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": "artificial intelligence",
+ "url": "https://feeds.arstechnica.com/arstechnica/index"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond annotations: 'CF-robust: fetches directly and falls back to a proxy if the source blocks the gateway.' This discloses a specific behavioral trait (proxy fallback) that is not encoded in the annotations, and 'normalize' hints at output processing. No contradiction with 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?
The description is three sentences, each with purpose: the first states the core action, the second adds a key technical nuance, and the third gives cross-tool guidance. There is zero wasted wording. It is front-loaded and easy to scan.
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 no output schema, the description covers the core purpose, usage guidance, and a unique behavioral detail. It does not specify the return format or potential errors, but the combination of annotations and the description is sufficient for an agent to select and invoke the tool correctly. A small gap remains in describing output structure, keeping it from a 5.
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 input schema fully describes all three parameters (url, limit, query) with descriptions and examples, giving 100% coverage. The description does not add significantly to parameter semantics beyond the schema; it only restates that the tool fetches by URL. Per the rubric, a baseline of 3 is appropriate when schema coverage is high.
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 action: 'Fetch and normalize any RSS / Atom / RDF feed by URL.' It uses a specific verb ('fetch') and resource ('feed'), and distinguishes itself from list_feeds by explicitly saying 'Use list_feeds first for curated sources.' This makes the tool's purpose unambiguous and differentiated.
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 provides explicit usage guidance: 'Use list_feeds first for curated sources' implies that fetch_feed is for arbitrary or non-curated feeds. It also adds a behavioral note about CF-robustness, which helps the agent decide when to use this tool. This satisfies the criterion of explicit alternatives and when-to-use.
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.
Most tools have distinct purposes, but some overlap exists between query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and company research tools (entity_profile, compare_entities, recent_changes). Descriptions clarify differences, but an agent might occasionally select the wrong one.
The naming is mostly snake_case with descriptive verb_noun patterns (list_feeds, read_feed, remember, recall). However, some tools break the pattern (ai_visibility_check, ask_pipeworx, discover_tools) and there is inconsistency in verb prefixes (ask_ vs. query vs. nothing). Overall still readable.
With 33 tools covering AI visibility, data queries, betting, feeds, memory, subscriptions, and more, the server feels overloaded and lacks a focused scope. Many tools belong to distinct domains, suggesting this should be split into multiple specialized servers.
Within each subdomain (Pipeworx data, Polymarket, feeds, memory), the tool surface is quite complete, offering CRUD-like coverage and advanced features. Only minor gaps exist (e.g., no tool for updating a subscription, no direct PolyMarket trade execution).