list_feedsList the curated technology feeds (id, title, category, source). Optionally filter by category (tech) or keyword. Pass an id to read_feed.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Keyword to match in feed title/source/description. | |
| category | No | Filter by category: tech. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "category": "tech"
+ },
+ {
+ "query": "artificial intelligence"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. Description adds minimal behavioral context (curated feeds, optional filters) without contradiction.
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, front-loaded with the main purpose, no unnecessary words. Every sentence adds value.
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?
Adequate for a simple list tool with good annotations and schema. No output schema, but the tool's return type is inferable from description; brief but sufficient.
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 descriptions. The description repeats parameter purposes and adds an example value ('tech'), providing slight additional context beyond the schema.
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 verb 'List', resource 'curated technology feeds', and includes output fields (id, title, category, source). It also distinguishes from sibling tool read_feed by mentioning passing an id to that tool.
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?
Provides optional filters and redirects to read_feed for a specific feed, implying when to use. Does not explicitly state when not to use or exclude alternatives, but context is clear.
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.
Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants, bet_research vs. polymarket_edges, and several entity/profile tools. An agent could easily misselect between similar tools despite detailed descriptions.
Tool names are inconsistent: some use snake_case (ask_pipeworx), some camelCase (ai_visibility_check), and verbs vary (ask, bet, compare, scan, validate). No clear naming convention is followed across the set.
With 34 tools, the server covers a broad range of functionalities for a platform like Pipeworx. While each tool seems justified, the count is on the higher end, potentially overwhelming for simpler use cases.
The tool set covers data retrieval, memory, subscriptions, Polymarket analysis, and utility functions comprehensively. Minor gaps exist (e.g., no direct user management) but core workflows are well-supported.