Get metadata for a specific episode including title, publish date, duration, α-sentiment score (crypto) (0-10 scale), traditional markets sentiment (0-10), and podcast info.
| Name | Required | Description | Default |
|---|---|---|---|
| episode_id | Yes | Episode ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the returned fields and implies a read-only metadata operation, but does not mention error conditions, authentication, rate limits, or whether the episode must exist. For a simple getter, this is acceptable but not deeply transparent.
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?
One sentence, front-loaded with the action, and includes all necessary detail without fluff. Every word contributes to understanding the tool's output.
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 single-parameter metadata getter with no output schema, the description covers the main return fields and the podcast info. It does not describe the full return structure or error behavior, but given the tool's simplicity, it is fairly 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?
Schema coverage is 100% (episode_id) and the schema description is sufficient. The tool description does not add extra meaning to the parameter beyond indicating it identifies a specific episode, which the schema already conveys. 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?
The description clearly identifies the tool as retrieving metadata for a specific episode, listing the exact fields (title, publish date, duration, sentiment scores, podcast info). This distinguishes it from sibling tools like get_episode_summary or get_episode_full, which focus on different aspects.
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 implies the tool is for fetching comprehensive metadata for a single episode, but it does not explicitly state when to choose this over alternatives like get_episode_summary or get_episode_full. Sibling names suggest clear distinctions, but the description itself lacks explicit when/when-not guidance.
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.
Every tool targets a distinct resource and data aspect: episode details, quotes, transcript, summary, market snapshots, ticker history, user feeds, etc. Even the 'full' composite variants are clearly described as one-call conveniences that bundle granular data, so there is no real ambiguity about which tool to use.
All 23 tools follow a consistent 'get_<entity>_<detail>' pattern using lowercase snake_case. This uniformity makes the tool names predictable and mentally indexed, with no mixing of verb styles or naming conventions.
With 23 tools, the server sits in the 'heavy' range (16–25) and feels a bit bloated. The breadth of resources justifies many endpoints, but several composite 'full' versions and overlapping history functions inflate the count and could be consolidated without losing capability.
The read-only surface covers core workflows well: episode-level detail, podcast discovery, market-wide snapshots/history/themes, ticker-specific data/leaderboards, and user personalization. Minor gaps include no way to enumerate all supported tickers or podcasts beyond search, and no direct episode list by date without going through the market endpoint.