Composite tool. Queries GitHub + HN + npm simultaneously. Returns a unified landscape report with each source's own freshness.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Project idea or keyword e.g. 'mcp server' |
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 indicate read-only and open-world behavior. The description adds the composite nature (queries three sources) and that each source has its own freshness, but does not disclose potential rate limits, failure modes, or how freshness is represented. This is useful but not rich 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?
Three short sentences, front-loaded with 'Composite tool', followed by action and output. No superfluous words.
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?
No output schema, so description must convey return value, which it does at a high level ('unified landscape report with each source's own freshness'). However, it does not detail the report structure or behavior on partial source failures, leaving some gaps for a multi-source tool.
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 covers 100% of the single parameter with a clear example. Description adds that the topic is used to query the three sources, which is a minor addition. Baseline of 3 applies as 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?
Description states it is a composite tool that queries GitHub, HN, and npm simultaneously and returns a unified landscape report. This clearly distinguishes it from single-source siblings like extract_github or extract_hackernews, and from other domain-specific landscape tools by specifying the exact sources.
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 use when a cross-source landscape across GitHub, HN, and npm is needed, contrasting with individual source extractors. However, it does not explicitly name alternatives or state when not to use it, leaving some inference required.
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.