Payload Sample MCP Server
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PAYLOAD_HARD_CAP | No | Hard cap on the total number of calls to keep the sample from being used as a free service. | 200 |
| PAYLOAD_FREE_QUOTA | No | Number of free premium tool calls before the PAYMENT_REQUIRED response is returned. | 5 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| word_countB | Count words, characters, and lines in text. Free and unlimited in this sample. |
| summarizeB | Return a short extractive summary of text. PREMIUM: consumes 1 free-quota call. |
| extract_keywordsB | Return the most frequent significant words in text. PREMIUM: consumes 1 free-quota call. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
word_count, summarize, and extract_keywords each target a clearly different text operation, though summarize and extract_keywords overlap somewhat in that both perform content-level analysis of the same input.
All names use snake_case and are readable, but the pattern varies: word_count is noun-based while summarize and extract_keywords are verb-based, so the convention isn't fully uniform.
Three tools is on the thin side even for a sample server; the text-utility domain could reasonably support a few more operations, but the small surface is defensible given the explicit 'sample' framing.
The set covers basic text stats, summarization, and keyword extraction, but leaves obvious gaps like sentiment, readability, or other transformations, so coverage is partial rather than a full text-analysis lifecycle.