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": "crypto",
+ "url": "https://feeds.bloomberg.com/markets/news.rss"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds valuable behavioral context: 'CF-robust: fetches directly and falls back to a proxy if the source blocks the gateway' and the normalization aspect, which go beyond annotation data.
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 two sentences and front-loaded with the core purpose. Every sentence adds value: the first states functionality, the second provides robustness and guidance. No redundant or excessive wording.
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?
The description covers purpose, feed types, fallback behavior, and mentions a sibling tool. However, it does not explicitly differentiate from read_feed or describe the return format, which could be useful given there is no 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%, so all parameters (url, limit, query) are already described in the schema. The description does not add additional parameter-specific meaning beyond the schema, so it meets the baseline for high coverage.
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 uses specific verb and resource: 'Fetch and normalize any RSS / Atom / RDF feed by URL.' It clearly identifies the tool's function and distinguishes it from siblings like list_feeds (curated sources) and read_feed (likely reading stored feeds).
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 states 'Use list_feeds first for curated sources,' providing explicit context for when to use an alternative tool. It implies this tool is for arbitrary URLs and live fetching, but it does not explicitly contrast with read_feed or mention when to avoid this tool.
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 overlap among query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and several Polymarket-specific tools. Descriptions help differentiate, but some confusion is possible.
All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, compare_entities, validate_claim). No mixing of conventions, and naming is predictable.
33 tools is a high count for a finance server, including many meta-tools (memory, subscriptions, feedback) that are not finance-specific. The core finance set is reasonable, but the overall surface feels heavy.
The server covers many data sources (SEC, FRED, FDA, etc.) but lacks direct stock quotes or fundamental CRUD operations. Some areas (e.g., Polymarket, AI visibility) are over-represented, leaving gaps in core finance tasks.