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make_presenter

Turn the approved draft picture into a real talking AI presenter and save it to the user's account under a name. Only call this once the user has seen the picture and said yes to it. SPENDS 30 seconds of the user's avatar time (not video minutes) and takes a few minutes to get ready; use check_presenter. Users keep up to 5 presenters, and there is a daily limit on new ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesShort name the user will refer to the presenter by, e.g. Cedric. Up to 40 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses the cost ('SPENDS 30 seconds of the user's avatar time (not video minutes)'), the latency ('takes a few minutes to get ready'), and quota constraints (max 5 presenters, daily limit on new ones). It even clarifies the unit confusion between avatar time and video minutes, which an agent could not infer.

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 short sentences, no padding, and the required precondition plus cost are front-loaded or immediately adjacent to the action. Every sentence contributes a distinct fact (purpose, precondition, cost/latency, quota).

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 a one-parameter async creation tool with no output schema, the description covers everything needed: precondition, cost, expected latency, how to check status, and limits on how many can exist. Nothing an agent needs to call it correctly or set user expectations 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%, and the single 'name' parameter is already documented in the schema with length limits and an example. The description only restates that the presenter is saved 'under a name,' adding no syntax or format detail beyond the structured field, so baseline 3 applies.

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 ('turn the approved draft picture into a real talking AI presenter') plus the durable effect ('save it to the user's account under a name'). This clearly separates it from the sibling draw_presenter (which makes the draft) and list_presenters (which reads them back).

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?

Gives an explicit gate: 'Only call this once the user has seen the picture and said yes to it,' which tells the agent exactly when not to call it. It also routes the agent to check_presenter for readiness polling, giving a concrete alternative for the waiting period.

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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