Fetch versioned changelog entries for a specific API (openai, anthropic, stripe, github, twilio, cloudflare, sendgrid, slack). Filter by date and breaking-only.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max entries to return (default 10, max 50) | |
| since | No | Return entries on or after this date (ISO 8601) | |
| api_id | Yes | API identifier | |
| breaking_only | No | Return only entries with breaking changes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full transparency burden. It only states what the tool does (fetch entries and filters) but does not disclose pagination behavior, default ordering, error handling, or what the response looks like. This is a significant gap for an un-annotated tool.
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 a single, information-dense sentence that starts with the action and resource. It is concise, covers the key features, and avoids any redundant 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?
With no output schema, the description should clarify return format and default behavior, but it only says 'fetch entries' and leaves response details and pagination unstated. Given the simple parameter set, it is adequate but not 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 schema already describes all four parameters, but the description adds value by listing valid api_id values (openai, anthropic, etc.) and summarizing the filter options. This goes beyond the schema's generic 'API identifier' and justifies a score above baseline.
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 action (fetch) and resource (versioned changelog entries for a specific API), and enumerates the supported API identifiers. It also mentions filtering by date and breaking-only, which distinguishes it from sibling tools like diff_versions and search.
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 usage when you need changelog entries for one of the listed APIs, but it does not explicitly compare with alternatives such as detect_breaking_changes or search. It lacks explicit exclusions or 'when not to use' 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.
Tools are mostly distinct: list_tracked_apis focuses on API metadata, fetch_changelog retrieves entries, diff_versions compares time ranges, detect_breaking_changes filters by severity, search provides full-text, and subscribe_feed handles notifications. Slight overlap exists between detect_breaking_changes and fetch_changelog (both can return entries filtered by date), but descriptions clarify the breaking-change focus.
All tool names follow a consistent verb_noun pattern (detect_, diff_, fetch_, list_, subscribe_) with snake_case, and 'search' is a simple verb that fits the pattern. No mixed conventions.
Six tools is well-scoped for a changelog tracker, covering discovery, analysis, and subscription functionality without unnecessary bloat.
The core lifecycle is covered: listing APIs, fetching changelogs, detecting breaking changes, diffing versions, searching, and subscribing. Minor gaps include no unsubscribe/resubscription management and no single-entry detail view, but these are not critical for the primary use case.