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norrietaylor

io.github.norrietaylor/distillery-mcp

Official
by norrietaylor

distillery_classify

Persist a previously determined classification to an entry, specifying type, confidence, and optional tags/project. Handles low-confidence results by flagging for review.

Instructions

Apply a pre-computed classification to an existing entry.

USE WHEN: you have determined an entry's type and confidence via LLM or heuristic analysis and want to persist the classification result.

PARAMS:

  • entry_id (str, required): UUID of the entry to classify.

  • entry_type (str, required): Assigned type. Valid: [session, bookmark, minutes, meeting, reference, idea, inbox, github, person, project, digest, feed]. Common intuitive aliases like "note" are NOT accepted but the error response includes a details.suggestion pointing to the canonical type (e.g. "note" -> "inbox").

  • confidence (float, required): Classification confidence (0-1). Entries below the configured threshold (default 0.6) go to pending_review.

  • reasoning (str, optional): Explanation of the classification decision.

  • suggested_tags (list[str], optional): Tags to merge onto the entry.

  • suggested_project (str, optional): Project to assign if entry has none.

RETURNS (success): { id: str, entry_type: str, status: str, ... } (full updated entry) RETURNS (error): { error: true, code: "NOT_FOUND" | "INVALID_PARAMS" | "INTERNAL", message: "...", details?: { field, provided, allowed, suggestion? } }

RELATED: distillery_resolve_review (to act on pending_review entries), distillery_list (with output_mode="review" to see the review queue)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entry_idYes
reasoningNo
confidenceYes
entry_typeYes
suggested_tagsNo
suggested_projectNo
Behavior5/5

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

With no annotations provided, the description carries full behavioral disclosure. It reveals that entries below threshold (default 0.6) go to pending_review, that invalid types trigger a suggestion in the error response, and that successful calls return the full updated entry. It also explains tag merging and project assignment side effects.

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 structured with clear sections (USE WHEN, PARAMS, RETURNS, RELATED) and front-loads the core purpose. Each sentence earns its place: parameter explanations, return formats, error codes, and related tools are all information-dense with no repetition or fluff.

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?

Given 6 parameters, no output schema, and no annotations, the description is remarkably complete. It covers input semantics, success/error return structures, error codes with suggestion details, and names related tools. The only minor omission is whether existing classifications are overwritten, but this is not critical given the overall thoroughness.

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 coverage is 0%, but the description compensates fully. For entry_type it lists all valid values and explicitly notes that aliases like 'note' are rejected but the error includes a suggestion pointing to canonical type 'inbox'. For confidence it explains the threshold behavior. All six parameters are described with semantics beyond their schema types.

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 precise verb-resource pair: 'Apply a pre-computed classification to an existing entry.' It then clarifies the intended context (persisting results from LLM/heuristic analysis) and distinguishes this from sibling tools by focusing on classification persistence rather than storage, retrieval, or review.

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?

Explicit 'USE WHEN' section gives a clear precondition: when you have determined type and confidence and want to persist it. It also names related tools with their specific purposes, distillery_resolve_review for acting on pending_review entries and distillery_list for seeing the queue, effectively telling when not to use this tool.

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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