MCP

AI-agent format

Spring-clean a messy shared library

List, filter and search what's actually in there — find the duplicates and the gaps before adding more.

OpsTeam Knowledge & Library Opslibrary_listlibrary_search
The use case

The problem

Shared libraries follow the same entropy curve as shared drives: enthusiastic curation, then accumulation, then a state where nobody's sure what's in there, three tags mean the same thing, and the safest move is importing the video again just in case — which is how the mess funds its own growth.

The fix isn't discipline; it's visibility. A library you can actually see — what's in it, how it's tagged, where the duplicates cluster, which topics are over-collected and which are gaps — is a library the team keeps using instead of abandoning for a fresh start that will entropy identically.

The mechanics

How veridive MCP helps

library_list and library_search are the audit tools, and they're free — browsing costs nothing. List by tag and type to see the actual shape of the collection; search titles to find the duplicate imports and the near-duplicates; walk the tag landscape to find the three tags doing one tag's job.

The audit output drives the cleanup: a duplicate list, a tag-consolidation map, and — the valuable surprise — the gap list: topics the team assumes are covered and aren't. Twenty minutes quarterly keeps the library an asset; the alternative is the shared-drive death spiral, with import credits attached.

Step by step

How to run it

  1. 1
    Connect veridive MCP

    One-time setup: add the veridive MCP server to Claude, ChatGPT, Cursor or any MCP client and generate your API key. Takes about two minutes.

  2. 2
    Map what's actually there

    The full inventory by tag and type — the library's real shape, often surprising.

    List my library's contents grouped by tag — how many items per tag, and which items carry no tags at all?
  3. 3
    Hunt the duplicates and tag-sprawl

    Title search finds the double imports; the tag map shows the synonyms.

    Search my library for likely duplicates — same or near-same titles — and list tags that look like synonyms of each other.
  4. 4
    Write the cleanup plan

    Consolidations, removals and the gap list — the part worth showing the team.

    From this audit: which tags should merge, what's duplicated, and which topics we reference often are actually missing from the library?
Before vs after

Problems it solves

  • Nobody knows what's in the shared library, so re-importing 'just in case' funds the mess.
  • Tag sprawl — three names for one concept — quietly breaks every scoped query.
  • Duplicates waste import credits and split any question's context across copies.
  • Assumed coverage: the topics everyone thinks are in there and aren't.
The payoff

Why it's valuable

  • The audit runs on free calls — visibility costs nothing.
  • Scoped queries (deep_context) get sharper the moment tags consolidate.
  • The gap list turns the next import budget into deliberate curation.
  • Quarterly twenty minutes beats the fresh-start-and-re-entropy cycle every team knows.
Copy & paste

Example prompts

List library contents by tag — counts per tag, untagged items flagged.
Find likely duplicate imports and synonym tags.
Cleanup plan: merges, removals, and the missing-topics list.
Questions

FAQ

Does the audit cost credits?

No — library_list and library_search are free calls. The whole audit is browsing; credits only enter if you decide to import the gap list.

Can tags be renamed in bulk from here?

The audit produces the consolidation map; applying tag changes happens in veridive's library UI. The MCP layer is the visibility half — which was the missing half.

How does the search work for finding duplicates?

library_search matches against item titles — same-title and near-title imports surface immediately. For content-level near-duplicates (same talk uploaded by two channels), the title patterns usually give them away too.

Keep going

Related use cases

Run this in your own assistant

Connect the veridive MCP server to Claude, ChatGPT or Cursor and paste the prompts above — setup takes about two minutes.