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Exploring MCP Sessions

Free

Investigate individual PostHog MCP sessions with ease.

by posthog37.6k stars on posthog/posthog
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Updated Aug 11, 2026
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Free · Opens the source repo

What Exploring MCP Sessions does

The Exploring MCP Sessions skill allows users to delve into the details of individual PostHog MCP sessions, which represent the sequence of tool calls made by an agent during a single run. This skill provides a structured way to access session data, making it easier to understand what an agent was attempting to achieve, identify errors, and analyze tool usage. By leveraging typed tools specifically designed for session analysis, users can efficiently retrieve relevant information without needing to write complex queries from scratch.

The skill includes three primary tools: one for listing sessions, another for retrieving the tool calls of a specific session, and a third for generating a summary of the agent's intent. Each of these tools is designed to streamline the investigation process, ensuring that users can quickly access the information they need. The session listing tool provides an overview of recent sessions, while the tool calls tool allows for a chronological view of the events that transpired during a specific session. The intent generation tool utilizes a language model to summarize the agent's goals, which can be particularly useful for understanding the context of the actions taken.

It's important to note that the default behavior of the detail tools is to look back only seven days, which can lead to confusion if users attempt to access older sessions without specifying the correct date parameters. Additionally, there are certain limitations, such as the inability to filter sessions based on errors directly in the listing tool, which necessitates the use of SQL queries for more advanced analysis. Overall, this skill is ideal for developers and data analysts who need to monitor and troubleshoot agent behavior within PostHog's MCP framework.

When to use it

Use this skill when you need to analyze specific MCP sessions for insights into agent behavior or to troubleshoot errors.

When not to use it

This skill may not be suitable for users who require extensive historical data analysis beyond the seven-day lookback or need to perform complex queries that are not supported by the available tools.

What you can build with it

Troubleshooting Agent Errors

Quickly identify which sessions encountered errors by using SQL queries to filter and analyze session data.

Understanding Agent Goals

Generate a summary of an agent's intent for a specific session to better understand its objectives and actions.

Analyzing Tool Usage

Review the sequence of tool calls made during an MCP session to assess performance and identify potential improvements.

How to install Exploring MCP Sessions

View source

1. Install with the skills CLI

npx skills add posthog/posthog/exploring-mcp-sessions --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by posthog

Exploring MCP sessions

An MCP session is one agent run: the set of $mcp_tool_call events sharing a $session_id, ordered by timestamp.

Listing sessions, reading a session's tool calls, and summarising its goal each have a typed tool — reach for those first. Drop to HogQL only for the three things the typed tools genuinely can't do (see When to drop to SQL). The full $mcp_* property schema and query recipes live in the shared reference: models-mcp.md.

Tools

ToolPurpose
posthog:mcp-analytics-sessions-listList sessions — one row per session, newest first
posthog:mcp-analytics-sessions-tool-callsOne session's tool calls, chronological
posthog:mcp-analytics-sessions-generate-intentLLM summary of a session's goal (cached after first call)
posthog:execute-sqlErrored sessions, effective tool names, cross-session cuts

The three mcp-analytics-* tools are gated behind the mcp-analytics flag and run the same code as the sessions UI, so results match the screen. If they aren't in your tool list, the project doesn't have the flag — fall back to posthog:execute-sql, which is ungated.

The date-window trap — read this first

The two detail tools default to a 7-day lookback. A session you found in a list that reaches further back will come back empty unless you pass its session_start as date_from:

  • posthog:mcp-analytics-sessions-tool-callsdate_from is an absolute ISO timestamp; pass the session_start you got from posthog:mcp-analytics-sessions-list.
  • posthog:mcp-analytics-sessions-generate-intent — same date_from query param, same reason.

Empty tool calls for a session that visibly exists is almost always this, not a data problem. Carry session_start forward from the list row.

Workflow: list recent sessions

posthog:mcp-analytics-sessions-list
{ "date_from": "-7d", "order_by": "-session_start", "limit": 100 }

Each row: session_id, tool_calls, session_start, session_end, tools_used, mcp_client_name, distinct_id (+ resolved person_email / person_name), and intent (empty until generated). Response is { results, has_next } — page with limit / offset.

