Unhide story
betterpost_unhide_storyUnhides a previously hidden story so generation can use it again.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| storyId | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| hidden | Yes | ||
| storyId | Yes |
betterpost_unhide_storyUnhides a previously hidden story so generation can use it again.
| Name | Required | Description | Default |
|---|---|---|---|
| storyId | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| hidden | Yes | ||
| storyId | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / tokenRemoved value: -{
- "description": "Your saved BetterPost token from a prior call (demo `bp_demo_…` or paid `bp_live_…`). Reuse the same token on every call and across conversations; omit only on the very first call to be issued a free demo token. Paid users may instead pass the key in the server URL (?key=…) or an Authorization: Bearer header.",
- "type": "string"
-}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide idempotentHint=true and destructiveHint=false; the description adds context that the story is used by generation, which is valuable. No 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?
Single sentence, 11 words, front-loaded verb, no wasted 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?
Sufficient for a simple operation with one parameter and output schema present. Lacks error handling details but covers essential purpose and effect.
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?
With 0% schema description coverage, the description adds value by linking the storyId parameter to 'previously hidden story', clarifying its required context.
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 (unhides), the resource (story), and the effect (generation can use it again). It distinguishes from sibling 'betterpost_hide_story' by specifying reversal.
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 when to use (to make a hidden story available for generation) but does not explicitly state when not to use or provide alternatives.
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 has a clearly distinct purpose—project management, source and story handling, content generation, settings, and usage—with no overlapping functionality. The descriptions clearly delineate when to use each tool.
All tools follow the consistent pattern 'betterpost_<verb>_<noun>', making the set predictable and easy to navigate. The naming convention is uniform throughout.
With 23 tools, the server covers a comprehensive content generation workflow without being overwhelming. Each tool serves a necessary function, and the count feels well-scoped for the domain.
The tool surface covers the full lifecycle of project management, source acquisition, story curation, content generation/derivation, and settings configuration. There are no obvious gaps that would cause an agent to fail in typical use cases.