read_feedRead a curated technology feed by its id (from list_feeds). Returns normalized items (title, link, published, summary). Optionally filter items by keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| feed | Yes | Curated feed id (from list_feeds). | |
| limit | No | Max items (1-50, default 20). | |
| query | No | Keyword filter over item title/summary. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "feed": "hn-top-stories",
+ "limit": 10
+ },
+ {
+ "feed": "techcrunch-news",
+ "query": "startup funding"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false. Description adds value by detailing return format (title, link, published, summary) and optional keyword filtering. No contradictions. Adds useful behavioral context beyond 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?
Single sentence, front-loaded with action and result, no extraneous words. Efficient and clear.
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 3 parameters, no output schema, and strong annotations, the description adequately covers return format and optional filter. Lacks explanation of 'normalized' and pagination details, but these are not critical. Reasonably complete.
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. Description adds minor extra context: links feed id to list_feeds and clarifies query as 'keyword filter over item title/summary'. Does not significantly improve understanding beyond schema.
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 clearly states 'Read a curated technology feed by its id' with specific verb and resource. It mentions 'from list_feeds' which differentiates from list_feeds but does not explicitly differentiate from sibling fetch_feed. Purpose is clear but sibling distinction could be stronger.
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?
Implies usage context by mentioning 'from list_feeds', suggesting a prerequisite. However, no explicit when-to-use or when-not-to-use compared to alternative tools like fetch_feed. Lacks guidance on exclusions.
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.
Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants, bet_research vs. polymarket_edges, and several entity/profile tools. An agent could easily misselect between similar tools despite detailed descriptions.
Tool names are inconsistent: some use snake_case (ask_pipeworx), some camelCase (ai_visibility_check), and verbs vary (ask, bet, compare, scan, validate). No clear naming convention is followed across the set.
With 34 tools, the server covers a broad range of functionalities for a platform like Pipeworx. While each tool seems justified, the count is on the higher end, potentially overwhelming for simpler use cases.
The tool set covers data retrieval, memory, subscriptions, Polymarket analysis, and utility functions comprehensively. Minor gaps exist (e.g., no direct user management) but core workflows are well-supported.