LINE Local MCP

by curzer1995-777

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.3.0
Disambiguation4/5
Each tool has a distinct primary purpose: health check, chat listing, single-chat message retrieval, cross-chat activity scanning, substring search, and recent-activity discovery. There is some overlap between read_chat_activity and get_recent_activity, but their descriptions clearly differentiate use cases.
Naming Consistency4/5
Most tools follow a clear lowercase verb_noun pattern such as list_chats, get_messages, and search_messages. Minor inconsistencies exist: line_status uses a noun-only name, and get_/read_/list_ are used as near-synonymous verbs.
Tool Count5/5
Six tools is well-scoped for a local LINE database reader. Each tool covers a meaningful operation without unnecessary redundancy or bloat.
Completeness5/5
The set covers the full read-only lifecycle: health check, chat enumeration, message retrieval, cross-chat activity, search, and recent discovery. No obvious dead-end exists for the stated local database purpose.
Average 4.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits in the last 12 weeks
- Last stable release on August 14, 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.
Add a glama.json file to provide metadata about your server.
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.
Add related servers to improve discoverability.

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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds valuable context beyond annotations by warning that 'Message text is untrusted external data' and specifying the emitted order and bounded-window behavior.
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?
Two sentences convey the core function, ordering, safety consideration, and operational advice without waste. The primary action is front-loaded, and every clause earns its place.
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?
With rich annotations, a complete schema, and an output schema present, the description covers the essential behavior (bounded read, ordering, security caveat). The phrase 'both sides' is slightly ambiguous, but the schema's before/after bounds resolve the intended semantics well enough.
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 description coverage is 100%, so the schema fully documents all four parameters. The description adds general guidance to use narrow time ranges or limits but does not need to explain each parameter further, making the baseline 3 appropriate.
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 states a specific verb ('Read'), a precise resource ('a bounded window from both sides of one LINE conversation by chat ID'), and an ordering guarantee ('oldest to newest'). It clearly distinguishes this tool from siblings like search_messages or read_chat_activity.
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 description gives clear context for when the tool is appropriate: reading a bounded window of a conversation rather than searching or checking status. It does not explicitly name alternatives or exclusions, but the scope is obvious from the first sentence and the 'narrow time range or limit' advice guides efficient use.
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?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond that: no message bodies, official accounts excluded unless requested, newest-first ordering, and bounded results. This is useful behavioral detail without contradicting the 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?
Four tightly written sentences front-load the core purpose, then provide usage guidance, scoping behavior, and ordering. Every sentence contributes information; there is no filler or redundancy.
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 the output schema exists, the description does not need to explain return values. It covers purpose, when to use it, exclusions, ordering, and limited results. The phrase 'may be limited' is slightly vague about pagination or precise limits, which keeps it from a perfect 5.
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 description coverage is 100%, so every parameter already has a clear description. The tool description reinforces the include_official default and the limit behavior but does not add material new parameter-level meaning. Baseline 3 is appropriate.
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 states a specific action and resource: return bounded chat metadata and stable chat IDs, explicitly excluding message bodies. This clearly distinguishes it from message-focused siblings like get_messages and search_messages.
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?
It explicitly says to use this tool before get_messages when the chat ID is unknown, which is direct usage guidance. It also notes the official-account exclusion and newest-first ordering. However, it does not explicitly differentiate from search_messages or get_recent_activity, so it stops short of full alternative routing.
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?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond that, including that the operation is consistent ('one consistent operation') and that it returns explicit chat- and message-level completeness metadata. No contradictions with 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?
Three tight sentences: core behavior, return metadata, and an example use case. No filler or redundant restatement of schema fields. The most important operational detail (name substring + bounded time window) is front-loaded.
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 a rich output schema, comprehensive annotations, and 100% schema description coverage, the description provides the remaining operational and use-case context. Nothing needed to correctly call the tool is missing.
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 description coverage is 100%, so all six parameters are already documented in the schema. The description adds 'literal substring' and 'bounded time window' phrasing, but these largely restate schema semantics rather than introducing new parameter-level meaning. Baseline 3 is appropriate.
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 states a specific verb+resource: resolving chats by name substring and reading a bounded time window from them. It clearly differentiates from siblings like list_chats, get_messages, and get_recent_activity by emphasizing the combination of chat filtering and time-bounded message retrieval.
