AutoVault

Server Quality Checklist

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Latest release: v0.4.0
Disambiguation4/5
Tools are mostly distinct, but add_skill and propose_skill both add skills, differing only in source type; agents might struggle to choose between them without careful reading. Other tools have clear, separate responsibilities.
Naming Consistency5/5
All tools follow a consistent verb_noun snake_case pattern (e.g., add_skill, delete_skill, check_updates). No mixing of conventions or irregular names.
Tool Count5/5
With 7 tools, the server covers the core skill lifecycle (add, propose, get, update, delete, bulk import, check updates) without unnecessary bloat or missing essentials.
Completeness4/5
The tool surface covers CRUD plus batch import and update checking. A dedicated 'list all skills' tool is missing, but get_skill with a query can approximate it. Minor gap prevents a perfect score.
Average 3.7/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 8 of 10 community issues answered or closed in the last 6 months
- 16 commits in the last 12 weeks
- Last stable release on May 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses validation, dedup, agents filling, resource inference, and profile sync. However, it does not mention side effects (e.g., whether source files are modified), authentication needs, or error handling, leaving gaps.
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 a single paragraph with no wasted words. It front-loads the core action and adds details sequentially. Could be slightly improved with bullet points for clarity, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity (7 params, no output schema, no annotations), the description provides adequate but not complete context. It explains main behavior and a few parameters, but leaves many details about error handling, prerequisites, and return behavior implicit.
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?
With 0% schema description coverage, the description must compensate. It explains source_dir, agents, and verbose, adding meaningful context. However, four parameters (allow_synthesized_frontmatter, sync_profiles, profile_roots, discover_profile_roots) remain unexplained, so coverage is only partial.
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 clearly states the action (import) and resource (immediate child directories containing SKILL.md). It also distinguishes behavior from sibling propose_skill by referencing it, but does not explicitly differentiate from other siblings like add_skill or update_skill.
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?
The description does not provide explicit guidance on when to use bulk_import versus alternative tools. It mentions 'like propose_skill' but lacks any when-to-use or when-not-to-use instructions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that remote bytes are fetched and validated, and that profile syncing occurs by default. However, it omits details on authorization requirements, error handling, or irreversible actions.
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?
Description is a single paragraph that front-loads purpose and then provides conditional details. It is concise with no fluff, though it could be split for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers core usage and distinguishes from propose_skill, but lacks documentation for 4 out of 8 parameters and does not describe return values or error conditions. Given the complexity and missing output schema, more completeness is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains source, identifier, skill_dir, and sync_profiles, but overlooks version, profile_roots, discover_profile_roots, and verbose, leaving those parameters unexplained.
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 'Add a known skill from a source' and differentiates from sibling tool propose_skill by specifying that caller-authored SKILL.md bytes should use propose_skill, not add_skill.
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?
Provides clear guidance on when to use this tool for different sources (GitHub, agentskills, URL, local) and mentions that propose_skill should be used for custom SKILL.md. However, it does not explicitly exclude other siblings like update_skill or delete_skill.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses that the tool runs validation, security scan, capability cross-check, and three-tier deduplication. This reveals significant behavioral traits beyond a simple save, such as potential rejection or merging. It does not, however, specify error behavior or what happens on duplicate detection.
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?
The description is concise at four sentences, with the primary action and key checks in the first sentence. Every sentence adds value, and the structure is front-loaded for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no output schema), the description lacks parameter explanations and output details. While core usage is clear, an agent cannot reliably construct calls without parameter semantics or understand return values, which is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description explains none of the 6 parameters beyond 'SKILL.md' for skill_md. Terms like resources, source_session, and allow_synthesized_frontmatter remain undefined, leaving the agent without guidance on how to populate them.
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 submits a SKILL.md to AutoVault and lists the specific checks performed (validation, security scan, etc.). It distinguishes itself from alternatives by explicitly recommending it over writing skill files directly to disk, implying it is for new skills, contrasting with siblings like update_skill.
