Skip to main content
Glama

importVideos

Add videos to a transcript collection for semantic search by specifying video IDs or URLs, collection settings, and chunking options.

Instructions

Import one or more videos into a local transcript collection for later semantic search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoIdsOrUrlsYes
collectionIdNo
chunkStrategyNo
chunkSizeSecNo
chunkOverlapSecNo
languageNo
reindexExistingNo
labelNo
embeddingProviderNo
embeddingModelNo
embeddingDimensionsNo
activateCollectionNo
dryRunNo

Implementation Reference

  • Implementation of the importVideos tool, which calls ensureCollection and persistItems to import videos into the transcript knowledge base.
    importVideos(seed: CollectionSeed, items: ImportTranscriptItem[]): ImportVideosOutput {
      this.ensureCollection(seed);
      const stats = this.persistItems(seed.collectionId, items);
      return {
        import: stats.import,
        failures: stats.failures.length > 0 ? stats.failures : undefined,
        collectionId: seed.collectionId,
        provenance: localProvenance(),
      };
    }
  • Tool registration for importVideos in mcp-server.ts.
      name: "importVideos",
      description: "Import one or more videos into a local transcript collection for later semantic search.",
      inputSchema: {
        type: "object",
        properties: {
          videoIdsOrUrls: { type: "array", items: { type: "string" }, minItems: 1, maxItems: 50 },
          collectionId: { type: "string" },
          chunkStrategy: { type: "string", enum: ["time_window", "chapters", "auto"] },
          chunkSizeSec: { type: "number", minimum: 30, maximum: 900 },
          chunkOverlapSec: { type: "number", minimum: 0, maximum: 300 },
          language: { type: "string" },
          reindexExisting: { type: "boolean" },
          label: { type: "string" },
          embeddingProvider: { type: "string", enum: ["local", "gemini"] },
          embeddingModel: { type: "string" },
          embeddingDimensions: { type: "number", minimum: 128, maximum: 3072 },
          activateCollection: { type: "boolean" },
          dryRun: { type: "boolean" },
        },
        required: ["videoIdsOrUrls"],
        additionalProperties: false,
      },
    },
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool imports videos for later semantic search, but fails to describe critical behaviors like whether it's idempotent, requires specific permissions, has rate limits, or what happens on failure. This is a significant gap for a tool with 13 parameters and no output schema.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it appropriately concise for its complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the high complexity (13 parameters, no annotations, no output schema), the description is inadequate. It lacks details on behavioral traits, parameter meanings, expected outputs, error handling, or integration with sibling tools. For a tool that likely performs significant processing, this leaves too many gaps for effective agent use.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for 13 undocumented parameters. It only vaguely references 'one or more videos' and 'collection', failing to explain the purpose of key parameters like 'chunkStrategy', 'embeddingProvider', or 'dryRun'. The description adds minimal value beyond what the schema's property names imply.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Import') and target ('videos into a local transcript collection') with a specific purpose ('for later semantic search'). It distinguishes from siblings like 'importComments' or 'importPlaylist' by focusing on videos for transcripts, but doesn't explicitly differentiate from all video-related tools like 'analyzeVideoSet'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'importPlaylist' or 'analyzeVideoSet', nor does it mention prerequisites such as needing an existing collection or system readiness. It only implies usage for preparing videos for semantic search without contextual boundaries.

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/rajanrengasamy/vidlens-mcp'

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