Skip to main content
Glama
norrietaylor

io.github.norrietaylor/distillery-mcp

Official
by norrietaylor

distillery_resolve_review

Resolve entries in the review queue by approving, reclassifying, or archiving low-confidence classifications. Clears pending-review backlogs by finalizing each entry's status.

Instructions

Resolve a pending-review entry by approving, reclassifying, or archiving it.

USE WHEN: acting on entries in the review queue (entries with status=pending_review from low-confidence classifications).

PARAMS:

  • entry_id (str, required): UUID of the pending-review entry.

  • action (str, required): Resolution action. Valid: [approve, reclassify, archive].

  • new_entry_type (str, optional): Required when action="reclassify". Valid: [session, bookmark, minutes, meeting, reference, idea, inbox, github, person, project, digest, feed].

  • reviewer (str, optional): Reviewer identity for audit metadata.

RETURNS (success): { id: str, status: str, ... } (full updated entry). When the requested action is a no-op (e.g. approve on an already-active entry), the response also includes { already_in_state: true } and the entry is returned unchanged (version is NOT bumped, reviewed_at / archived_at are NOT rewritten). RETURNS (error): { error: true, code: "NOT_FOUND" | "INVALID_PARAMS" | "FORBIDDEN" | "INTERNAL", message: "..." }

RELATED: distillery_classify (to classify entries), distillery_list (with output_mode="review" to see the queue)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
entry_idYes
reviewerNo
new_entry_typeNo
Behavior5/5

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

With no annotations, the description carries the full burden, and it delivers: it details success response format, error codes, and the no-op behavior (already_in_state, version not bumped, timestamps not rewritten). It also explains the conditional requirement for new_entry_type, giving agents a complete picture of what happens.

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-organized with clear sections (USE WHEN, PARAMS, RETURNS, RELATED) and uses bullet points for readability. Every sentence carries needed information; there is no padding. The purpose is front-loaded, and the length is justified by the tool's complexity.

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 tool with 4 parameters, conditional logic, and no output schema, the description is comprehensive. It covers all parameter semantics, return shapes, error scenarios, and related tools. An agent has everything needed to invoke it correctly without additional lookup.

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?

The PARAMS section goes far beyond the bare schema. It lists valid values for action and new_entry_type, marks new_entry_type as required when action='reclassify', and describes reviewer as audit metadata. Since the schema has zero parameter descriptions, this text is essential and fully compensates.

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 and resource: 'Resolve a pending-review entry by approving, reclassifying, or archiving it.' It explicitly ties the tool to the review queue and low-confidence classifications, clearly distinguishing it from the many sibling tools that handle storage, retrieval, or classification.

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?

The 'USE WHEN' section explicitly states the condition for using this tool (entries with status=pending_review). It also provides alternatives in 'RELATED' by naming distillery_classify and distillery_list with output_mode='review', making the choice unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/norrietaylor/distillery'

If you have feedback or need assistance with the MCP directory API, please join our Discord server