Repurpose long-form text into twitter_thread, linkedin_post, or newsletter.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| format | No | twitter_thread |
| Name | Required | Description | Default |
|---|---|---|---|
| cost | Yes | ||
| format | Yes | twitter_thread, linkedin_post, or newsletter | |
| content | Yes | ||
| word_count | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only restates the action. It does not explain whether the original text is preserved, any input length limits, how the output format affects content, or any side effects.
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 entire description is a single, front-loaded sentence with no filler. It conveys the verb, resource, and target output formats efficiently.
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?
The tool has only two simple parameters and an output schema, so the description covers the core purpose and parameter semantics sufficiently. It loses a point because it lacks any guidance on when to use the tool or what behavioral caveats exist.
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 description coverage is 0%, but the description compensates by clarifying that 'text' should be long-form and by explicitly listing the three valid format values. This adds meaningful context beyond the bare schema definitions.
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 uses a specific verb 'Repurpose' and identifies the resource as 'long-form text', then enumerates three concrete output formats (twitter_thread, linkedin_post, newsletter). This clearly distinguishes it from sibling tools like summarize_url and find_competitors.
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 for converting long-form content into a listed format, but it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites.
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.
Each tool performs a clearly distinct function: news discovery, competitor research, content repurposing, and URL summarization. There is no overlap in their purposes.
Three tools follow a verb_noun pattern (find_competitors, repurpose_content, summarize_url), but daily_digest deviates as a noun phrase. This minor inconsistency is still readable and predictable.
Four tools is well-scoped for a focused automation server covering research and content workflows. Each tool earns its place without redundancy.
The set covers common research and content repurposing needs, but could benefit from a general web search or content generation tool. The current surface is workable with minor gaps.