list_feedsList the curated gaming feeds (id, title, category, source). Optionally filter by category (gaming) 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: gaming. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description's mention of listing curated feeds adds context without contradicting 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?
Two sentences: the first states the core purpose and output fields, the second explains optional use and ties to sibling. No superfluous words.
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 simple list tool with 2 optional parameters and no output schema, the description covers output fields and directs to read_feed. Omits pagination or ordering, but annotations already assure safety and idempotence.
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% for both parameters. The description repeats the filter options (category and keyword) without adding significant semantic detail 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?
The description clearly states the tool lists curated gaming feeds with specific fields (id, title, category, source) and distinguishes from sibling read_feed by directing to use that for a specific feed.
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?
It explains optional filters by category or keyword and points to read_feed for individual feeds, but does not explicitly state when to avoid this tool or compare with other siblings like search_within.
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 notable overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research—all of which route questions to the same underlying catalog. The polymarket_* family also has several opportunity-scanning tools (edges, arbitrage, bet_research) that agents could confuse without reading the long descriptions carefully.
All 34 tool names use lowercase snake_case with a clear verb-first or noun-descriptive pattern (list_feeds, read_feed, remember, resolve_entity, polymarket_arbitrage). Even compound names like ai_visibility_check and ask_pipeworx_grounded follow a predictable, consistent style. No mixed conventions or camelCase deviations.
34 tools is far more than the apparent 'Gaming Feeds' scope suggests—only list_feeds, read_feed, and fetch_feed actually relate to gaming feeds. The rest form a sprawling data-research and prediction-market suite, creating a severe mismatch between the server name and its actual tool surface.
Viewed as a general Pipeworx data-access platform, the tool set is quite complete: question routing, grounded answers, entity resolution, profiles, comparisons, claim validation, memory, subscriptions, alerts, feed reading, and tool discovery are all covered. The only notable gaps are feed management (no create/update/delete for custom feeds) and a few odd add-ons like generate_llms_txt that feel outside the core domain.