api_rssParse any RSS or Atom feed into clean JSON items. ?url=&limit=10 [HTTP x402 price: $0.002]
| Name | Required | Description | Default |
|---|---|---|---|
| params | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / params / additionalPropertiesRemoved value: -trueInput schema / properties / params / propertiesAdded value: +{
+ "limit": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ },
+ "url": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It clearly states the parse-to-JSON behavior and even discloses the price, but it does not mention error behavior, invalid-feed handling, limits, defaults, or whether a URL is effectively required despite the schema marking it optional.
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 compact and front-loaded: a clear action sentence, a short usage example, and the price. The cryptic 'HTTP x402' segment is minor noise, but the overall structure 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?
The description plus output schema cover the basic invocation and expected result, and the price is useful for a paid tool. Still, missing information about required parameters, failure modes, and constraint handling makes it adequate rather than fully complete.
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 query-string example adds meaning to both parameters ('url' as the feed URL, 'limit' as the item count), which is essential at 0% schema coverage. However, it leaves ambiguity around limit's format/range, parameter optionality, and the nested 'params' wrapper that the input schema defines.
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 states a specific action ('Parse'), a resource type ('any RSS or Atom feed'), and an output ('clean JSON items'). This makes the tool easy to distinguish from siblings like api_scrape or api_readability without needing to inspect their schemas.
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 use case is implied: use this when you have an RSS or Atom feed URL and want JSON. However, there is no explicit guidance about when not to use it or how it compares to feed-adjacent sibling tools, so routing relies on the agent's inference.
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 tools have unclear boundaries: api_search and api_serp_google both return Google results, api_scrape and api_render_text both extract page text, and api_hash_multi overlaps with api_sha256 for SHA-256/SHA-512. While many tools are distinct, these overlapping pairs create real misselection risk.
Every tool follows the same api_<snake_case> pattern with no mixed conventions or casing styles. The prefix makes the server immediately recognizable and the action/resource is consistently readable across all 44 tools.
44 tools is well over the 25+ threshold for a well-scoped set, making the server feel like a grab-bag of unrelated utilities. Even though each tool is small and individually useful, the overall surface is too large and would benefit from consolidation into focused sub-servers.
The set covers many common utility categories—encodings, conversions, text analysis, web scraping, SEO, and trends—but has notable one-way gaps: CSV/YAML/TOML all convert to JSON but not back, and markdown converts to HTML but not the reverse. The broad domain makes full completeness hard to define, so only major reverse-conversion gaps stand out.