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openl-tablets

OpenL MCP Server

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Close Project

openl_close_project
Destructive

Close a project safely by saving unsaved changes with a comment or discarding them after explicit user confirmation, preventing data loss.

Instructions

Close a project. If the project has unsaved changes (status EDITING), you must either save (saveChanges: true with comment) or discard (discardChanges: true). When discarding, ask the user for confirmation and then call again with confirmDiscard: true. Prevents accidental data loss.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentNoGit commit comment. Required if saveChanges is true. Optional if saveChanges is false or omitted.
projectIdYesProject ID returned by backend. Use the exact 'projectId' value from openl_list_projects() response without modification or reformatting.
saveChangesNoIf true, save changes before closing (requires comment). If false or omitted and project has unsaved changes, will error unless discardChanges is true.
confirmDiscardNoWhen closing with discardChanges: true and project has unsaved changes, must be set to true (explicitly) to confirm. Omitted or false will return a confirmation prompt. Ask the user first, then call again with confirmDiscard: true.
discardChangesNoIf true, close without saving (unsaved changes will be lost). When project is EDITING, you must also set confirmDiscard: true to confirm.
response_formatNoResponse format: 'json' for structured, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with contextjson

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv1.2.0
    • changedInput schema / properties / response_format / default
      Previous value: -"markdown"New value: +"json"
    • changedInput schema / properties / response_format / description
      Previous value: -"Response format: 'json' for structured data, 'markdown' for human-readable (default), 'markdown_concise' for brief summary (1-2 paragraphs), 'markdown_detailed' for full details with context"New value: +"Response format: 'json' for structured, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with context"
  2. First observedv0.0.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already mark destructiveHint=true, and the description adds concrete behavioral detail: unsaved changes trigger an error unless saved or discarded, and discarding requires explicit user confirmation. This meaningfully extends the annotation by explaining the destructive path and the safeguard. No contradiction with annotations was found.

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?

Four compact sentences front-load the core action and immediately cover the conditional close paths and the confirmation requirement. Every sentence contributes decision-relevant information, with no filler or redundant restatement of the tool name.

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 destructive closure tool, the description covers the main state-dependent branch, the user-confirmation loop, and the data-loss prevention rationale. With no output schema, return values are not described, but the fully documented input schema and workflow covers what an agent needs to call the tool correctly.

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?

Input schema descriptions cover 100% of the parameters, so the baseline is 3. The description reinforces relationships between saveChanges/comment, discardChanges/confirmDiscard, and the EDITING status, but the schema already documents these details. It adds context, though not substantial new semantic information beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource, 'Close a project,' which cleanly distinguishes it from save, open, and delete siblings. It further clarifies that closing may involve saving or discarding changes, making the action's finality clear. It does not explicitly name sibling tools for differentiation, so it misses a 5.

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?

The description gives an explicit conditional workflow: if the project is EDITING, either pass saveChanges with a comment or discard; when discarding, ask the user first and call again with confirmDiscard: true. This is clear usage context and a safety protocol. It does not explicitly contrast with alternatives like openl_save_project, so it stops short of a 5.

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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