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Propose AI Pack growth

propose_ai_pack_growth

Select a proposal from a successful run. This creates a reviewable delta and does not mutate the pack.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdYes
proposalIndexYes

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

The description explicitly states that it does not mutate the pack, which addresses a key side-effect concern. It also indicates it creates a reviewable delta, but it does not mention whether the run or proposal state is otherwise changed, or error conditions. With no annotations, this is a moderate level of transparency.

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 two short sentences with no redundant or promotional language. It efficiently conveys the operation and side-effect profile.

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 two-parameter action with no output schema or annotations, the description covers the core operation and side-effect effectively. It lacks explicit mention of the response shape or next steps, but the operation is simple enough that the description is nearly complete.

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?

The schema fields are runId and proposalIndex, and the description only implicitly references them ('successful run', 'proposal'). It does not explicitly define the UUID run identifier or the zero-based index and range semantics, though the names and schema constraints make them inferable.

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 clearly identifies the action ('Select a proposal'), the required context ('from a successful run'), and the primary outcome ('creates a reviewable delta'). It also clarifies that the pack is not mutated, making the purpose unmistakable.

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?

The description implies usage for reviewing a proposal before acceptance, but it does not explicitly contrast with sibling tools such as accept_ai_pack_growth or list_ai_pack_growth. The condition 'successful run' is a prerequisite signal, but no when-to-use or when-not-to-use guidance is given.

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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TDQS

B3/5.0
Disambiguation4/5

Most tools have clearly distinct roles across the pack lifecycle, but the growth/audition/quote cluster and multiple get_*_report/profile/readiness tools could cause an agent to pause and read descriptions carefully. The descriptions do help separate them, so the ambiguity is minor rather than severe.

Naming Consistency5/5

All tool names follow a consistent lowercase snake_case verb_noun pattern, with prefixes like list_, get_, run_, create_, update_, and upload_ used uniformly. The different noun segments reflect different resources, not inconsistent naming conventions.

Tool Count4/5

With 26 tools, the surface is slightly heavy, but it maps to a broad set of legitimate operations: drafting, publishing, auditing, running, growth, quoting, model listing, and upload/download workflows. Each tool appears purposeful, and none are obvious duplicates.

Completeness3/5

The tool set covers most major lifecycle actions, but there are notable gaps such as no delete/unpublish for packs or drafts, no simple get_ai_pack by ID, and no cancel operation for runs or auditions. These missing operations prevent full CRUD/lifecycle coverage.

Resources