betterpost_get_contentFetch one piece of content in full.
| Name | Required | Description | Default |
|---|---|---|---|
| contentId | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| type | Yes | ||
| content | Yes | The generated content. Fields present depend on `type`. | |
| summary | Yes | ||
| topicId | Yes | ||
| createdAt | Yes | ISO 8601 datetime string | |
| projectId | Yes | ||
| publicUrl | Yes | ||
| wordLimit | Yes | ||
| storyCount | Yes | ||
| derivedFromContentId | 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 provide readOnlyHint=true and destructiveHint=false, so the read-only nature is clear. The description adds 'in full' but no further behavioral details (e.g., response size, errors). Annotations carry most of the burden; description is adequate but minimal.
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?
Extremely concise: one sentence with no filler. Information is front-loaded and every word earns its place. Ideal for quick scanning.
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 fetch-by-ID tool with an output schema, the description covers the essential purpose. Could mention error handling or behavior if content not found, but not critical for basic understanding.
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 0%, so the description must compensate for the undocumented parameter. The description does not explain contentId beyond its name and type in the schema. It adds no meaning like format expectation or relationship to other tools.
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 'Fetch one piece of content in full' clearly states the verb (fetch), resource (content), and scope (one, in full). It distinguishes from siblings like list_content (multiple) and delete_content (deletion).
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?
No guidance on when to use this tool versus alternatives (e.g., list_content for multiple). No prerequisites or context for when fetching is appropriate. The description is purely declarative.
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.