Tonr Music
Server Details
Free, copyright-safe AI music library for video creators and AI agents.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.5/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose with no overlap: find_similar for semantic similarity, get_download_url for downloading, get_track for detailed metadata, and search_music for library queries. The descriptions clearly differentiate their functions, eliminating any risk of misselection.
All tool names follow a consistent verb_noun pattern in snake_case: find_similar, get_download_url, get_track, and search_music. This uniformity makes the set predictable and easy to understand, with no deviations in style or convention.
With 4 tools, the count is reasonable for a music library server, covering core operations like searching, retrieving metadata, downloading, and finding similar tracks. It might feel slightly thin for broader music management, but it's well-scoped for the apparent purpose without bloat.
The tools cover essential music library functions: search, retrieval, downloading, and similarity finding. Minor gaps exist, such as no explicit update or delete operations for tracks, but these may be unnecessary for a read-heavy domain, and agents can likely work around this with the provided tools.
Available Tools
4 toolsfind_similarBInspect
Find tracks similar to a given track using Tonr's semantic embeddings.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of similar tracks (max 10) | |
| track_id | Yes | Track ID to find similar tracks for |
Tool Definition Quality
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 the method ('semantic embeddings') but doesn't disclose behavioral traits such as performance characteristics, rate limits, authentication needs, or what the output looks like (e.g., format, ordering). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, front-loading the core purpose. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (semantic similarity search), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values, error conditions, or behavioral context needed for effective use, leaving gaps in understanding how the tool behaves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('track_id' and 'limit' with default and max). The description adds no additional meaning beyond what the schema provides, such as explaining how similarity is calculated or the implications of the limit. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('find') and resource ('tracks similar to a given track'), specifying the semantic embedding method ('using Tonr's semantic embeddings'). It distinguishes from siblings like 'get_track' (fetch single track) and 'search_music' (general search), but doesn't explicitly contrast with 'get_download_url'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when similarity based on embeddings is needed, but doesn't explicitly state when to use this tool versus alternatives like 'search_music' or provide exclusions. It lacks guidance on prerequisites or constraints beyond the parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_download_urlAInspect
Get a download URL for a Tonr track. Download access depends on the caller's plan.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Audio format | mp3 |
| track_id | Yes | Track ID |
Tool Definition Quality
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 adds useful context about plan-dependent access, which is a key behavioral trait not evident from the schema. However, it lacks details on other aspects like rate limits, authentication needs, or what happens on failure (e.g., error messages), leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and adds a crucial behavioral note without any wasted words. Every part earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and a key behavioral constraint (plan dependency), but lacks details on return values (since no output schema), error handling, or full usage scenarios, leaving room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents both parameters ('track_id' and 'format' with enum values). The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining the significance of 'track_id' or format choices. Thus, it meets the baseline but doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'download URL for a Tonr track', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_track' (which likely retrieves metadata rather than a download URL), so it falls short of 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context by mentioning 'Download access depends on the caller's plan', which suggests when this tool might fail or require specific conditions. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_track' or 'search_music', nor does it provide clear exclusions or prerequisites beyond the plan dependency.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_trackAInspect
Get complete metadata for a specific Tonr track. Returns all details including mood arc, instruments, use cases, and preview URL.
| Name | Required | Description | Default |
|---|---|---|---|
| track_id | Yes | Track ID (starts with tnr_) |
Tool Definition Quality
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 return data (metadata details) but does not cover aspects like error handling, rate limits, authentication needs, or whether it's a read-only operation. The description adds value by specifying the scope of metadata but lacks comprehensive 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence and efficiently lists the returned details in the second sentence. Every sentence earns its place by providing essential information without redundancy or unnecessary elaboration, making it appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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, no output schema, no annotations), the description is reasonably complete. It explains what the tool does and what data it returns, though it could benefit from more behavioral details like error cases or performance expectations. Without an output schema, it adequately covers the expected return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'track_id' documented as 'Track ID (starts with tnr_)'. The description does not add any additional meaning beyond this, such as format examples or validation rules, so it meets the baseline for high schema coverage without compensating further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get complete metadata') and resource ('for a specific Tonr track'), distinguishing it from sibling tools like 'find_similar' (similarity search), 'get_download_url' (download functionality), and 'search_music' (broad search). It specifies the scope of returned data including mood arc, instruments, use cases, and preview URL.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when detailed metadata for a specific track is needed, but it does not explicitly state when to use this tool versus alternatives like 'search_music' for broader queries or 'find_similar' for related tracks. No exclusions or prerequisites are mentioned, leaving some ambiguity about optimal use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_musicBInspect
Search the Tonr music library. Use natural language queries like 'upbeat music for a cooking video' or structured filters. Returns tracks with preview URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| mood | No | Filter by mood (happy, sad, calm, dark, epic, romantic, energetic, mysterious, etc.) | |
| genre | No | Filter by genre (cinematic, electronic, ambient, acoustic, lo-fi, etc.) | |
| limit | No | Number of results (max 20) | |
| query | No | Natural language search query | |
| bpm_max | No | Maximum tempo in BPM | |
| bpm_min | No | Minimum tempo in BPM | |
| energy_max | No | Maximum energy between 0 and 1 | |
| energy_min | No | Minimum energy between 0 and 1 | |
| has_vocals | No | Filter for vocal (true) or instrumental (false) tracks | |
| instruments | No | Filter by one or more instruments | |
| video_theme | No | Filter by video type (travel, corporate, wedding, gaming, vlog, tutorial, etc.) | |
| duration_max | No | Maximum duration in seconds | |
| duration_min | No | Minimum duration in seconds |
Tool Definition Quality
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. It mentions that the tool 'returns tracks with preview URLs', which gives some output information, but doesn't cover important behavioral aspects like rate limits, authentication requirements, pagination, error handling, or whether this is a read-only operation. The description is insufficient for a tool with 13 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the purpose and usage method, while the second specifies the return format. There's zero waste or redundancy in the wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex search tool with 13 parameters and no output schema, the description is incomplete. It doesn't explain the return format beyond 'tracks with preview URLs', doesn't mention result ordering, pagination, or error cases. With no annotations and rich parameter schema, the description should provide more behavioral and output context to be truly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 13 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'natural language queries' which relates to the 'query' parameter, but doesn't provide additional semantic context or usage examples for the parameters. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search the Tonr music library'), the resource ('music library'), and distinguishes from siblings by specifying it returns tracks with preview URLs. It provides concrete examples of natural language queries, making the purpose immediately understandable and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning natural language queries and structured filters, but doesn't explicitly state when to use this tool versus sibling tools like 'find_similar' or 'get_track'. It provides some guidance on query types but lacks explicit alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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