A
license-
qualityD
maintenanceMCP server for SecretiveShell/Awesome-llms-txt. Add documentation directly into your conversation via MCP resources.
24
MIT
Context Awesome
Server Details
MCP server for accessing curated awesome list documentation
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- bh-rat/context-awesome
- GitHub Stars
- 57
- Server Listing
- context-awesome
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
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.
100% free. Your data is private.
Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.4/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools have clearly distinct purposes: one discovers sections/categories, the other retrieves items. No overlap in functionality.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern (find_awesome_section, get_awesome_items), making the set predictable.
Tool Count4/5
Two tools is minimal but appropriate for a focused server that covers discovery and retrieval. Could benefit from a listing tool, but not excessive.
Completeness4/5
Covers the core workflow: find section then get items. Missing a direct list-all-sections function, but the search-based discovery is sufficient for most cases.
Available Tools
2 toolsfind_awesome_sectionAInspect
Discovers sections/categories across awesome lists matching a search query and returns matching sections from awesome lists.
You MUST call this function before 'get_awesome_items' to discover available sections UNLESS the user explicitly provides a githubRepo or listId.
Selection Process:
Analyze the query to understand what type of resources the user is looking for
Return the most relevant matches based on:
Name similarity to the query and the awesome lists section
Category/section relevance of the awesome lists
Number of items in the section
Confidence score
Response Format:
Returns matching sections of the awesome lists with metadata
Includes repository information, item counts, and confidence score
Use the githubRepo or listId with relevant sections from results for get_awesome_items
For ambiguous queries, multiple relevant sections will be returned for the user to choose from.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum sections to return | |
| query | Yes | Search terms for finding sections across awesome lists | |
| confidence | No | Minimum confidence score (0-1) |
Tool Definition Quality
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool's behavior: searches across awesome lists, returns matching sections with metadata, and for ambiguous queries returns multiple options. It does not mention destructive actions or auth, but that is acceptable 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, usage rule, selection process, response format, ambiguous queries). It is appropriately sized, with no redundant sentences, though it could be slightly more concise.
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 complexity (search/discovery) and lack of output schema, the description adequately explains what is returned (matching sections with metadata) and the selection criteria. It also connects to the sibling tool, covering key contextual needs.
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 already documents all three parameters. The description adds limited additional meaning beyond the schema, such as mentioning confidence in the selection process, but does not elaborate on format or constraints beyond what is in 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 'Discovers sections/categories across awesome lists matching a search query' and distinguishes from sibling tool 'get_awesome_items' by positioning it as a prerequisite. The verb 'Discovers' and resource 'sections/categories' are 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?
Provides explicit guidance: 'You MUST call this function before get_awesome_items to discover available sections UNLESS the user explicitly provides a githubRepo or listId.' Also outlines the selection process and when multiple results are returned, giving 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.
get_awesome_itemsAInspect
Retrieves items from a specific awesome list or section with token limiting. You must call 'find_awesome_section' first to discover available sections, UNLESS the user explicitly provides a githubRepo or listId.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| listId | No | UUID of the list (from find_awesome_section results) | |
| offset | No | Pagination offset for retrieving more items | |
| tokens | No | Maximum number of tokens to return (default: 10000). Higher values provide more items but consume more tokens. | |
| section | No | Category/section name to filter | |
| githubRepo | No | GitHub repo path (e.g., 'sindresorhus/awesome') from find_awesome_section results | |
| subcategory | No | Subcategory to filter |
Tool Definition Quality
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It reveals token limiting and retrieval nature (no mutation implied). However, it does not explicitly state that the tool is read-only or describe side effects. Lacks details on output format or pagination behavior. Still, the description is largely transparent about core 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 concise sentences, no superfluous information. The key purpose and prerequisite are front-loaded. Every sentence adds value.
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 annotations or output schema, the description covers the main behavioral aspects (retrieval, token limiting, prerequisite). However, it lacks details on pagination behavior and return format, which are relevant for a retrieval tool. Overall, it is sufficient but not exhaustively 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 description coverage is 100%, so schema already documents all parameters. The description adds context by explaining when to use certain parameters (e.g., 'You must call find_awesome_section first... UNLESS the user explicitly provides a githubRepo or listId'). This provides usage guidance but does not add semantic depth beyond the schema for individual 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?
Description clearly states 'Retrieves items from a specific awesome list or section with token limiting.' The verb 'Retrieves' and resource 'awesome list or section' are specific. It differentiates from sibling tool 'find_awesome_section' by noting the prerequisite call, establishing its distinct role.
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 states when to use: 'You must call find_awesome_section first... UNLESS the user explicitly provides a githubRepo or listId.' This gives clear context and exclusion, directly guiding the agent on prerequisites and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- Alicense-qualityDmaintenanceAn MCP server that provides version-pinned, deterministic documentation sourced from DevDocs.io to AI assistants (Claude, RooCode, Cline, Copilot etc.) and also via offline mode. Not via Scraping! But using the supported downloading option from devdocs.4913MIT
- AlicenseAqualityDmaintenanceSearch and explore a catalog of 2,000+ MCP servers — full-text search, trending, hot, categories, and side-by-side comparison — from any MCP client. A zero-dependency stdio bridge over the hosted awesome-mcp.tools endpoint.820MIT
- AlicenseAqualityDmaintenanceMCP server that fetches and searches the latest stable documentation for any package from PyPI, npm, and crates.io.5MIT
Try in Browser
Your Connectors
Sign in to create a connector for this server.