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Kiln

by codeofaxel
OverviewSchemaRelated ServersScoreDiscussions
Python

get_printer_insights

Retrieve success rates, failure mode breakdown, and per-material statistics for a specific printer, including unresolved prints awaiting user confirmation.

Instructions

Query cross-printer learning insights for a specific printer.

        Returns success rates, failure mode breakdown, and per-material
        statistics based on previously recorded outcomes — plus any
        UNRESOLVED prints: jobs that started (or ended) while no Kiln
        process was watching, whose outcome nobody has settled yet.

        **Agent contract for ``unresolved_prints``**: these entries are
        waiting on the one witness the machine can't replace — the
        user, who has the part.  When the moment is natural (not
        mid-task), ask casually ("Your ashtray finished while Kiln
        wasn't watching — did it come out OK?") and settle the answer
        via ``record_print_outcome(job_id=..., outcome=...,
        determined_by="user_reported")``.  Never guess an outcome on
        the user's behalf; an unresolved print stays out of all
        success-rate math until someone who knows answers.

        **Note**: Insights are advisory.  They do NOT override safety limits
        or preflight checks.

        Args:
            printer_name: The printer to get insights for.
            limit: Maximum recent outcomes to include (default 20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
printer_nameYes

Tool Definition Quality

A4/5.0
Behavior4/5

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

No annotations are provided, so the description bears the full burden. It explains the behavior regarding unresolved prints: they are jobs that ended while no Kiln process was watching and require user input. It warns against guessing outcomes and states that insights are advisory, not overriding safety limits. This adds significant behavioral context beyond the schema.

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 well-structured: a clear opening sentence, bullet points for the agent contract, and a note section. It is front-loaded with the core purpose and avoids unnecessary repetition. Every sentence adds value, making it efficient for an AI agent to parse.

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 tool returning multiple data types, the description covers success rates, failure modes, per-material stats, and unresolved prints. It explains the unresolved print concept in detail. Without an output schema, it does not specify the return format, but the listed categories provide sufficient operational context. The limit parameter is addressed. Slight gap: no mention of pagination or how 'limit' interacts with statistics.

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 0%, so the description compensates by naming both parameters. 'printer_name' is described as 'The printer to get insights for' and 'limit' as 'Maximum recent outcomes to include (default 20).' While this clarifies purpose, it adds minimal detail beyond the parameter names. The description could elaborate on how 'limit' affects the returned data.

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 the verb 'Query' and the resource 'cross-printer learning insights for a specific printer.' It details the return types: success rates, failure mode breakdown, per-material statistics, and unresolved prints. This distinguishes it from sibling tools like 'troubleshoot_print_issue' which focus on troubleshooting specific issues.

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?

The description provides an 'Agent contract' for handling unresolved prints and notes that insights are advisory. However, it does not explicitly state when to use this tool versus alternatives like 'get_print_diagnostic' or 'monitor_print.' Usage is implied but lacks exclusion criteria.

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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