Hashflags
get_v1_1_HashflagsHashflags Group: Misc. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_v1_1_HashflagsHashflags Group: Misc. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
There are no annotations, so the description should carry the full burden of explaining behavior. It only mentions billing cost and a group label; it does not disclose whether this is a read operation, what the response contains, or any other behavioral traits.
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 very short but under-specified. The sparse text does not earn its place because it omits the core purpose; this is not effective conciseness, it is a lack of specification.
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 annotations, no output schema, and no input schema, the description is the only source of functional context. It fails to explain what hashflags are, what the tool returns, how filter or search works, or any other operational detail, making the tool effectively unusable.
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 input schema has zero parameters and 100% schema coverage vacuously, so there are no param semantics to document. The baseline for 0 params is 4; the description's absence of parameter details is acceptable.
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 only says 'Hashflags Group: Misc. Billing per call: 1 Credits,' which merely restates the tool name and provides cost/grouping information. It never states what the tool does or what data it returns, so there is no clear verb, resource, or action.
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 is provided for when to use this tool versus any alternative. The description contains no context about use cases, prerequisites, or conditions, leaving an agent completely without directional information.
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.
Several tools appear to do the same thing, such as get_v1_1_Followers vs get_v2_Followers and get_v2_Tweet vs get_v2_TweetDetail. The descriptions are too brief to clarify differences, and multiple user lookup tools (get_v1_1_Users, get_v2_UserByRestId, etc.) create confusion.
Naming is inconsistent, mixing camelCase (get_ShortUrl), snake_case (get_email_search_by_username), and version prefixes with varying formats (get_v1_1 vs get_v2). No uniform verb_noun pattern is followed.
With 32 tools, the server is overloaded, especially given many redundant variations across API versions. The count exceeds the 25-tool threshold for too many tools, and many could be consolidated.
The server is entirely read-only (all tools are GET), missing write operations like posting tweets, following users, or sending direct messages. This is a significant gap for a Twitter server, and even read coverage has redundancies rather than comprehensive distinct endpoints.