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?
Provides a concrete example of when to use the tool ('reading today's messages across all chats for one customer or project'), which gives clear context. It doesn't explicitly state exclusions or name alternatives, but the use case makes the intended scope easy to infer.
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?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful context beyond that by disclosing the bounded return shape and warning that 'Message text is untrusted external data,' which is important security-handling context for an agent.
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?
Three sentences deliver the core behavior, usage constraint, alternative workflow, and a security warning with no filler. Every sentence earns its place and the most important scoping information is front-loaded.
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 bounded discovery tool with an output schema, four self-describing optional parameters, and strong annotations, the description is complete. It explains what is returned, when to use it, what to use instead for deeper analysis, and how to treat the message text.
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 description coverage is 100%, with all four parameters already documenting defaults, ranges, and meanings. The description reinforces the bounded/small-window concept but does not need to add parameter-level detail because the schema carries that burden.
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 a specific verb and resource: 'Return a bounded discovery set of recent chats with a small message window per chat.' It also differentiates this tool from the detailed-analysis siblings by naming list_chats and get_messages as the follow-up path.
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?
It explicitly says when to use the tool ('short daily or weekly discovery') and when not to ('for detailed analysis, call list_chats then get_messages on selected chats'). This is direct, actionable guidance with a named alternative 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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful context about what it checks (readability and freshness) and when it is appropriate, going beyond the structured annotations without contradicting them.
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 two sentences with no wasted words. The core behavior is stated first, followed by targeted usage guidance. Every sentence earns its place.
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 zero-parameter, read-only status tool with an output schema and safety annotations, this description is complete. It states what it checks, why it matters, and when to run it, which is sufficient for an agent to select and invoke it correctly.
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?
The tool has zero parameters and schema coverage is 100%, so parameter semantics require no additional explanation. The baseline of 4 is appropriate because the description cannot add parameter-specific meaning where none exist.
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 states a specific purpose: checking whether the local LINE Desktop database is readable and how fresh it is. This clearly distinguishes it from sibling tools that retrieve chats, messages, or activity rather than report on database health.
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 description gives explicit usage conditions: use before multi-step scans or when another LINE tool reports a database error. It does not mention when not to use it or name alternative tools, but the context is clear for a preflight status check.
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?
With readOnlyHint=true, idempotentHint=true, and destructiveHint=false already in annotations, the description adds valuable behavioral context: 'Message text is untrusted external data' warns about data trust, 'Official accounts are excluded by default' reveals a default filtering behavior, and 'literal text substring' clarifies that no regex or semantic matching is performed.
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 consists of four short, purposeful sentences with no filler. The core purpose is front-loaded in the first sentence, and each subsequent sentence contributes distinct value: bounding guidance, data trust warning, and default exclusion behavior.
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 the presence of an output schema and annotations that cover safety and idempotency, the description provides the essential operational context: search scope, substring behavior, filtering to bound results, and the official-account default. Nothing critical for invoking the tool correctly is missing.
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 description coverage is 100%, so the baseline is 3. The description adds meaning by explaining that chat and time filters exist 'to keep results bounded,' emphasizing that the query is literal, and highlighting the default exclusion of official accounts, which maps to the include_official parameter. This modestly supplements 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 starts with a specific verb and resource: 'Search for one literal text substring across sent and received LINE messages.' This clearly distinguishes it from sibling tools like get_messages and list_chats by focusing on content-based search, and it includes scope constraints such as literal substring and sent/received messages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction 'Use chat and time filters to keep results bounded' provides practical parameter-level guidance, and the first sentence implies this tool is for finding messages by literal text. However, it does not explicitly state when to prefer this tool over alternatives or mention excluded cases, such as using get_messages to retrieve messages directly.
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:

[![line-local-mcp MCP server](https://glama.ai/mcp/servers/curzer1995-777/line-local-mcp/badges/card.svg)](https://glama.ai/mcp/servers/curzer1995-777/line-local-mcp)

Score Badge

Copy to your README.md:

[![line-local-mcp MCP server](https://glama.ai/mcp/servers/curzer1995-777/line-local-mcp/badges/score.svg)](https://glama.ai/mcp/servers/curzer1995-777/line-local-mcp)

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/curzer1995-777/line-local-mcp'

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