Context Awesome
Server Details
MCP server for accessing curated awesome list documentation
- 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 4.3/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one for discovering sections/categories from awesome lists, the other for retrieving items from a specific section. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern in snake_case: find_awesome_section and get_awesome_items, making them predictable and easy to understand.
With only 2 tools, the set is slightly below the typical 3-15 range, but it is well-scoped for the narrow domain of querying awesome lists. Each tool earns its place.
The tools cover the primary read workflow (discover sections, retrieve items). However, there is no tool to list all sections without a search query or to manage awesome lists, leaving minor gaps.
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.
| 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
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the selection process (name similarity, category relevance, item counts, confidence score) and response format (repository info, item counts, confidence score). While it does not cover potential errors or rate limits, it provides adequate behavioral context for a search tool without annotations.
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 concise and well-structured, with a clear initial statement, bold usage rule, and labeled sections for selection process and response format. No unnecessary information.
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 no annotations or output schema, the description adequately explains what is returned (matching sections with metadata) and the selection logic. It could be more detailed about the exact response structure, but it is sufficient for an agent to understand the tool's purpose and usage.
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 coverage is 100%, so baseline is 3. The description adds minimal additional meaning to parameters beyond the schema; it mentions the role of query in analysis but does not elaborate 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 tool discovers sections/categories across awesome lists matching a search query. It distinguishes itself from the sibling 'get_awesome_items' by explicitly stating that this function must be called first unless the user provides a githubRepo or listId.
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?
Explicit usage guidelines are provided: 'You MUST call this function before get_awesome_items to discover available sections UNLESS the user explicitly provides a githubRepo or listId.' This clearly indicates when to use this tool and when it can be bypassed.
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.
| 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
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions 'token limiting' as a behavioral trait, which is helpful. However, it does not disclose error behavior, authentication needs, rate limits, or the format of returned items. For a retrieval tool, this is marginally adequate but could be more transparent.
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?
Two concise sentences that front-load the purpose and immediately follow with critical usage guidance. No extraneous information; every sentence adds value.
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?
The tool has 6 parameters, no required, no output schema, and no annotations. The description explains the purpose and usage but does not describe the return format or content of items, which is needed for an agent to process results. Without an output schema, the description should compensate, but it does not.
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?
All 6 parameters have descriptions in the input schema (100% coverage), so the description adds no additional semantic value beyond the schema. Baseline 3 is appropriate as the description does not enhance parameter 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 it retrieves items from an awesome list or section with token limiting. It specifies the verb 'Retrieves' and the resource 'awesome items', and distinguishes from the sibling tool 'find_awesome_section' by indicating it should be called after that discovery step.
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?
Explicitly says when to use: after calling 'find_awesome_section' to get section IDs, or directly if the user provides githubRepo or listId. This provides clear guidance on prerequisites and alternative entry points, helping the agent decide when to invoke.
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!