A
licenseNot graded
qualityD
maintenanceEnables AI models to administer personality tests, score responses, and provide personality type assessments, with optional integration with Ollama for personalized AI interactions.
1
ISC
agenttune
Server Details
Personality tuning files for AI agents: 43 MIT-licensed tunings + 5 inline personality tests.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- bernardjhuang/agenttune
- GitHub Stars
- 0
Available Tools
3 toolsget_test_specGet a personality test spec (administer inline)ARead-onlyInspect
Fetch a complete, self-contained test specification as Markdown: full item list, response scale, scoring algorithm, and the mapping from result to tuning slug. Administer the items to the user inline (bulk-paste is fine), score per the algorithm, then call get_tuning. Tests: mbti (OEJTS, 32 items, ~5 min), enneagram (OEPS, 36, ~5 min), disc (ODAT, 16, ~3 min), attachment (ECR-R, 36, ~5 min), big-five (IPIP-50, 50, ~7 min → maps to ocean files).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| test | Yes | Which test instrument. |
TDQS
A4.8/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description details what the specification contains (items, scales, algorithm, mapping) and the intended usage flow. Annotations already declare readOnlyHint=true, which is consistent. Adds context about return format and next steps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences pack the purpose, content details, and usage guidance efficiently. Front-loaded with core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with no output schema, the description fully explains what it returns, how to use it, and what to do next. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with a single 'test' enum. Description enriches by listing each test with item count and duration, providing practical context beyond the enum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch a complete, self-contained test specification as Markdown' and specifies the content. It distinguishes from sibling tools by describing the follow-up action (call get_tuning).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to administer items inline, score per algorithm, then call get_tuning. Lists tests with details (item counts, duration) to help choose and plan.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_tuningGet a tuning file (paste-ready Markdown)ARead-onlyInspect
Fetch one tuning file as Markdown with YAML front-matter. The front-matter is machine-readable install metadata (install.surfaces = where to write it per agent surface, verify.probe = how to confirm it took effect); the body is the behavioral tuning to load as system-prompt content. MIT licensed.
ParametersJSON 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. |
TDQS
A3.9/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the return format (Markdown with YAML front-matter) and outlines the front-matter structure (install.surfaces, verify.probe, MIT license), adding context beyond the readOnlyHint annotation.
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 main description is two concise sentences that front-load the key purpose and output format. Extra detail in the slug description is appropriate within the schema.
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?
Given no output schema, the description adequately explains the return structure and content, covering the front-matter and body. Lacks discussion of error cases but sufficient for a read-only fetch.
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 coverage is 100%, so the description adds minimal extra meaning. The slug field example and cross-reference provide slight guidance, but no significant semantic additions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The title and description clearly state the tool fetches one tuning file as Markdown with YAML front-matter, distinguishing it from sibling tools like list_tunings and get_test_spec.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the slug field's cross-reference to list_tunings, but does not explicitly state when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tuningsList all personality tuningsARead-onlyInspect
Catalog of all 43 AgentTune personality tuning files (slug, code, name, one-line blurb), optionally filtered by system. Use it to resolve a user's personality type to the right slug before calling get_tuning.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| system | No | Optional filter: one of the five personality systems. |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint: true, so the description adds value by specifying the exact number of files (43) and the fields included in the output, enhancing transparency beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. The first sentence describes the output, the second gives usage context. Highly efficient and front-loaded.
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?
Given no output schema, the description adequately describes the output structure. It also explains the tool's role in the workflow with get_tuning. Missing details like pagination are not critical for a small fixed dataset.
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 coverage is 100% for the single parameter (system), so the description adds no new information beyond stating it is optional. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all 43 AgentTune personality tuning files with specific fields (slug, code, name, one-line blurb), and it distinguishes itself from the sibling get_tuning by indicating it should be used before get_tuning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to use this tool to resolve a user's personality type to the right slug before calling get_tuning, providing clear context. It does not mention when not to use it relative to get_test_spec, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
get_test_spec - First observed
get_tuning - First observed
list_tunings
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TDQS
A4.4/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: retrieving a test specification, listing available tunings, and fetching a specific tuning file. No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent 'verb_noun' pattern using snake_case (get_test_spec, get_tuning, list_tunings), making them predictable and easy to understand.
Tool Count5/5
With 3 tools, the server is tightly scoped to its personality tuning domain. Each tool serves a necessary function without redundancy or excess.
Completeness5/5
The tools cover the complete workflow: retrieve a test spec, list tunings to find the matching slug, and fetch the tuning file. The description includes clear instructions for scoring and installation, leaving no gaps.