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": "python",
+ "url": "https://www.reddit.com/r/programming/.rss"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds behavioral info about fetching directly and falling back to a proxy if blocked, which is not in annotations. However, it doesn't describe return format or error handling.
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 concise sentences (27 words) front-load the core purpose and add essential context without waste. Every sentence earns its place.
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 simplicity and complete annotations/schema, the description covers purpose, usage logic, and reliability. However, it omits return value structure (e.g., normalized feed items), which would help the agent understand output without an output schema.
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 clear property descriptions. The description does not add extra meaning beyond what the schema already provides for url, limit, and query. Baseline 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?
Description clearly states "Fetch and normalize any RSS / Atom / RDF feed by URL," specifying the verb and resource. It also distinguishes from sibling list_feeds by recommending its use for 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 "Use list_feeds first for curated sources," providing clear when-to-use guidance and an alternative. Also mentions CF-robustness and proxy fallback for reliability context.
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 there is some overlap between ask_pipeworx, ask_pipeworx_grounded, deep_research, and similar data query tools. However, detailed descriptions help agents differentiate.
Names consistently use snake_case and a mix of verb_noun and noun_verb patterns. No camelCase is present, but some tools like 'generate_llms_txt' have embedded acronyms, which slightly reduces consistency.
33 tools is high, but the server covers a wide range of functionalities (data lookups, prediction markets, RSS feeds, memory). Some tools could be combined, but the count is within reasonable limits for a comprehensive tool server.
The tool set covers major areas like entity lookups, prediction market analysis, data retrieval, and memory management. Minor gaps exist (e.g., no RSS feed deletion tool), but overall it is quite comprehensive.