agent-activity
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agent-activityshow my recent sessions"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
agent-activity
A read-only MCP server that exposes your local coding-agent session logs
(the JSONL transcripts written by Claude Code at
~/.claude/projects/<encoded-cwd>/<sessionId>.jsonl) as three tools, so
you — or an agent — can introspect recent work, debug tool failures, and
track token/estimated cost without hand-parsing log files.
Guarantees
Read-only. The server only ever reads log files; it never writes to, deletes, or otherwise mutates anything under the log root.
Local. All data comes from the filesystem on the machine the server runs on.
No network. The server never makes an outbound network call — not for logs, not for pricing data, not for telemetry. Pricing is a local, configurable table (see Cost is an estimate).
stdio transport. Standard MCP over stdio, via the official
mcpPython SDK (FastMCP).
Related MCP server: log-mcp
Tools
list_recent_sessions(limit=20, repo=None)
Recent sessions, most-recently-active first.
limit— max sessions to return (default 20, max 200).repo— optional substring filter against the session's repo/cwd.
Returns, per session: session_id, repo, cwd, git_branch, start/
end timestamps, message_count, schema_status ("verified" or
"unverified" — whether the log's shape matched what this server
expects; degrades gracefully rather than failing on drift), and a
usage block (total_tokens, total_cost, cost_status, and a
main/sidechain breakdown — see Sidechains).
tail_agent_log(session, limit=100, include_current=False)
The tail of one session's transcript, oldest-first.
session— a full session UUID, a unique UUID prefix, or"latest"(see Session resolution).limit— max entries to return (default 100, max 1000).include_current— when resolving"latest", whether to allow it to resolve to the session currently calling this tool (defaultFalse).
Returns session_id, repo, and a list of entries, each with type,
timestamp, any tool_calls (tool_use_id, tool_name) and
tool_results (tool_use_id, is_error), and — for assistant entries
that carry usage — a usage block with model, per-entry tokens, and
estimated cost.
summarize_tool_calls(session)
Per-tool aggregate for a session: invocation counts, error counts, which calls failed, and total usage.
session— a full session UUID, a unique UUID prefix, or"latest"(resolved including the current session — summarizing your own live session is a valid use case here).
Returns session_id, repo, is_live_session, per_tool (per tool
name: ok/error/pending/in_progress counts), totals (the same
breakdown across all tools, plus rejected), by_source (main vs
sidechain totals), failing_calls (each labeled error or
rejected), and a session-level usage block (tokens + estimated cost).
All three tools return a structured {"error": ...} dict (never raise)
when a session argument is ambiguous ("ambiguous_session", with
candidates) or matches nothing ("session_not_found").
Cost is an estimate, not a bill
Session logs record token counts (message.usage) but not dollar
cost. Cost is computed by multiplying token counts by a per-model rate
table (pricing.py) — order-of-magnitude, hand-maintained figures that
will drift as providers change pricing. Treat any cost field as a
ballpark to sanity-check spend, not an authoritative bill. Unknown
models degrade to cost_status: "unknown" (tokens are still reported)
rather than raising.
Override the table with your own by pointing AGENT_ACTIVITY_PRICING_FILE
at a JSON file shaped like:
{
"claude-sonnet-5": {
"input": 3.00,
"output": 15.00,
"cache_write": 3.75,
"cache_read": 0.30
}
}Rates are USD per 1,000,000 tokens. A model listed in the override file fully replaces that model's default entry; models you don't mention keep their built-in defaults.
Sidechains and subagents
Subagent ("sidechain") activity lives in separate sibling files
(<session-dir>/subagents/agent-*.jsonl), not inline in the main session
file. This server associates those files with their parent session and
includes their tool calls and token usage in summarize_tool_calls and
list_recent_sessions, but keeps every count and token total tagged
main vs sidechain so totals stay decomposable rather than blended.
Sidechain usage is summed only from the sidechain files' own
message.usage entries. The parent log's separate rollup fields
(toolUseResult.totalTokens / the <usage>...</usage> text tag on the
spawning Agent tool's result) are a second, independently-computed
aggregate of the same work — summing both would double-count, so those
fields are never added into the totals here.
Config
Env var | Default | Purpose |
|
| Root directory to scan for |
| (none — built-in table) | Path to a JSON pricing-table override (see above). |
| (none — falls back to a heuristic) | The calling session's own id, if your MCP client can supply it. Used to reliably identify "the current session" for |
Install
pip install -e .This installs the agent-activity console script and its one dependency
(mcp).
Run
Any of the following start the same stdio server:
agent-activity
python -m agent_activity
python -m agent_activity.serverThe process speaks MCP over stdio (stdin/stdout) — it's meant to be launched by an MCP client, not run interactively.
Register with an MCP client
An example client registration is checked in at .mcp.json:
{
"mcpServers": {
"agent-activity": {
"command": "agent-activity",
"args": [],
"env": {
"AGENT_ACTIVITY_LOG_ROOT": "~/.claude/projects"
}
}
}
}Point command at the console script (make sure it's on PATH, e.g. by
installing into the environment your MCP client uses), or swap it for
python with args: ["-m", "agent_activity"] if you'd rather invoke the
module directly.
Example tool calls
Once registered, a client can call e.g.:
list_recent_sessions(limit=5)
tail_agent_log(session="latest", limit=50)
summarize_tool_calls(session="latest")or target a specific session by its full UUID or an unambiguous prefix:
tail_agent_log(session="4140dfe3", limit=200)How session resolution works
The session argument to tail_agent_log and summarize_tool_calls
accepts:
a full session UUID — exact match against the log filename stem;
a unique UUID prefix — resolves if exactly one discovered session id starts with it; an ambiguous prefix (matches more than one) returns a structured error listing the candidates rather than guessing;
"latest"— the most-recently-active session (newest log file mtime). By default this excludes the session currently calling the tool, sotail_agent_log(session="latest")doesn't tail its own still-being-written log; passinclude_current=Trueto opt back in. (summarize_tool_callsresolves"latest"including the current session by default, since summarizing your own live session is a normal thing to want.)
Identifying "the current session" is inherently a heuristic — an MCP
server isn't told its caller's session id by the protocol. If the
AGENT_ACTIVITY_CALLER_SESSION_ID env var is set, it's used directly
(authoritative). Otherwise the server falls back to "the session with
the newest file mtime" (optionally scoped to the caller's cwd), which
can misfire if two sessions are active at once.
Development
pip install -e .[dev]
pytest tests/ -qAn end-to-end stdio smoke test (spawns the real server as a subprocess,
performs the MCP handshake, and calls all three tools against real logs)
lives at scripts/smoke_test.py:
python scripts/smoke_test.pyMaintenance
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