Enables security scanning and verification of AI agent skills hosted on GitHub repositories.
Enables security scanning and verification of AI agent skills hosted on GitLab repositories.
Supports verification of tamper-proof, on-chain security certifications for AI agent skills anchored on the Solana blockchain.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@skvil-mcp Check if sha256:4a2f...c81e is safe to install"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for the Skvil security scanner
Verify, scan, and check on-chain certifications for AI agent skills — directly from your AI assistant.
AI agents install skills from the internet — but how do you know a skill is safe?
Skvil is a community-powered security scanner that analyzes AI agent skills for malicious patterns, builds reputation scores through crowdsourced scans, and issues on-chain certifications that are tamper-proof and publicly verifiable.
This MCP server gives your AI agent native tools to interact with the Skvil network. No HTTP knowledge required — just ask your agent to verify a skill.
Skvil's certification pipeline is what sets it apart — the entire process is fully automated with zero human intervention:
Community scanning — multiple independent agents scan the same skill
Reputation building — scores aggregate via exponential moving average (EMA)
Crucible analysis — automated static analysis scans 32+ pattern categories, then an AI triage phase (embeddings + LLM) validates findings and filters false positives
On-chain registration — skills scoring ≥ 80 are automatically anchored on Solana via SPL Memo transactions, creating a tamper-proof trust anchor that no single party can forge or revoke silently
Certification is algorithmic: score ≥ 50 passes, score < 50 fails and revokes any existing certificate. A periodic re-certification scheduler re-analyzes certified skills and revokes those that no longer pass.
When you run skvil_verify, you're not just checking a database — you're verifying against an immutable on-chain record.
Related MCP server: vigile-mcp
Tip: Works with both
npx(Node) andbunx(Bun). We recommend Bun for faster installs and startup.
Add to your claude_desktop_config.json:
{
"mcpServers": {
"skvil": {
"command": "bunx",
"args": ["@skvil/mcp-server"]
}
}
}{
"mcpServers": {
"skvil": {
"command": "npx",
"args": ["-y", "@skvil/mcp-server"]
}
}
}Add to your project's .mcp.json:
{
"mcpServers": {
"skvil": {
"command": "bunx",
"args": ["@skvil/mcp-server"]
}
}
}{
"mcpServers": {
"skvil": {
"command": "npx",
"args": ["-y", "@skvil/mcp-server"]
}
}
}Add to your settings (JSON):
{
"mcp.servers": {
"skvil": {
"command": "bunx",
"args": ["@skvil/mcp-server"]
}
}
}{
"mcp.servers": {
"skvil": {
"command": "npx",
"args": ["-y", "@skvil/mcp-server"]
}
}
}That's it. The server auto-registers a free API key on first use. Zero config.
Tool | Auth | Description |
| No | Check if a skill is safe by its SHA-256 hash. Returns reputation score, risk level, on-chain certification status, and Crucible behavioral analysis. |
| No | Community statistics: total skills scanned, trusted, critical, and on-chain certified counts. |
| No | List skills with active on-chain certifications (V1/V2/V3/Gold). Up to 10 most recent. |
| No | Browse the full catalog of certified skills with metadata, install URLs, and provider info. Up to 100 skills. |
| No | Get a free API key (500 scans/day). Auto-cached locally for future use. |
| Key | Submit security scan results to the community reputation network. Requires full skill identification (see below). |
| Key | Report a suspicious skill. Confirmed reports trigger automatic on-chain revocation. |
Every scan submission requires full identification so the Crucible behavioral analysis pipeline can fire:
Field | Type | Description |
| string | Skill name (max 256 chars) |
| string |
|
| number | Number of files in the skill |
| object | Map of |
| number | Computed security score 0-100 (server recomputes from findings) |
| string |
|
| string | Required. Source URL — must be |
| string | Required. |
| string | Required. Agent platform submitting the scan (e.g. |
| array | Security findings (severity, category, description, file, line) |
| object | Optional SKILL.md metadata |
Without skill_url, the backend skips Crucible forwarding and the skill can never be certified.
