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Glama

deprecate_rule

Create a pending proposal to deprecate a coding rule or lesson without changing its scope; approval after human review applies the deprecation.

Instructions

Create a proposal to deprecate a rule or lesson on its own, without changing its scope (unlike promote_rule). Accepts both RN-* and LL-* ids. Only creates a pending_proposals row (type='deprecate') — no immediate effect; approve_proposal performs the actual deprecation after human review.

Access level: analyze. Returns: the new proposal's id, as a plain string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYesWhy this rule/lesson is being deprecated.
rule_idYesThe RN-* or LL-* id of the rule/lesson to deprecate.
superseded_byNoOptional RN-*/LL-* id of the active rule/lesson that replaces this one.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that only a pending_proposals row is created, that there is 'no immediate effect', that approve_proposal is required after human review, and states the access level (analyze). It omits whether the proposal can be withdrawn or reversed (e.g., via reject_proposal), leaving a small gap for a mutation tool with zero annotation coverage.

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?

Three tightly written sentences that are front-loaded with the core action and differentiation, followed by the side-effect disclosure and return value. No filler; every sentence earns its place.

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 an unannotated mutation tool this is complete: it covers the action, differentiation from siblings, side-effect behavior, follow-up approval step, access level, and even names the return value. Nothing an agent needs to call it correctly is missing.

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 100%, so the schema already documents all three parameters including the RN-*/LL-* id format and the optional superseded_by. The description's note that it 'accepts both RN-* and LL-* ids' largely restates schema content, so baseline 3 is appropriate with the schema doing the heavy lifting.

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?

States a specific verb and resource ('Create a proposal to deprecate a rule or lesson') and immediately distinguishes itself from the sibling 'promote_rule' by noting it does not change scope. An agent can tell exactly what this tool produces and how it differs from its nearest alternative without opening either schema.

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?

Explicitly names the alternative ('unlike promote_rule') and the condition that separates them (deprecate on its own vs. changing scope). It also routes the agent through the workflow by stating that 'approve_proposal performs the actual deprecation after human review', so the when-to-use and what-happens-next path is fully clear.

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