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§ MCP · Model Context Protocol

Give an agent the whole record

Every number and quote on these pages comes from a live query, and the same record is open to AI agents through a Model Context Protocol server. Add it as a connector and your agent can search every passage, read every topic and every episode, compare what each period says and follow every claim to its transcript.

Add it as a connector

Connector URLhttps://insightbridge-longform.aicolab.org/mcp

This report is public: no authentication is required.

Claude — personal accounts (Free, Pro and Max)
  1. Open Customize → Connectors.
  2. Click +, then Add custom connector; name it and paste the URL above.
  3. Leave Advanced settings empty and click Add.
  4. In a chat, click +, open Connectors and switch it on for that conversation.

Connectors are account-wide: added once, there in Claude on the web, desktop and mobile. Claude's connector guide →

Claude — organisation accounts (Team and Enterprise)

On Team and Enterprise plans only an organisation owner can add a custom connector; members do not see the option.

  1. An owner opens Organisation settings → Connectors and adds a custom web connector with the URL above.
  2. No authentication is required for this public report.
  3. Each member connects it from their own connector settings and switches it on in a chat.

Custom connector administration →

ChatGPT — custom MCP app
  1. Turn on Developer mode in Settings → Apps → Advanced settings, where your plan offers it; a workspace may need an administrator.
  2. Open Apps → Create and enter the URL above.
  3. Choose No authentication, scan the tools and create the app.
  4. Select the app in a conversation and ask about the record.

OpenAI's setup guide →

Microsoft 365 Copilot — via Copilot Studio
  1. Open an agent in Copilot Studio, then Tools → Add a tool → New tool → Model Context Protocol.
  2. Enter a name, a description of this record and the URL above. Choose None for authentication.
  3. Create the connection and add it to the agent. Test a question, then publish it.

Microsoft's connection guide →

Claude Code

Add the report as a remote HTTP server:

claude mcp add --transport http insightbridge-joewalker https://insightbridge-longform.aicolab.org/mcp

Run /mcp in Claude Code to check the connection. Claude Code's MCP guide →

Then ask: “Using Long Form, which topics have the widest reach, where do episodes differ, and what verbatim quotes support their positions?”

Transport: Streamable HTTP, stateless JSON-RPC over POST https://insightbridge-longform.aicolab.org/mcp. The endpoint answers each request with JSON and keeps no SSE stream open.

How to read what comes back

  • Counts describe this record. These are the episodes in it, the conversations one interviewer chose to record; "N episodes say X" measures the record, never a wider population.
  • Positions are relative to each topic's own proposition. Supports, Builds on, Mixed, Redirects, Opposes, Unclear measure a stance toward one framing, not agreement with the report or with each other.
  • One lens: Period. Perspectives compare what each value of it says; the tools that take it list its values.
  • Use the ids the tools return. topic_id is the topicClusterId a topic tool returns; episode and transcript tools return the ids their detail tools take.
  • Episode counts are not prevalence. These are the 133 conversations one interviewer chose to record between 2018 and 2026. "N episodes say X" measures the record, never a population, a profession or a field.
  • 2018-2019 · 2020-2021 · 2022-2023 · 2024-2026 — when the conversation was recorded, derived from the publication year. There is no sector or speaker-type axis; guests, topics, series and format are the site's own tags and ride as facets.
  • Two kinds of transcript. 53 are the site's own speaker-labelled transcripts; 80 are machine transcripts of the audio with no speaker labels, so in those the pipeline inferred who was speaking and attribution to host or guest is less certain. get_document and find_documents say which.
  • One grouping generation. The pipeline built 15 themes over the 68 substantive topics. list_families returns the themes; list_superclusters returns the same nodes with a note; topics_in_supercluster walks into any of them.

Try the tools

Choose a tool, adjust its inputs and call the live server: the exact request an agent makes. The tools, their inputs and their example values come from the server itself.

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