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djOsh-03

ScreenApp Legacy MCP Bridge

by djOsh-03

Get stored ScreenApp summary

get_summary
Read-onlyIdempotent

Fetch the stored summary for a recording by providing its UUID. Read-only operation, no credits consumed.

Instructions

Read the summary ScreenApp already stored for a UUID recording. This is read-only and does not generate an answer or consume Ask AI credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileIdYesScreenApp recording/file UUID
formatNojson

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
fileIdYes
sourceYes
chaptersYes
warningsYes
availableYes
descriptionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds value by noting that it does not generate an answer and does not consume Ask AI credits, which is meaningful context beyond the annotations. It does not describe the return format, but an output schema exists to cover that.

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?

Two sentences with no redundancy. The core purpose is front-loaded, and the clarifying detail about read-only behavior and credits is concise. Every word 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 a simple read operation with an output schema present, the description is complete. It states what the tool does, clarifies it is read-only, and disambiguates from generation. No prerequisites or edge cases are left unexplained, and the sibling tools cover other recording-related operations.

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 50%: fileId has a clear description ('ScreenApp recording/file UUID'), but format lacks an explicit description. The tool description does not mention either parameter, so it adds no extra meaning. However, format is self-explanatory via its enum (json/markdown) and default, so the schema already provides sufficient guidance. The description does not compensate for the missing format description, but the gap is minor.

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 states a specific verb ('Read') and a specific resource ('summary stored for a UUID recording'), making the tool's purpose unmistakable. It also clarifies that it is read-only and does not generate an answer, which distinguishes it from potential generation-oriented siblings like get_transcript or an Ask AI feature. The title reinforces the same meaning.

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?

The description gives clear context: use this when you want the existing summary for a recording, and explicitly states that it does not generate an answer or consume Ask AI credits, so it should not be used for generation. However, it does not name specific alternative tools (e.g., get_transcript for transcripts), so the when-not is implicit rather than explicit.

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