Extract real-time data from a GitHub repository — README, stars, forks, last commit, topics. Returns timestamped freshcontext.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full GitHub repo URL e.g. https://github.com/owner/repo |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and open-world behavior. The description adds useful context by specifying 'real-time data' and 'timestamped freshcontext', plus listing the fields returned. It goes beyond annotations, though it omits potential limitations like rate limits.
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 sentence that front-loads the primary action and resource, followed by a concise list of extracted data and output characteristics. Every phrase adds value without redundancy.
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?
With no output schema, the description must convey return expectations. It lists data fields and mentions timestamps, but the term 'freshcontext' is vague and does not clarify the response format. Adequate for a simple tool but leaves room for ambiguity.
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 fully covers the single URL parameter with format and example, and the description does not add further parameter semantics. With 100% schema coverage, the baseline score of 3 applies.
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: extracting real-time data from a GitHub repository, listing specific data elements (README, stars, forks, last commit, topics). This distinguishes it from sibling extraction tools for other domains.
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 needing data from a specific GitHub repository, but it does not explicitly provide when-to-use guidance, exclusions, or alternatives. No mention of when to prefer this over related tools like search_repos.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct data source (finance, GitHub, Hacker News, etc.), with clear separation and no overlap. An agent can easily distinguish which tool to use for a given source.
Tools use a consistent verb_noun pattern with 'extract_' for data extraction and 'search_' for search functions. The outlier 'package_trends' is still descriptive and fits the theme, so the pattern is mostly predictable.
11 tools is well-scoped for a data aggregation server. Each tool serves a clear purpose and the count is neither too sparse nor overwhelming.
The server covers a broad range of sources (finance, code, news, social, academia, jobs, packages). Minor gaps like missing Twitter or general news are acceptable given the breadth already provided.