Which integrations are available for this server?

Query OpenSearch API directly to retrieve log entries and return compact tables.

How do I use Log Pruner MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Log Pruner MCP Server read 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

by nrapendra-singh

Overview Schema Source Related Servers Discussions Admin

Python

Local

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_logs`	Read any log file, return compact table. Auto-detects format and log type (HTTP vs application).
`query_logs`	Query OpenSearch API directly, return compact table
`summarize_logs`	Aggregate summary: level distribution, top loggers/endpoints, error rates
`get_errors`	Full detail for errors — HTTP 5xx, app-log ERROR/WARN, ImpEx failures, stack traces
`get_context`	All entries within N seconds of a timestamp — like `grep -C` for logs

Related MCP server: AgentCost

Supported Formats

Structured (JSON-based)

Format	Example source
OpenSearch/Elasticsearch JSON	Kibana export, API response (`hits.hits[]._source`)
NDJSON	`kubectl logs`, `docker logs --format json`, Fluent Bit, CloudWatch
JSON Array	API responses, custom export tools (`[{...}, {...}]`)
OpenSearch/Kibana CSV	Kibana Discover CSV export (with `_source.*` columns or `log` column)

Plain Text

Format	Pattern
Spring Boot	`2026-06-06T15:12:44.842Z INFO 1 --- [thread] logger : message`
Log4j / Logback	`2026-06-06 15:12:44,842 INFO [thread] [logger] - message`
Python logging	`2026-06-06 15:12:44,842 - module - ERROR - message`
Nginx / Apache access	`10.0.0.1 - - [12/Jun/2026:10:53:07 +0000] "GET /path HTTP/1.1" 200 1234`
Docker container	`2026-06-06T15:12:44.842Z stdout F message`
Syslog	`Jun 12 10:53:07 hostname process[pid]: message`
Generic ISO + level	`2026-06-06T15:12:44Z ERROR something broke`
Generic ISO timestamp	`2026-06-06T15:12:44Z any message here`

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 imported
could not import N lines — final failure summary
Can not resolve any more lines — resolution failure
Impex import failed — SystemSetupException
SHUTTING 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_PATTERNS in src/parser.py.

Setup

1. Install dependencies

cd log-pruner
pip install -r requirements.txt

2. 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 5000ms

Nginx/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/users

NDJSON (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 time

Token Savings

Supported Formats

Before vs After Workflow

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 log field or direct level/message fields
ImpEx detection is pattern-based — custom error messages outside the known patterns won't be caught
API mode requires opensearch-py and env vars configured
Time filtering uses string comparison (works for ISO timestamps; approximate for other formats)

Available Tools

5 tools

get_contextA

Get full log entries around a specific timestamp — like grep -C for logs. Shows what happened before and after an event for troubleshooting.

Args: path: Path to local OpenSearch JSON file. index: OpenSearch index to query (requires OPENSEARCH_URL env). timestamp: Center timestamp (ISO format, e.g. "2026-06-03T01:05:05Z"). window_seconds: Seconds before and after timestamp to include (default 5). pod: Optional pod name filter — only show entries from this pod. limit: Max entries to return (default 50).

Inputs: pod, path, index, limit, +2 more. Returns: result

get_errorsA

Get full detail for non-200 responses (errors). Shows stack traces, log bodies, pod info — everything needed for troubleshooting.

Args: path: Path to local OpenSearch JSON file. index: OpenSearch index to query (requires OPENSEARCH_URL env). query: Additional Lucene query for API mode. time_from: ISO timestamp lower bound. time_to: ISO timestamp upper bound. limit: Max error entries to return (default 20). context_before: Number of log entries to show before each error (default 0). Set to 20 to see what happened leading up to the error.

Inputs: path, index, limit, query, +3 more. Returns: result

query_logsA

Query OpenSearch directly and return compact log table. Requires OPENSEARCH_URL env var.

Args: index: OpenSearch index pattern (e.g. "logs-json-"). query: Lucene query string (e.g. "status:500 AND logs.requestFirstLine:/api/users*"). time_from: ISO timestamp lower bound (e.g. "2026-06-03T01:00:00Z"). time_to: ISO timestamp upper bound. limit: Max entries to fetch (default 50). status_filter: Client-side filter by HTTP status after fetch. min_response_time: Client-side filter by min response time (ms).

Inputs: index (required), limit, query, time_to, +3 more. Returns: result

read_logsA

Read an OpenSearch log file (JSON or CSV) and return compact log table. Strips kubernetes metadata, duplicate fields — ~98% token reduction. Supports both HTTP access logs and application logs (auto-detected).

Args: path: Path to the OpenSearch JSON or CSV export file. limit: Max entries to return (default 100). status_filter: Only show entries with this HTTP status (e.g. "500", "4xx"). Only applies to HTTP access logs. min_response_time: Only show entries slower than this (ms). Only applies to HTTP access logs. time_from: ISO timestamp lower bound filter (e.g. "2026-06-03T01:05:05Z"). time_to: ISO timestamp upper bound filter.

Inputs: path (required), limit, time_to, time_from, +2 more. Returns: result

summarize_logsA

Aggregate log summary: top endpoints, error rates, slowest requests, unique IPs. Provide either path (file) or index (API).

Args: path: Path to local OpenSearch JSON file. index: OpenSearch index to query (requires OPENSEARCH_URL env). query: Lucene query string for API mode. time_from: ISO timestamp lower bound for API mode. time_to: ISO timestamp upper bound for API mode. limit: Max entries to analyze (default 200).

Inputs: path, index, limit, query, +2 more. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

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TDQS

A4.1/5.0

Scored 2026-07-16 15:25 across 5 tools

Disambiguation5/5

Each tool has a distinct purpose: get_context retrieves logs around a timestamp, get_errors filters errors, query_logs queries OpenSearch directly, read_logs reads from a file, and summarize_logs aggregates. No two tools overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: get_context, get_errors, query_logs, read_logs, summarize_logs. The naming is predictable and clear.

Tool Count5/5

With 5 tools, the server is well-scoped for log analysis. Each tool covers a specific operation (read, query, error extraction, context, summary) without redundancy or excessive complexity.

Completeness5/5

The tool set covers all essential log analysis operations: reading, querying, error inspection, context retrieval, and summarization. No obvious gaps for the intended domain of log troubleshooting.

Maintenance

ActivityInactive

ResponsivenessNo recent community issues

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Log Pruner MCP Server

Tools

Supported Formats

Structured (JSON-based)

Plain Text

Log Type Auto-Detection

Domain-Specific Error Detection

SAP Commerce ImpEx

Setup

1. Install dependencies

2. Configure OpenSearch (optional, for live API access)

3. Add to Claude Code

4. Restart Claude Code

5. Add to your project's CLAUDE.md

Usage Examples

Any Plain Text Log File

Nginx/Apache Access Logs

NDJSON (kubectl logs, docker logs)

Aggregate Summary

Error Detection (with ImpEx awareness)

Troubleshooting Workflow

Token Savings

Limitations

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Log Analyzer MCP

AgentCost

log-mcp

cctx-mcp