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Glama

approve_proposal

Approve a pending proposal to write rule/lesson content or update an existing block in both the scope's Markdown file and SQLite database.

Instructions

Approve a pending proposal. For type='rule'/'lesson' this assigns a new RN-/LL- code, appends the atomic block to the scope's .md file, and inserts the corresponding row; for type='update' it splices the target's existing block in place instead. This is the single choke point that writes proposal content into both the .md file and SQLite together.

Access level: write. Returns: JSON with the resulting code, scope_id, and file location (file_path/file_offset/byte_length).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
proposal_idYesThe pending_proposals id to approve.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does so well: it discloses the dual .md + SQLite write, code assignment, in-place splicing, and declares 'Access level: write.' It stops short of stating reversibility/undo or error behavior if the proposal is not pending, so it is strong but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the action and the per-type consequences, then access level and return shape. Every sentence is informative; slightly dense but no filler.

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?

Output schema exists, yet the description usefully names the returned fields (code, scope_id, file location). Type-dependent behavior is fully covered. Minor gaps remain around failure/edge cases (e.g., approving an already-approved proposal), but it is solid for a single-param mutation.

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?

Only one parameter with 100% schema description coverage, so the schema already documents proposal_id. The description adds no naming/format detail beyond it, so the baseline 3 is appropriate.

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?

Specific verb+resource ('Approve a pending proposal') with a precise breakdown of what happens by type (rule/lesson get code assignment + block append + row insert; update splices in place). This clearly distinguishes it from siblings like reject_proposal, edit_proposal, and create_pending_proposal.

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?

Usage is implied — you approve a pending proposal, and type determines the write path. However, there is no explicit when-to-use vs alternatives guidance (e.g., when to reject vs edit vs approve, or prerequisites such as the proposal must be pending). Adequate but with a clear gap.

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