Ableton MCP

by Dysron

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: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between 'find_track' and 'list_tracks' that could cause confusion, as both involve retrieving track information. However, their specific functions (search vs. list all) are clarified in descriptions, preventing major misselection issues.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun structure, such as 'export_selected_track', 'find_track', and 'list_tracks'. This predictability makes it easy for agents to understand and use the toolset without naming confusion.
Tool Count5/5
With 9 tools, the server is well-scoped for managing Ableton Live tracks, covering operations like listing, finding, selecting, preparing, and exporting. Each tool serves a specific role without unnecessary redundancy, making the count appropriate for the domain.
Completeness4/5
The toolset provides good coverage for track management and export workflows, including preparation and range setting. However, there are minor gaps, such as no tools for modifying track properties (e.g., mute, volume) or handling non-track elements like clips or effects, which agents might need to work around.
Average 3.3/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists tracks but doesn't mention critical details like whether it's read-only, requires specific permissions, or has any side effects. This is inadequate for a tool that interacts with a live session.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by parameter and return details. It avoids unnecessary fluff, though the structure could be slightly more polished (e.g., bullet points).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema, the description is somewhat complete but lacks depth. It doesn't cover behavioral aspects or usage guidelines, which are needed for effective agent operation in a session context.
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?
The description adds meaning by explaining that 'include_clips' controls clip count inclusion, which the input schema (with 0% description coverage) lacks. However, it doesn't detail how clip counts are formatted or other parameter nuances, so it partially compensates but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('tracks in the current Ableton Live session'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'find_track' or 'get_track_info', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'find_track' or 'list_groups'. It mentions an optional parameter but doesn't explain scenarios for its use, leaving the agent with minimal context for tool selection.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool sets a range for export but doesn't clarify whether this is a configuration change, if it affects existing exports, requires specific permissions, or has side effects like modifying project data. The mention of 'Returns: Confirmation with time info' adds some context but is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by structured sections for Args and Returns, making it efficient and easy to parse. However, the 'Returns' section is somewhat vague ('Confirmation with time info'), which slightly reduces clarity without adding unnecessary length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which should detail the return values), the description doesn't need to fully explain returns. However, for a mutation tool with no annotations and 2 parameters, it lacks details on behavioral traits (e.g., side effects, permissions) and usage context, making it minimally adequate but with clear gaps in guiding the agent.
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?
The description explicitly lists the two parameters ('start_beats' and 'length_beats') and provides basic semantic meaning ('Start position in beats', 'Length in beats'), which adds value beyond the input schema's 0% description coverage. However, it doesn't explain units, valid ranges, or how these parameters interact with the export process, leaving gaps in understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Set') and resource ('loop/punch range for export'), making it immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'export_selected_track' or 'prepare_track_for_export', which might have overlapping export-related functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., whether a track must be selected first), or exclusions, leaving the agent to infer usage from the purpose alone.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Select a track' implies a UI/state change operation, it doesn't specify whether this requires specific conditions (like Live being in a particular mode), what happens if the index is invalid, or whether this action is reversible. The confirmation message return is mentioned but not elaborated.
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 extremely concise and well-structured: a clear purpose statement followed by brief Arg and Return sections. Every sentence earns its place with no redundant information, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter mutation in a music production context) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete parameter guidance, it leaves gaps in behavioral understanding that could affect reliable tool invocation.
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?
The description adds minimal parameter semantics beyond the schema: it explains that 'track_index' represents 'The index of the track to select (0-based)'. With 0% schema description coverage, this provides basic clarification but doesn't compensate fully for the coverage gap (e.g., no range constraints or validation rules).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Select a track') and target system ('in Ableton Live'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential sibling tools like 'find_track' or 'list_tracks', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'find_track' or 'list_tracks'. It doesn't mention prerequisites, context requirements, or any explicit when/when-not scenarios, leaving the agent to infer usage from the tool name alone.
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?
No annotations are provided, so the description carries the full burden. It mentions partial matching behavior, which is useful, but lacks details on permissions, rate limits, pagination, or error handling. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
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 highly concise and well-structured: a clear purpose statement followed by separate 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete behavioral details, it could benefit from more context on usage and limitations.
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 description adds meaningful context for the single parameter: 'Text to search for in track names' clarifies that 'name' is used for partial matching. With 0% schema description coverage, this compensates well, though it doesn't specify format constraints like case sensitivity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find tracks by name (partial match).' It specifies the verb ('find'), resource ('tracks'), and scope ('by name'), though it doesn't explicitly differentiate from siblings like 'list_tracks' or 'get_track_info'. This makes it clear but not fully sibling-aware.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'list_tracks' (which might list all tracks without filtering) or 'get_track_info' (which might retrieve details for a specific track), leaving the agent to infer usage context.
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?
No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions what information is returned (name, type, mute status, clips), it doesn't address critical aspects like whether this is a read-only operation, potential error conditions (e.g., invalid index), or performance characteristics. The description adds some value but leaves significant behavioral gaps.
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 perfectly structured and concise: a clear purpose statement followed by organized 'Args' and 'Returns' sections. Every sentence earns its place, with no wasted words, making it easy to scan and understand quickly.
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 tool's moderate complexity (single parameter, read-oriented operation) and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the purpose, parameter semantics, and return content at a high level. The main gap is the lack of usage guidance relative to sibling tools.
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 description adds meaningful context for the single parameter 'track_index' by specifying it's '0-based', which isn't captured in the input schema (which has 0% description coverage). This clarification is crucial for correct usage. However, it doesn't explain valid ranges or constraints beyond the 0-based nature.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information about a specific track'), making it immediately understandable. However, it doesn't distinguish this tool from sibling tools like 'find_track' or 'select_track_by_index', which likely have related but different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'find_track' and 'select_track_by_index' available, there's no indication of when this retrieval tool is preferred over those other track-related operations, leaving usage context unclear.
