BytesAgain AI Skills Search
Server Details
Search 60,000+ AI agent skills via MCP. Supports 7 languages (EN/ZH/JA/KO/DE/FR/ES). Free, no auth required.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.2/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: get_skill retrieves specific skill details, popular_skills lists top downloads, and search_skills performs keyword searches across languages. No overlap exists between these functions, making tool selection unambiguous.
All tools follow a consistent verb_noun pattern with snake_case: get_skill, popular_skills, and search_skills. The naming is predictable and readable throughout the set.
With only 3 tools, the set feels thin for a server focused on searching 60,000+ AI skills. While the tools cover basic retrieval and search, the scope suggests potential gaps in functionality like filtering, sorting, or managing skills.
The tools provide core search and retrieval operations but lack update, delete, or creation capabilities, which might be expected for a skill management domain. However, for a read-only search interface, the coverage is reasonable though minimal.
Available Tools
3 toolsget_skillCInspect
Fetch complete details for a single skill by its unique slug. Returns full metadata: name, description, category, tags, version, author, downloads, stars, source URL, and install instructions. Use after search_skills to get more info about a specific skill. Returns error if slug not found.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Unique skill slug from search results. Example: "chart-generator" or "email-automation". |
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. While 'Get full details' implies a read operation, it doesn't specify whether this requires authentication, has rate limits, returns structured data, or handles errors. For a tool with zero annotation coverage, this represents significant gaps in 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 a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a simple lookup tool and front-loads the essential information effectively.
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 tool with no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain what 'full details' includes, how to interpret the slug parameter, or what format the response takes. The agent would need to guess about the tool's behavior and output.
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 description mentions the single parameter ('by slug') but provides no additional semantic context beyond what's implied by the parameter name. With 0% schema description coverage and only one parameter, this meets the baseline expectation but doesn't add meaningful value beyond the minimal schema information.
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 action ('Get full details') and target resource ('for a specific skill by slug'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from its siblings (popular_skills and search_skills), which would require explicit differentiation to earn a 5.
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 no guidance on when to use this tool versus its siblings (popular_skills and search_skills). It doesn't mention alternatives, exclusions, or contextual prerequisites, leaving the agent with insufficient information to make informed selection decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
popular_skillsCInspect
Get the most popular AI agent skills ranked by download count. Returns top skills with slug, name, description, category, downloads, stars. Use when user wants to discover trending skills without a specific topic. Ideal for onboarding. Default returns top 20.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of top skills to return. Default: 20. Max: 50. |
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 implies a read-only operation ('Get'), but lacks details on permissions, rate limits, pagination, or what the return format looks like. This is a significant gap for a tool with zero 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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain behavioral traits like safety or return values, and while the schema covers parameters well, the overall context for agent usage is insufficient, especially compared to sibling tools.
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 'limit' parameter fully documented. The description doesn't add any parameter details beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without extra value.
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 tool's purpose with a specific verb ('Get') and resource ('top skills by download count'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_skill' or 'search_skills', 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.
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 its siblings ('get_skill' and 'search_skills'), nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_skillsAInspect
Search 60,000+ AI agent skills by keyword or natural language query. Supports 7 languages (EN/ZH/JA/KO/DE/FR/ES). Returns skills with slug, name, description, category, downloads, stars. Results ranked by relevance then popularity. Use when user wants to find skills for a specific task. Example: "email automation" or "邮件自动化".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results. Default: 10. Max: 50. | |
| query | No | Search keyword in any supported language. Example: "data analysis" or "数据分析". |
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 discloses the scale ('60,000+ AI agent skills') and language support, which adds useful context beyond basic functionality. However, it lacks details on behavioral traits such as rate limits, authentication needs, pagination, or response format, leaving gaps for a search tool.
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 functionality ('Search 60,000+ AI agent skills by keyword') and adds essential context (language support). Every word earns its place with no redundancy or waste.
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 (search with parameters), no annotations, and no output schema, the description is incomplete. It covers purpose and scope but lacks details on behavioral traits, output format, or error handling. It is adequate as a minimum viable description but has clear gaps for effective agent use.
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 fully documents the two parameters ('limit' and 'query'). The description does not add any parameter-specific details beyond what the schema provides, such as search syntax or language-specific query handling. 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 specific action ('Search') and resource ('60,000+ AI agent skills'), distinguishing it from sibling tools like 'get_skill' (likely retrieves a specific skill) and 'popular_skills' (likely lists trending skills). It specifies the scope (keyword-based search) and supported languages, making the purpose explicit 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 for keyword-based searches across multiple languages, but does not explicitly state when to use this tool versus alternatives like 'get_skill' or 'popular_skills'. It provides context (searching by keyword) but lacks explicit guidance on exclusions or comparisons to siblings.
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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{
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