agenttune

Get a tuning file (paste-ready Markdown)

get_tuning

Read-only

Fetch one tuning file as Markdown with YAML front-matter. The front-matter links to the shared platform registry and installation protocol. structuredContent.body is the preference text; metadata stays separate. Confirmation of saved text is distinct from evaluation of behavior. MIT licensed.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Type slug, lowercase. mbti: 4-letter code (intj). enneagram: N-name (5-investigator). disc: letter-name (d-dominance). attachment: style (secure). ocean: dimension-pole (openness-high). Unsure? Call list_tunings.
`system`	Yes	Personality system.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`body`	Yes
`slug`	Yes
`system`	Yes
`body_url`	Yes
`revision`	Yes
`canonical_url`	Yes
`evidence_status`	Yes
`install_protocol`	Yes
`metadata_markdown`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 26, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "body": {
+      "type": "string"
+    },
+    "body_url": {
+      "type": "string"
+    },
+    "canonical_url": {
+      "type": "string"
+    },
+    "evidence_status": {
+      "type": "string"
+    },
+    "install_protocol": {
+      "type": "string"
+    },
+    "metadata_markdown": {
+      "type": "string"
+    },
+    "revision": {
+      "type": "string"
+    },
+    "slug": {
+      "type": "string"
+    },
+    "system": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "system",
+    "slug",
+    "canonical_url",
+    "body_url",
+    "revision",
+    "evidence_status",
+    "body",
+    "metadata_markdown",
+    "install_protocol"
+  ],
+  "type": "object"
+}

First observedJun 10, 2026

TDQS

B3.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description does not contradict that. It adds context about the output structure (structuredContent.body vs metadata) and a semantic nuance (confirmation vs evaluation), but does not disclose potential side effects, authentication, or rate limits. Since annotations cover the read-only nature, the added context is modest.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences and front-loads the core purpose. It includes a note about licensing that is not essential for invocation, but the overall length is appropriate and there is minimal fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only fetch with an output schema, the description explains the output format and separates metadata from content. It does not cover error handling or pagination, but these are not critical for this tool's usage, and the schema and annotations cover the rest.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (system and slug) are already fully documented. The description does not add any additional meaning or syntax details beyond what the schema provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Fetch') and resource ('tuning file') with an explicit format (Markdown with YAML front-matter). It distinguishes the tool's scope from generic operations, though it does not explicitly name sibling tools like list_tunings for contrast.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as list_tunings. The schema's slug description suggests calling list_tunings when unsure, but the description itself offers no usage context or exclusions.

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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