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?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: GUI automation method, platform restriction ('macOS only'), and permission requirements ('Accessibility permissions for Terminal/Python'). However, it lacks details on what gets exported (e.g., file format, content), potential side effects, error handling, or performance implications like timeouts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose, followed by important notes and parameter/return details. Each sentence adds value: the first defines the action, the second covers critical constraints, and the last two explain inputs/outputs. Minor improvements could include bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (GUI automation with permissions) and no annotations, the description is moderately complete. It covers purpose, constraints, and parameter semantics, and an output schema exists, so return values needn't be explained. However, it lacks details on export specifics (e.g., format, range) and error scenarios, which could hinder agent usage.
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 description adds meaningful context for the single parameter 'output_folder' by stating 'Where to save the exported file', which clarifies its purpose beyond the schema's title 'Output Folder'. With 0% schema description coverage and only one parameter, this compensates adequately. However, it doesn't specify allowed paths or constraints beyond the default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Export') and target resource ('currently selected track'), and specifies the method ('using GUI automation'). It distinguishes from siblings like 'prepare_track_for_export' by focusing on execution rather than preparation. However, it doesn't explicitly differentiate from all siblings (e.g., 'set_export_range' might relate to export configuration).
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 description provides some context with 'IMPORTANT: Requires Accessibility permissions for Terminal/Python. macOS only.' and implies usage when a track is selected. However, it doesn't explicitly state when to use this tool versus alternatives like 'prepare_track_for_export' or 'set_export_range', nor does it mention prerequisites like needing to select a track first using 'select_track_by_index'.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'List all group tracks' but doesn't describe return format, pagination, permissions needed, or potential side effects. For a read operation with zero annotation coverage, this is a significant gap in transparency, though it doesn't contradict any 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?
The description is a single, efficient sentence that front-loads the core action ('List all group tracks') and adds necessary context ('folders' clarification and 'in the current Live session'). There is zero waste, making it appropriately sized and well-structured for its 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 the tool's simplicity (0 parameters, output schema exists, no annotations), the description is reasonably complete. It specifies what is listed and the context, though it could benefit from more behavioral details like return format. With an output schema handling return values, the description doesn't need to explain those, making it adequate for this low-complexity tool.
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 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description adds no parameter-specific information, which is appropriate here. Baseline for 0 parameters is 4, as no additional semantic detail is needed beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('group tracks (folders)'), specifying the scope ('in the current Live session'). It distinguishes from siblings like 'list_tracks' by focusing on groups/folders rather than individual tracks. However, it doesn't explicitly contrast with other group-related tools if any existed, keeping it from a perfect 5.
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 description implies usage when needing to see all group tracks in the current session, but provides no explicit guidance on when to use this versus alternatives like 'list_tracks' or 'find_track'. It lacks any 'when-not' scenarios or prerequisites, leaving usage contextually inferred rather than clearly defined.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's actions (selecting a track and setting loop range based on audio clips) and hints at a workflow with 'export_selected_track', but lacks details on permissions, side effects, or error handling. It doesn't contradict annotations, but could be more comprehensive for a mutation tool.
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 and concise, with a clear purpose statement followed by 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
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 tool has one parameter with 0% schema coverage and an output schema exists (so return values are documented elsewhere), the description does a good job covering the essential context. It explains the tool's role in a workflow and parameter usage, though it could benefit from more behavioral details like error cases or dependencies.
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 description adds meaningful context for the single parameter 'track_index' by explaining it's used to identify which track to prepare, which is helpful given the schema has 0% description coverage. However, it doesn't specify valid ranges or constraints (e.g., if indices start at 0 or 1), leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('prepare', 'select', 'set') and resources ('track', 'loop range', 'audio clips'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from siblings like 'select_track_by_index' or 'set_export_range', which have overlapping functionality.
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 description implies usage context by mentioning 'ready for export_selected_track', suggesting this tool should be used before that sibling. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'select_track_by_index' or 'set_export_range', nor does it specify prerequisites or exclusions.
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?
With no annotations provided, the description carries the full burden. It discloses the tool's behavior (testing connectivity/status) but does not detail traits like error handling, response format, or performance characteristics. It meets a minimal baseline by stating what it does but lacks richer behavioral context.
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 a single, efficient sentence that directly states the tool's purpose with no wasted words. It is front-loaded and appropriately sized for a simple diagnostic tool, making it easy to understand quickly.
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 tool's simplicity (0 parameters, no annotations, but has an output schema), the description is complete enough for its purpose. It clearly defines what the tool tests, and with an output schema, additional details on return values are unnecessary. However, it could slightly enhance context by mentioning typical use cases.
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 0 parameters with 100% schema description coverage, so no parameter information is needed. The description appropriately focuses on the tool's purpose without redundant parameter details, aligning with the baseline 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?
The description clearly states the specific action ('Test if') and the target resources ('Ableton Live is running and AbletonOSC is enabled'), distinguishing it from sibling tools that focus on track manipulation, export, or listing. It precisely defines what the tool checks without ambiguity.
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 description implies usage context (e.g., verifying Ableton Live and OSC status before other operations) but does not explicitly state when to use it versus alternatives or provide any exclusions. It offers basic guidance through implication but lacks explicit directives.
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:

[![ableton-mcp MCP server](https://glama.ai/mcp/servers/Dysron/ableton-mcp/badges/card.svg)](https://glama.ai/mcp/servers/Dysron/ableton-mcp)

Score Badge

Copy to your README.md:

[![ableton-mcp MCP server](https://glama.ai/mcp/servers/Dysron/ableton-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Dysron/ableton-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/Dysron/ableton-mcp'

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