Generate relevant hashtags from a topic or text. Pure CPU, instant.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "<text>"
},
"name": "hashtag"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Topic or text | |
| limit | No | Max hashtags |
Changes observed during successful MCP inspections.
Input schema / properties / limitAdded value: +{
+ "description": "Max hashtags",
+ "type": "integer"
+}Input schema / properties / textAdded value: +{
+ "description": "Topic or text",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "text"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden. It does disclose two useful traits beyond structure — 'Pure CPU' (no network, no external API) and 'instant' (fast) — but it omits how many hashtags are returned by default, whether results are prefixed with '#', and whether output is deterministic.
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?
Two clipped sentences, zero waste, with the core action front-loaded and the performance note trailing. Nothing needs to be cut or reordered.
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 two-parameter, no-output-schema utility the description is nearly sufficient — the agent knows what goes in and that the call is cheap. The remaining gap is the shape of the result (count and '#' formatting), which matters for downstream use but is not strictly required to invoke the tool.
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 100%, so both params are already documented ('Topic or text' and 'Max hashtags'), making 3 the baseline. The phrase 'from a topic or text' corroborates the text parameter but adds no new detail, and the description says nothing about the limit parameter's default or acceptable range.
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 gives a specific verb+resource ('Generate relevant hashtags') and states the input source ('from a topic or text'), which is enough to separate it from nearby siblings such as generate_headlines, caption, or slugify. No other listed tool produces hashtags, so the differentiation is unambiguous.
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?
Usage is only implied: the agent can infer it should call this when it needs hashtags for a topic or block of text. There is no explicit when-to-use versus alternatives guidance, no exclusions, and no note about the required input form (topic phrase vs full text).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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