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by tapra-ai
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Name speakers

name_speakers

Assign real names and optional colors to speaker labels from transcripts, rewrite TXT/SRT/VTT with those names, merge matching labels, and optionally burn named subtitles into the video.

Instructions

Give the speakers found by speech_to_text or transcribe_video real names (and optionally colors), then rewrite the txt, srt and vtt with those names. Each speaker keeps one subtitle color (srt colors, vtt voice styles). Labels given the same name are merged into one speaker. Free, works offline. Can also burn the named subtitles into the video.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "transcript_path": "<transcript_path>",
      "names": "<names>"
    },
    "name": "name_speakers"
  }
}

Input Schema

NameRequiredDescriptionDefault
namesYesSpeaker label to name, e.g. {"S1": "Riadh", "S2": "Host"}. Unlisted speakers keep their label.
colorsNoOptional label or name to color: #RRGGBB or a word (amber, skyblue, green, pink, violet, yellow, teal, peach, red, orange, blue, purple, white, gold).
formatsNoFiles to write. Default: all three (txt, srt, vtt). A .json with full timings is always written too.
subtitle_fontNoTahoma
burn_subtitlesNoAlso render the source video with the named, colored subtitles drawn in.
transcript_pathYesThe .json from the transcript folder (or the folder itself).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare that this is not read-only, not idempotent, and not destructive; the description adds meaningful behavior beyond that: subtitle files are rewritten, labels sharing a name are merged, colors are applied per speaker, and subtitles can optionally be burned into video. It also usefully states that the tool is free and offline.

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 compact and front-loaded, moving from purpose to color behavior, merge behavior, operational traits, and optional video burning. Every sentence adds relevant information without repetition.

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 six-parameter mutation tool with nested objects and no output schema, the description covers the core workflow, output formats, merge semantics, color behavior, and video burning. It does not explain the exact MCP response, but for a file-rewriting tool the operational picture is sufficiently complete.

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 high at 83%, so the baseline is 3, but the description adds useful semantics not fully captured in the schema: same-name labels merge into one speaker and each speaker keeps one subtitle color. It does not deeply explain every parameter, but it compensates for the main conceptual gaps around names and colors.

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 gives a specific verb and resource: name speakers found by speech_to_text or transcribe_video, then rewrite txt/srt/vtt with those names. It clearly distinguishes itself from upstream transcription tools and sibling media utilities by describing post-processing and subtitle rewriting.

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 explicitly ties usage to speakers produced by speech_to_text or transcribe_video, which gives the agent a clear context for when this tool applies. It does not name exclusions or alternatives, but the upstream dependency is specific enough to guide selection.

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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