Log Pruner MCP Server
Query OpenSearch API directly to retrieve log entries and return compact tables.
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., "@Log Pruner MCP Serverread logs from /var/log/app.log"
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.
Log Pruner MCP Server
A Python MCP server that reduces token usage by ~98% when working with log files. Auto-detects file format, strips noise (kubernetes metadata, duplicate fields, boilerplate), and returns only actionable signal. Works with any common log format — no manual conversion needed.
Tools
Tool | What it does |
| Read any log file, return compact table. Auto-detects format and log type (HTTP vs application). |
| Query OpenSearch API directly, return compact table |
| Aggregate summary: level distribution, top loggers/endpoints, error rates |
| Full detail for errors — HTTP 5xx, app-log ERROR/WARN, ImpEx failures, stack traces |
| All entries within N seconds of a timestamp — like |
Related MCP server: AgentCost
Supported Formats
Structured (JSON-based)
Format | Example source |
OpenSearch/Elasticsearch JSON | Kibana export, API response ( |
NDJSON |
|
JSON Array | API responses, custom export tools ( |
OpenSearch/Kibana CSV | Kibana Discover CSV export (with |
Plain Text
Format | Pattern |
Spring Boot |
|
Log4j / Logback |
|
Python logging |
|
Nginx / Apache access |
|
Docker container |
|
Syslog |
|
Generic ISO + level |
|
Generic ISO timestamp |
|
Format detection is automatic — just pass any log file path. No configuration needed.
Log Type Auto-Detection
Type | Detection | Output |
HTTP Access Logs | Has status code, request line, response time | Compact table: timestamp, status, ms, bytes, IP, request |
Application Logs | Has level, message, thread/logger | Compact table: timestamp, level, thread, message |
If HTTP parsing yields no results, tools automatically fall back to application log parsing.
Domain-Specific Error Detection
SAP Commerce ImpEx
get_errors recognizes ImpEx deployment failures that are often logged at INFO level:
dumped: N(where N > 0) — lines that couldn't be importedcould not import N lines— final failure summaryCan not resolve any more lines— resolution failureImpex import failed— SystemSetupExceptionSHUTTING DOWN— context startup failure
These are surfaced as [IMPEX FAILURE] entries alongside regular errors.
The pattern detection system is extensible — add your own domain-specific patterns to
IMPEX_ERROR_PATTERNSinsrc/parser.py.
Setup
1. Install dependencies
cd log-pruner
pip install -r requirements.txt2. Configure OpenSearch (optional, for live API access)
export OPENSEARCH_URL="https://your-opensearch-cluster:9200"
export OPENSEARCH_USER="admin"
export OPENSEARCH_PASSWORD="secret"3. Add to Claude Code
{
"mcpServers": {
"log-pruner": {
"command": "python",
"args": ["/absolute/path/to/log-pruner/server.py"]
}
}
}For live OpenSearch access, add one entry per environment with connection details:
{
"mcpServers": {
"log-pruner-dev": {
"command": "python",
"args": ["/absolute/path/to/log-pruner/server.py"],
"env": {
"OPENSEARCH_URL": "https://dev-opensearch-cluster:9200",
"OPENSEARCH_USER": "admin",
"OPENSEARCH_PASSWORD": "dev-password"
}
},
"log-pruner-staging": {
"command": "python",
"args": ["/absolute/path/to/log-pruner/server.py"],
"env": {
"OPENSEARCH_URL": "https://staging-opensearch-cluster:9200",
"OPENSEARCH_USER": "admin",
"OPENSEARCH_PASSWORD": "staging-password"
}
}
}
}4. Restart Claude Code
5. Add to your project's CLAUDE.md
## Log Analysis
When the user provides a log file or asks to analyze/debug logs:
