LogMCP

Overview Schema Related Servers Score Discussions

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: configuration inspection, environment validation, log discovery, metadata retrieval, reading, searching, and server status. No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., check_config, list_logs, read_log). No mixing of conventions.
Tool Count5/5
With 7 tools, the set covers all core operations for a log management server without being excessive or sparse. Each tool earns its place.
Completeness4/5
The surface covers log discovery, reading, searching, metadata, and diagnostic checks. A minor gap is the lack of real-time tailing or streaming, but the set is otherwise complete for the intended purpose.
Average 4.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 30 commits in the last 12 weeks
- Last stable release on August 9, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
View related servers

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description implies a read-only operation ('Show', 'highlight'), but annotations set destructiveHint=true. No disclosure of any destructive behavior contradicts the description, leading to a score of 1. Annotation contradiction flagged.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with clear sections (main purpose, when to use, response format). Every sentence adds value; no wasted words. Front-loaded with primary purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and simple output, the description is complete in explaining purpose and response. Provides reference to config docs. However, the contradiction with annotations slightly degrades completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so description adds meaning by explaining the response structure (current and defaults fields). Baseline 4 for zero parameters is appropriate with the addition of response semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states specific verb 'Show' and resource 'current LogMCP server configuration', with additional detail about highlighting default parameters. It distinguishes from sibling tools like 'server_status' and 'check_environment'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'When to use' section explicitly describes scenarios (understanding accessible logs, proxy mode, etc.) and references configuration documentation. Does not mention when not to use or alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations include destructiveHint: true, implying potential side effects, but the description describes only checks that return status (no modifications). This contradiction misleads the agent about the tool's behavior. The description does not clarify the destructive hint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the purpose, and organized into clear sections ('When to use', 'Response'). Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description fully explains the checks performed and the structure of the response array, making the tool's behavior complete and understandable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is trivially 100%. The description adds no parameter information beyond the schema, but this is acceptable per the baseline for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a set of server-side environment checks and returns pass/fail status, listing specific covered areas (config, TLS, etc.). This distinguishes it from sibling tools like check_config (likely more specific) and server_status (probably server-level info).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'When to use' section provides explicit scenarios: verifying configuration, diagnosing tool issues, or after configuration changes. It lacks explicit exclusions or alternatives but gives clear context for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation by stating it returns metadata. However, annotations have destructiveHint=true, which contradicts this. The description does not address this inconsistency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for when to use, parameters, and response. It is concise with no wasted words, and every sentence adds necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fully details the response fields and provides enough context for an agent to use the tool correctly. For a simple tool with one parameter, it is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description repeats exactly what the schema already provides ('Absolute path to the log file. Obtain valid paths from list_logs.'). No additional semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns metadata (size, line count, last-modified) for a single log file. It distinguishes from siblings like list_logs (which lists files) and read_log (which reads content).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'When to use' section explicitly says to check if a log file has changed or determine size/line count before reading. It references read_log as an alternative, but does not explicitly state when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description describes a read-only status report, but annotations set readOnlyHint=false and destructiveHint=true, contradicting the implied behavior. This is a serious inconsistency that undermines transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with 'When to use' and 'Response' sections. Every sentence adds value, no redundancy. Efficiently conveys all necessary information about usage and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description fully covers the tool's purpose, usage context, and response format. It is self-contained and leaves no ambiguity about what the tool does and returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. Description adds no parameter-level detail but provides useful context about the response and return structure, which aligns with standard practice for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool reports runtime status of LogMCP server, including MCP layer responsiveness, tool count, and extension accessibility. It uses specific verb 'report' and distinguishes from siblings like check_environment by positioning as a first connectivity check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use as first step for MCP connectivity issues and to use before deeper checks. Provides clear context for when to apply the tool and how it fits into a diagnostic workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavioral details beyond annotations: pattern redaction, response structure with context lines, and default values. However, annotations indicate destructiveHint=true while description implies read-only operation, creating a subtle contradiction not addressed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (overview, when to use, parameters, response). Each sentence is informative, no fluff, and front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description details the response structure. It covers parameters and usage well but fails to explain the destructiveHint=true annotation anomaly, leaving a minor completeness gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds value with context like 'Obtain valid paths from list_logs' for path and 'Uses Go regexp syntax' for pattern. This surpasses the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches log files for regex patterns, specifying verb, resource, and result. It distinguishes from sibling 'read_log' by targeting specific patterns versus full file reading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
A dedicated 'When to use' section explains the tool is for targeted searches without reading the whole file, explicitly comparing it to 'read_log'. It suggests combining with temporal parameters but lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, but description does not elaborate on why it's destructive or any side effects. Response format is useful but not behavioral. Some value added, but no hidden traits disclosed beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections. Every sentence adds value, no fluff. Concise yet informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 0-parameter list tool, description covers purpose, usage guidance, and response format thoroughly. No missing aspects given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so schema coverage is irrelevant. Baseline 4 applies; description does not need to add param info and doesn't miss anything.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'List all log files the server has been configured to expose' with verb and resource. Distinguishes from sibling tools like read_log and search_log.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to call this first before using read_log, search_log, or log_info, and to use it when path is unknown. Provides clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description fully explains the tool's behavior: reading from start or end, time-based filtering, pagination, default line count, and response structure. Despite `destructiveHint=true` in annotations, the description accurately reflects a read-only operation, adding transparent details beyond the annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections, bullet points, and front-loaded purpose. Every sentence adds value—no fluff. It is appropriately sized for 6 parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description includes a 'Response' section detailing output fields (path, lines, count), making it complete. It covers time filtering, pagination, and file source, addressing all aspects for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, baseline is 3, but the description adds significant meaning: explains when to use `tail`, describes time formats with examples, clarifies offset for pagination, and lists defaults. This goes well beyond the schema by providing usage context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Read lines from a log file' and specifies reading from beginning or end with time filtering. It distinguishes from sibling 'search_log' by noting that tool is for pattern matching, making the purpose specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'When to use' section explicitly states when to use the tool (inspect log portions), when to use `tail` (recent entries), and when not to (use `search_log` for pattern matching). It also advises obtaining paths from `list_logs`, providing clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![logmcp MCP server](https://glama.ai/mcp/servers/kascada/logmcp/badges/card.svg)](https://glama.ai/mcp/servers/kascada/logmcp)

Score Badge

Copy to your README.md:

[![logmcp MCP server](https://glama.ai/mcp/servers/kascada/logmcp/badges/score.svg)](https://glama.ai/mcp/servers/kascada/logmcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kascada/logmcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server