gutfeel-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| likelyA | Judge whether a claim is true of a text. Answers yes or no with the model's probability that the claim is true -- or unsure, when ask_human is set. |
| classifyA | Pick the option that fits a text best: a team, a topic, a language, an intent. Answers with the chosen label, every option's probability and a confidence. |
| rateA | Rate a text on an ordered scale such as urgency or severity. Answers with a score, which can fall between levels, the nearest level and a confidence. |
| eachA | Ask the same question about many texts at once: likely with just a question, classify with options, rate with levels. Far faster than one call per text. |
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 4 tools
likely, classify, and rate have clearly distinct output types (binary truth, categorical label, ordinal score). 'each' overlaps as a batch version of all three, which could cause some confusion if the description is not read, but its purpose is clarified as handling many texts at once.
Names are all lowercase single words but mix parts of speech: classify and rate are verbs, while likely is an adjective and each is a determiner. There is no consistent verb_noun or action-oriented pattern, making the set less predictable.
Four tools is a lean but appropriate set for a focused text-judgment service. Each tool covers a distinct primitive or batch variant, with no obvious redundancy.
The surface covers binary judgment, multi-class classification, ordinal rating, and batch versions of all three, forming a complete core for quick text judgments. Minor gaps exist (e.g., no explicit pairwise comparison or human escalation for classify/rate), but agents can work around them.