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This is where the magic happens. The MCP Reader is the interface designed for engineering managers, tech leads, and everyone above them. It gives you complete freedom to query your knowledge graph and shape the data exactly the way you need it. Ask anything: team performance summaries, AI token ROI, sprint progress, individual contribution reports. The MCP Reader understands natural language, so you simply describe what you need and get the answer in the format you prefer.

Authentication

The MCP Reader uses OAuth with JWT. On first connection, your client will open a login page where you authenticate with your Cogniscape account. Tokens expire after 60 minutes and are automatically renewed. After 30 days of inactivity, a new login is required. This applies to all clients listed in the setup guide.

Beyond interactive queries

The MCP Reader is not limited to AI clients like Claude Desktop. It’s a standard MCP endpoint that can be integrated into automated workflows and scheduled tasks. Build pipelines that generate reports automatically and deliver them via Slack, email, WhatsApp, Telegram, or any channel your organization uses. Weekly team summaries, monthly performance reviews, daily token usage digests: all fully automated.

Tools

The Cogniscape MCP exposes seven tools that AI assistants can call to retrieve data from your knowledge graph.

get_activity_timeline

The primary tool for understanding what happened. Returns entities (PRs, issues, features, developers) that were active during a time period, along with their summaries and temporally-scoped facts. When to use: Broad questions like “what happened this week?”, “what did the team deliver?”, or any time-based query.

search_nodes

Semantic search for entities (nodes) in the knowledge graph — developers, repositories, PRs, issues, features, and any other concept Cogniscape has extracted. When to use: Entity questions like “who is Alice?”, “what is the billing service?”, or “find all repositories related to payments”.

search_facts

Semantic search for relationships (edges) between entities. Facts include temporal validity — you can see when a relationship was true and when it expired. When to use: Relationship questions like “what PRs did Alice merge?”, “which issues are blocking the release?”, or “what was authored this week?” (use since for temporal filtering).

get_episodes

Retrieves raw ingestion events — the conversations, code changes, and webhook payloads that were added to the graph. Useful for understanding provenance and context. When to use: Provenance questions like “when was this information added?”, “show me the raw events from yesterday”, or debugging data ingestion.

get_token_usage

Returns AI token consumption and estimated cost in USD, broken down by model and developer.
When date_start and date_end are provided, the period parameter is ignored.
When to use: Cost questions like “how much did Claude cost this week?”, “what’s our token usage by developer?”, or “show me AI spending for the billing repo”.

get_developer_roi

Returns ROI analysis per developer: total cost, daily average, daily trend, and breakdowns by model and repository. When to use: ROI questions like “what’s the cost per developer?”, “show me the spending trend”, or “is our AI investment efficient?“.

get_saga_timeline

Retrieves the ordered timeline of episodes belonging to a lifecycle saga. Sagas automatically group related events — every PR, issue, and coding session gets its own saga, linking all events in chronological order. Saga names follow a deterministic pattern based on the event source: When to use: Lifecycle questions like “show me the full history of PR #99”, “what happened with issue #42?”, or “trace this coding session from start to finish”.

Prompts

The Cogniscape MCP includes six built-in prompts — pre-written conversation starters that guide the AI assistant to use the right tools and present results in a structured way. These appear as suggested prompts in compatible clients.

daily_standup

Summarizes the last 24 hours of activity: what was done, what’s in progress, and attention points. Accepts optional repository and developer filters.

weekly_review

Generates a comprehensive weekly report: deliveries, merged PRs, deployments, decisions, and blockers. Designed for team review meetings.

developer_profile

Shows a developer’s activity profile: recent contributions, repositories, contribution types (code, reviews, issues), and AI token consumption.

document_history

Shows the change history for a specific document, repository, or component: who modified it, when, and what changed.

cost_report

Generates an AI cost report for a given period using get_token_usage and get_developer_roi. Includes total estimated cost, distribution by model and developer, daily trend, and average daily cost. Presented for non-technical managers.

team_overview

Maps the project’s contributors: areas of expertise, contribution types (code, reviews, management), and recent activity. Presented as a team map.

Tool selection guide

Not sure which tool to use? Here’s a quick reference:
For complex questions, the AI assistant will combine multiple tools automatically. For example, “what did Alice work on this week and how much did it cost?” will use both get_activity_timeline and get_token_usage.