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://www.designernews.co/feed"
+ },
+ {
+ "limit": 15,
+ "query": "poster",
+ "url": "https://www.instagram.com/explore/tags/graphicdesign/feed/"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive traits. The description adds value by revealing normalization behavior and a proxy fallback mechanism for blocked sources. No contradictions or missing critical behavioral 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?
Two sentences, no filler. The first sentence covers core purpose and normalization; the second adds robustness and usage guidance. Front-loaded and efficient.
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 simple fetch tool with well-documented parameters and good annotations, the description covers key aspects: what it fetches, normalization, proxy fallback, and guidance. Lack of output schema is mitigated by the tool's simplicity, but a brief note on return format would improve completeness.
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%, with each parameter already described clearly. The description does not add additional semantic nuance beyond what is in the schema, so baseline score of 3 is appropriate.
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 verb (fetch) and resource (RSS/Atom/RDF feed by URL), and distinguishes from siblings by specifying normalization and the suggestion to use list_feeds for curated sources. It covers feed types and proxy fallback, making the tool's scope unambiguous.
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,' which provides clear context for when this tool is appropriate versus siblings. However, it does not explicitly state when not to use it or mention alternatives beyond list_feeds.
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.
Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose, with the beta variant currently identical to the stable version. Similarly, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all target prediction-market analysis with fuzzy boundaries, and ai_visibility_check is effectively wrapped by scan_competitor_ai_presence. The detailed descriptions help, but an agent would frequently need to read large descriptions to pick correctly.
All tool names use snake_case and many are readable verb_noun constructions (list_feeds, read_feed, fetch_feed, resolve_entity, validate_claim). However, conventions are mixed: some are bare verbs (remember, recall, forget), some are adjective_noun (recent_alerts, recent_changes), and some use domain prefixes or generic labels (ask_pipeworx, polymarket_edges, entity_profile). The naming is not chaotic, but it does not follow a single predictable pattern.
With 34 tools, the server is overstuffed for its stated 'Design Feeds' name, which only accounts for list_feeds, read_feed, and fetch_feed. The vast majority of tools belong to unrelated domains like investment research, prediction markets, entity resolution, and memory management, making the overall surface feel like a general-purpose toolkit rather than a focused design feed server.
For the design-feed domain, only list/read/fetch operations exist; there are no create, update, or delete feed-management tools, and the subscribe tool does not support design feeds. For the broader Pipeworx/research surface, coverage is quite strong with memory, subscriptions, discovery, grounded lookup, research, comparison, and claim verification. The mix of two very different domains leaves notable gaps in each.