Skip to main content
Glama

generate_asset

Create a sales asset from Andru's catalog using your company context, save it to your Andru library, and retrieve it as markdown via a job ID.

Instructions

Generate an asset from Andru's catalog (find it with list_assets) — e.g. 'Board Presentation', 'Buying Committee Navigation'. Charged at its catalog price (Tool $3, Framework $12, Decision $49); your first ICP is free. Built from everything Andru knows about your company and saved to your Andru library. Takes up to a few minutes: returns a job_id — call get_asset with it to receive the asset as markdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetYesThe asset name from list_assets.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.4.1

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only say readOnlyHint=false, openWorldHint=true, destructiveHint=false. The description adds genuinely valuable behavior beyond that: per-tier pricing ($3/$12/$49), a free first ICP, an async 'up to a few minutes' duration, a job_id return, and persistence to the Andru library. Cost, latency, and persistence semantics an agent could not infer are all disclosed.

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 core action and examples, then packed with cost, latency, and return mechanics. Dense but every clause carries distinct information. Slightly congested with parenthetical pricing detail, but nothing is wasted.

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?

With no output schema, the description compensates by explaining the async job_id return and the follow-up get_asset call. Cost, latency, and save destination are all covered, so an agent has everything needed to invoke and follow through correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the sole parameter's purpose is documented in the schema. The description still adds value by clarifying the value must come from list_assets and by giving example asset names, though it doesn't specify name format or handling of invalid names.

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 ('Generate an asset from Andru's catalog') and grounds it with concrete examples ('Board Presentation', 'Buying Committee Navigation'). It also distinguishes itself from siblings by naming list_assets as the discovery path and get_asset as the retrieval path.

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?

Explicitly routes the agent: find the asset name via list_assets, and retrieve the result via get_asset using the returned job_id. This gives clear context for the generate step. It stops short of stating when NOT to generate (e.g., cost gating or prerequisites), so it isn't a full when/when-not treatment.

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