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 when saving a conversationally created skill or after drafting one for reuse. It also advises preferring this over direct file writes. However, it does not explicitly exclude use cases for updating existing skills, where update_skill might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description covers main behaviors: hash comparison, handling of sources, and reporting of unchecked inline skills. Does not explicitly state it is read-only but implies it.
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 focused sentences, front-loaded with purpose, every sentence adds value, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers drift detection mechanism and special case for inline skills, but lacks output format details (no output schema), leaving the return structure unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The optional 'skill' parameter is not explained in the description, leaving ambiguity about whether it filters or specifies which skills to check, despite 0% schema coverage.
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 detects upstream drift for skills by comparing content hashes, distinguishing it from sibling tools like update_skill or delete_skill.
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?
Implies use before updating but lacks explicit when-to-use or when-not-to-use guidance relative to siblings like update_skill or propose_skill.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It discloses that query returns best match and alternates, include_resources controls inlining, and agent applies AutoVault transforms. However, it does not mention behavior if both name and query are provided, or error handling.
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?
Three sentences cover the main functionality efficiently. The structure mixes parameter explanations within usage, but no word wasted. Could be more organized by grouping parameter descriptions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 0% schema description, no output schema, and no annotations, the description covers the two modes and most parameters. However, it misses the top_k parameter, does not describe the output format, and lacks error behavior. Adequate but not complete.
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 has 0% description coverage, so description must compensate. It explains name, query, include_resources, and agent clearly, but omits top_k completely despite its exclusiveMinimum and maximum constraints. 4 out of 5 parameters are described, but the missing one is a gap.
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?
Description clearly states the verb (Find/load) and resource (installed skills). It distinguishes between exact match by name and search by query, and mentions optional parameters. Among siblings focused on mutation, it stands out as the retrieval tool.
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 when to use name vs query, but lacks explicit guidance on when to use this tool over siblings like propose_skill or delete_skill. No prerequisites or when-not conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses that deletion triggers profile refresh and removes vault-local transforms, which is valuable behavioral context beyond the basic delete action.
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?
The description is two sentences long, no fluff. Every sentence adds essential information: the action and the side effects.
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 delete tool with one parameter and no output schema, the description covers the action and side effects adequately. It lacks error handling or return information, but that is minor for a deletion tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'name' is not elaborated upon. The schema has 0% description coverage, and the description adds no extra meaning (e.g., format, case sensitivity, scope) beyond the parameter name.
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 it deletes an installed skill and explicitly lists side effects (profile refresh, transform removal). This differentiates it from siblings like add_skill or update_skill.
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?
Usage is implied by the verb 'delete', but no explicit when-to-use or when-not-to-use guidance is given. Alternatives like update_skill exist but are not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses key behaviors: refreshing source with only name, rejecting mismatched frontmatter, default compact sync counts, and the verbose option. It does not cover side effects or auth requirements, but for an update operation the transparency is relatively high.
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 a single paragraph covering numerous update modes and flags without excessive verbosity. It front-loads the main purpose. Each clause adds value, but the density could be slightly improved by splitting into bullet points for easier scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 12 parameters, nested objects, no output schema, the description is fairly complete for the main use cases. However, details on resources, sync profiles, profile_roots, and discover_profile_roots are missing, which may leave agents uncertain about those parameters.
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 0%, so the description adds meaning to many parameters (name, source, identifier, skill_dir, skill_md, reuse_existing_resources, verbose). However, parameters like resources, sync_profiles, profile_roots, discover_profile_roots, and version are not explained. The coverage is about 7/12 (58%), providing moderate compensation but with gaps.
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 'Update an installed skill' and distinguishes various update modes (refresh, new source, local, inline). It is specific about the verb and resource, and the sibling tools (add_skill, delete_skill) provide contrast, making the purpose unambiguous.
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 provides explicit guidance on when to use different parameter combinations (e.g., just name for refresh, source+identifier for new source, etc.) and flags like verbose and reuse_existing_resources. However, it does not explicitly contrast when to use this tool over siblings like add_skill, but the update context is implied.
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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