the geometric_signature_v2 rationale, adding substantial semantic value.

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 opens with a clear verb and resource: "Contribute a print outcome to the community registry." It immediately clarifies the anonymous aggregation purpose and differentiates from the related record_print_outcome flow by explaining the dedupe semantics around job_id.

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

Usage Guidelines3/5

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

Guidance is mostly implied rather than explicit. The dedupe note about record_print_outcome and watched prints gives context on when job_id matters, but the description never directly states when to choose this tool over record_print_outcome or other community-related tools.

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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