Three sharp edges:

  • order_by takes column names, not response field names. Sort call volume as tool_call_count (not tool_calls). duration_seconds sorts fine even though it isn't returned. An unrecognised key silently falls back to newest-first — so verify the order you got is the order you asked for. Valid: session_id, session_start, session_end, duration_seconds, tool_call_count, mcp_client_name, distinct_id; prefix - to descend.
  • There is no error filter and no error count on a session row. "Which sessions had errors?" is a SQL question — see below.
  • distinct_id_count is always 0. The field is in the response but the backend never populates it, so don't read it as "one distinct id per session" — it says nothing. To count distinct ids in a session, use SQL.

search does a case-insensitive substring match across session_id, distinct_id, mcp_client_name, and tools_used.

Workflow: read one session's tool calls

posthog:mcp-analytics-sessions-tool-calls
{ "id": "<session_id>", "date_from": "<session_start>", "limit": 500 }

Chronological tool_name, intent, timestamp, duration_ms, is_error, error_message — read top to bottom to reconstruct the run. limit defaults to 500 (also the max), which is the whole page for almost every session; has_next tells you if more remain.

Caveat: tool_name here is the raw $mcp_tool_name. Unlike the tool-quality and tool-detail tools, this endpoint does not resolve the inner tool of a single-exec wrapper call, so wrapper calls show the wrapper. When the inner tool is what matters (comparing against a tool-quality ranking, tracing a specific tool through a run), use the SQL recipe below instead. The same applies to tools_used on the session list.

Workflow: summarise the agent's goal

posthog:mcp-analytics-sessions-generate-intent
{ "id": "<session_id>", "date_from": "<session_start>" }

Summarises the session's recorded $mcp_intent values via an LLM and persists the result; later calls return the cached summary. Returns { session_id, intent }. A 503 means LLM summarisation isn't configured — fall back to reading the raw $mcp_intent values from the tool-call list.

When to drop to SQL

Four cases, all via posthog:execute-sql, which — unlike the typed tools above — is not gated behind the mcp-analytics flag.

1. The project doesn't have the mcp-analytics flag. The typed tools simply won't be in your tool list. Everything below still works; this query is the plain session listing:

SELECT
    $session_id AS session_id,
    min(timestamp) AS session_start,
    max(timestamp) AS session_end,
    dateDiff('second', min(timestamp), max(timestamp)) AS duration_seconds,
    count() AS tool_calls,
    countIf(toBool(properties.$mcp_is_error)) AS errors,
    any(properties.$mcp_client_name) AS client
FROM events
WHERE event = '$mcp_tool_call'
    AND $session_id != ''
    AND timestamp >= now() - INTERVAL 7 DAY
GROUP BY session_id
ORDER BY session_start DESC
LIMIT 50

2. Errored sessions. The session list can't filter or count errors — add HAVING errors > 0 to the query above and order by errors DESC.

3. Effective tool names within a session — the coalesce the typed tool-calls endpoint doesn't apply:

SELECT
    timestamp,
    coalesce(nullIf(toString(properties.$mcp_exec_tool_call_name), ''), toString(properties.$mcp_tool_name)) AS tool,
    toBool(properties.$mcp_is_error) AS is_error,
    toString(properties.$mcp_error_message) AS error_message,
    round(toFloat(properties.$mcp_duration_ms)) AS duration_ms
FROM events
WHERE event = '$mcp_tool_call'
    AND $session_id = '<session_id>'
ORDER BY timestamp ASC

4. Cross-session aggregation — "sessions per day", "sessions that used tool X and then failed", custom breakdowns. Recipes in models-mcp.md.

Note $session_id is a materialised events column — the same id as $mcp_session_id. Reference it bare, never as properties.$session_id: the properties. accessor renders null-wrapped in SELECT but as the raw column in HAVING/ORDER, so a HAVING search would mismatch the GROUP BY key.

Constructing UI links

  • Sessions list: https://app.posthog.com/project/<project_id>/mcp-analytics/sessions

Tips

  • A session with many calls but no errors that ends abruptly often means the agent gave up — check whether the last call returned a large or empty result
  • $mcp_intent is only present when the client supplied it; absence is common, so generate-intent is the more reliable goal signal
  • To go from a failing tool (see exploring-mcp-tool-quality) to the sessions that hit it, search the session list by tool name — remembering tools_used holds raw names, so search the registered name, not the inner one

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