fetch_feedFetch and normalize any RSS / Atom / RDF feed by URL. CF-robust: fetches directly and falls back to a proxy if the source blocks the gateway. Use list_feeds first for curated sources.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Feed URL, e.g. "https://news.ycombinator.com/rss". | |
| 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: +[
+ {
+ "url": "https://news.ycombinator.com/rss"
+ },
+ {
+ "limit": 15,
+ "query": "security",
+ "url": "https://feeds.arstechnica.com/arstechnica/index"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds that it normalizes feeds and is CF-robust with proxy fallback, providing behavioral context beyond annotations without contradiction.
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 sentences, front-loaded with core purpose, no fluff. Every sentence adds value: main function, robustness note, and usage guidance.
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 the tool's moderate complexity, the description covers fetching, normalization, fallback, and usage guidance. Schema covers all parameters. No output schema exists, but the description sufficiently explains behavior for agent invocation.
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 description coverage is 100%, so the schema fully documents parameters. Description adds no extra parameter-level detail beyond what's in the schema, meeting baseline expectations.
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 fetches and normalizes RSS/Atom/RDF feeds by URL, specifying verb, resource, and feed types. It distinguishes from sibling list_feeds which lists curated 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?
Explicitly advises to 'Use list_feeds first for curated sources,' providing context for when to use this tool vs. a sibling. Does not cover all alternatives but gives clear guidance for one key distinction.
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.