Skip to main content
Glama
nmbrthirteen

podcli

by nmbrthirteen
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

suggest_clips

Analyze podcast transcripts to identify viral moments, then submit timestamped clip suggestions with titles, context, and payoff for review in the Web UI.

Instructions

STEP 2 — Submit your clip suggestions after analyzing the transcript.

Before calling this: read the transcript via get_ui_state(include_transcript: true) and identify the best viral moments.

Every suggestion must carry its own context. A clip that opens on an answer whose question stayed behind the cut is rejected: widen start_second so the question is inside the clip.

What it does: Stores your suggestions, assigns clip numbers (#1, #2, etc.), and pushes them to the Web UI for the user to review.

After this: the user reviews in the UI. Then export with batch_create_clips(export_selected: true) or create_clip(clip_number: N).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
suggestionsYesArray of suggested clip moments

Schema Changelog

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

  1. Changed13 schema fields changedv2.7.3
    • addedInput schema / properties / suggestions / items / properties / content_type / description
      Added value: +"Content classification: guest_story, technical_insight, market_landscape, business_strategy, hot_take"
    • addedInput schema / properties / suggestions / items / properties / context_line
      Added value: +{
+  "description": "The question or setup that makes the clip land, in one line, for an editor to place. Nothing renders it yet, so it does NOT satisfy the standalone check: the clip range itself must still contain the setup.",
+  "type": "string"
+}
    • addedInput schema / properties / suggestions / items / properties / end_second / description
      Added value: +"End timestamp in seconds"
    • addedInput schema / properties / suggestions / items / properties / payoff
      Added value: +{
+  "description": "What the viewer walks away with. One sentence, second person, e.g. \"You learn why raising a seed round early cost them control of pricing.\" Not a description of the clip and not a restatement of the title.",
+  "type": "string"
+}
    • addedInput schema / properties / suggestions / items / properties / preview_text / description
      Added value: +"The first sentence or two the viewer actually hears, verbatim from start_second. This is what the standalone check reads, so it has to be the real opening line, not a paraphrase."
    • addedInput schema / properties / suggestions / items / properties / reasoning / description
      Added value: +"Why this earns 30 seconds of a stranger's attention"
    • addedInput schema / properties / suggestions / items / properties / score / description
      Added value: +"Virality score (0-20). Sum of standalone + hook + relevance + quotability (each 1-5)."
    • changedInput schema / properties / suggestions / items / properties / segments / description
      Previous value: -"Multi-cut keep-ranges. Omit for a single continuous clip."New value: +"Multi-cut keep-ranges within the clip. Use to cut out filler/tangents in the middle. Omit for a single continuous clip."
    • addedInput schema / properties / suggestions / items / properties / standalone
      Added value: +{
+  "description": "What a viewer who never heard this episode must already know to follow the clip. Write \"nothing\" when the clip carries its own setup.",
+  "type": "string"
+}
    • addedInput schema / properties / suggestions / items / properties / start_second / description
      Added value: +"Start timestamp in seconds. If the moment is an answer, move this back to include the question that prompted it."
    • addedInput schema / properties / suggestions / items / properties / suggested_caption_style / description
      Added value: +"Recommended caption style for this clip"
    • addedInput schema / properties / suggestions / items / properties / title / description
      Added value: +"Short catchy title for the clip"
    • changedInput schema / properties / suggestions / items / required
      Previous value: -[
-  "title",
-  "start_second",
-  "end_second",
-  "reasoning"
-]New value: +[
+  "title",
+  "start_second",
+  "end_second",
+  "payoff",
+  "standalone",
+  "reasoning",
+  "preview_text"
+]
  2. Changed3 schema fields changedv2.7.2
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedInput schema / properties / suggestions / items / additionalProperties
      Removed value: -false
    • removedInput schema / properties / suggestions / items / properties / segments / items / additionalProperties
      Removed value: -false
  3. First observedv2.0.0

TDQS

A4.9/5.0
Behavior5/5

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

Despite no annotations, the description discloses behavioral rules and side effects. It states that suggestions must carry context and that clips opening on an answer without its question are rejected, with a concrete remedy ('widen start_second'). It also explains that the tool stores suggestions and pushes them to the UI, making the write operation explicit and transparent.

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 well-structured and front-loaded, starting with 'STEP 2 — Submit your clip suggestions' and then logically organizing prerequisites, rules, action, and follow-up. It is detailed but not redundant, with each section serving a purpose. The use of headings and imperative phrasing makes it easy to parse and contains no wasted sentences.

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?

Given the lack of annotations and output schema, the description is remarkably complete. It covers the workflow (before, during, after), validation rules, and the eventual outcome (UI review). It provides enough context for an agent to know exactly how to use the tool and what constraints apply, leaving no critical information missing.

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?

The schema already covers all parameter descriptions (100% coverage), but the tool description adds meaningful semantic context beyond that. For instance, it explains that 'context_line' does not satisfy the standalone check and that 'preview_text' must be the actual opening line. This enriches understanding of parameters like start_second and preview_text, elevating the baseline score from 3 to 4.

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 states the tool's purpose: 'Submit your clip suggestions after analyzing the transcript.' It specifies the verb (submit) and resource (clip suggestions), and distinguishes it from siblings like 'create_clip' by framing it as a suggestion step that stores and pushes to UI for review. The 'What it does' section reinforces the unique action of storing suggestions and assigning clip numbers.

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?

The description gives explicit usage context: 'Before calling this: read the transcript via get_ui_state(include_transcript: true) and identify the best viral moments.' It also explains the follow-up steps with 'After this: the user reviews in the UI. Then with batch_create_clips(export_selected: true) or create_clip(clip_number: N).' This clearly indicates when to use this tool relative to alternatives, providing a workflow and prerequisites.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nmbrthirteen/podcli'

If you have feedback or need assistance with the MCP directory API, please join our Discord server