- Do NOT read the file with the built-in Read tool — it will flood context
- Pass the file path to the log-pruner MCP tools which strip ~98% of noise:
- `mcp__log-pruner__read_logs` — compact table from any log file
- `mcp__log-pruner__summarize_logs` — overview (level distribution, top loggers, error rates)
- `mcp__log-pruner__get_errors` — all errors with full detail
- `mcp__log-pruner__get_context` — entries around a specific timestamp
- `mcp__log-pruner__query_logs` — query live OpenSearch (requires env vars)Usage Examples
Any Plain Text Log File
read_logs(path="app.log")
→ 1327 app log entries
TIMESTAMP | LVL | THREAD | MESSAGE
2026-06-06T15:12:44Z | INFO | main | Starting application
2026-06-06T15:12:45Z | ERROR | http-nio-8080-exec-1 | NullPointerException at line 42
2026-06-06T15:12:46Z | WARN | scheduler-1 | Job took 5000msNginx/Apache Access Logs
read_logs(path="access.log", status_filter="5xx")
→ 3 hits (of 50000 total)
TIMESTAMP | ST | MS | BYTES | FROM | REQUEST
12/Jun/2026:10:53:07Z | 500 | | 56 | 10.0.0.2 | POST /api/login
12/Jun/2026:10:53:09Z | 502 | | 0 | 10.0.0.3 | GET /api/usersNDJSON (kubectl logs, docker logs)
read_logs(path="pod-logs.json")
→ 500 app log entries
TIMESTAMP | LVL | THREAD | MESSAGE
2026-06-06T15:12:44Z | ERROR | | Connection refused
2026-06-06T15:12:45Z | INFO | | Retrying in 5s...Aggregate Summary
summarize_logs(path="deployment.log")
→ === App Log Summary (3808 entries) ===
Level distribution:
INFO: 3749 (98.5%)
WARN: 55 (1.4%)
ERROR: 4 (0.1%)
Top 10 loggers:
3012x de.hybris.platform.impex.jalo.imp.ImpExWorker
215x de.hybris.platform.servicelayer.impex.impl.DefaultImportService
...
Warnings/Errors (22 unique):
[WARN] column absolute of type Discount is read-only...
[ERROR] Can not resolve any more lines...Error Detection (with ImpEx awareness)
get_errors(path="deployment.log")
→ === ImpEx Failures (3) ===
[IMPEX FAILURE] 2026-06-06T15:12:46Z
Thread: main
Logger: de.hybris.platform.impex.jalo.cronjob.ImpExImportJob
Message: Can not resolve any more lines ... Aborting further passes (at pass 3).
=== Errors (2) ===
[ERROR] 2026-06-06T15:12:46Z
Thread: main
Logger: de.hybris.platform.core.Initialization
Message: SystemSetupException: Impex import failed for : '00037874-ImpEx-Import'Troubleshooting Workflow
1. read_logs(path="file.log") → Quick scan (compact, low tokens)
2. summarize_logs(path="file.log") → Level distribution, top loggers
3. get_errors(path="file.log") → All errors + ImpEx failures
4. get_context(path="file.log", timestamp="..") → Entries around a specific timeToken Savings



Input | Without log-pruner | With log-pruner | Savings |
10-hit OpenSearch JSON | ~2,500 tokens | ~50 tokens | ~98% |
4MB CSV export (3,800 rows) | ~400,000 tokens | ~3,000 tokens | ~99% |
1000-line Spring Boot log | ~15,000 tokens | ~200 tokens | ~99% |
Nginx access log (10k lines) | ~150,000 tokens | ~500 tokens | ~99% |
Error extraction from any format | ~400,000 tokens | ~500 tokens | ~99% |
Limitations
Plain text parsing relies on pattern matching — custom formats without a recognized timestamp pattern won't be parsed (lines are skipped)
Application log parsing expects JSON in the
logfield or directlevel/messagefieldsImpEx detection is pattern-based — custom error messages outside the known patterns won't be caught
API mode requires
opensearch-pyand env vars configuredTime filtering uses string comparison (works for ISO timestamps; approximate for other formats)
Maintenance
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