Level | Meaning |
V1 | Basic verification — scanned by community, passed automated static analysis (32+ pattern categories + AI triage) |
V2 | Enhanced verification — V1 + passed Crucible behavioral analysis in sandboxed environment |
V3 | Full verification — V2 + passed periodic re-certification cycles |
Gold | Highest trust — V3 + continuous monitoring, reserved for critical infrastructure skills |
All levels are registered on-chain. The entire certification process is automated — no human review is involved at any level. Higher levels require progressively more rigorous automated verification.
The server automatically registers a free API key on first use and caches it in ~/.skvil/mcp-config.json.
To use an existing key:
{
"mcpServers": {
"skvil": {
"command": "npx",
"args": ["-y", "@skvil/mcp-server"],
"env": {
"SKVIL_API_KEY": "sk_your_key_here"
}
}
}
}For self-hosted or development instances:
{
"env": {
"SKVIL_API_URL": "http://localhost:8000"
}
}SKVIL_API_KEY environment variable
Cached key in ~/.skvil/mcp-config.json
Legacy Python client key in ~/.skvil/config
User: Check if sha256:4a2f...c81e is safe
Agent calls skvil_verify → gets reputation, certification, findings
Agent: This skill has a reputation score of 91.2 (safe) and is
on-chain certified at V2 level. 15 community scans with
no critical findings. Safe to install.User: Show me certified skills
Agent calls skvil_certified → gets list of on-chain certified skills
Agent: There are 7 on-chain certified skills:
1. web-scraper — V2, score 91.2, certified Feb 18
2. data-pipeline — V1, score 85.0, certified Feb 15
...User: Report sha256:dead...beef — it's sending data to an unknown server
Agent calls skvil_report → submits report
Agent: Report #42 submitted. The skill will be re-analyzed
automatically. If the report is confirmed, the skill
will be flagged and any on-chain certification will
be revoked.┌─────────────┐ stdio ┌────────────┐ HTTPS ┌──────────────────┐
│ AI Agent │ ◄────────────► │ skvil-mcp │ ────────────► │ api.skvil.com │
│ (Claude, │ MCP tools │ (local) │ REST API │ (reputation DB │
│ GPT, etc) │ │ │ │ + on-chain) │
└─────────────┘ └────────────┘ └──────────────────┘The MCP server runs locally as a subprocess of your AI client. It translates MCP tool calls into HTTPS requests to the Skvil API. No data is stored remotely except scan results and reports — and certifications are anchored on-chain for public verification.
git clone https://github.com/Skvil-IA/skvil-mcp.git
cd skvil-mcp
bun install # or: npm install
bun run build # or: npm run build# Point to local API for development
SKVIL_API_URL=http://localhost:8000 bun dist/index.jsnpx @modelcontextprotocol/inspector node dist/index.jsnpm run lint
npm run format
npm run typecheckMIT — Skvil 2026
Browse the full catalog of Skvil-certified AI agent skills with detailed metadata: author, version, description, provider, agent platform, file count, and install URL. Returns up to 100 skills. Use this to discover safe skills available for installation.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns up to 100 skills (a limit), includes metadata fields, and that skills are 'safe' and 'available for installation'. However, it doesn't mention potential rate limits, authentication needs, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first states purpose and scope, the second provides usage guidance. It's front-loaded with the core functionality and efficiently structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no annotations and no output schema, the description is reasonably complete: it explains what the tool does, what it returns, and when to use it. However, without an output schema, it could benefit from more detail on return format or pagination, though the 'up to 100 skills' limit is helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter information, maintaining focus on the tool's purpose and behavior. A baseline of 4 is applied for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'browse' and the resource 'full catalog of Skvil-certified AI agent skills', specifying it includes detailed metadata fields. It distinguishes from siblings by focusing on browsing/discovery rather than certification, registration, reporting, scanning, statistics, or verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this to discover safe skills available for installation', providing clear when-to-use guidance. It implies this is for discovery purposes, distinguishing it from tools that might perform actions like registration or verification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List skills that have been verified and certified by Skvil admins. Certified skills have been manually reviewed and registered for tamper-proof verification. Returns up to 10 most recently certified skills with their level (V1/V2/V3/Gold), reputation score, and certification date.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and adds valuable behavioral context: it discloses that results are limited to 'up to 10 most recently certified skills' (implying a limit and recency ordering), and details the return fields (level, reputation score, certification date). It does not mention permissions or rate limits, but provides clear output expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. Every sentence adds value: the first defines the action and resource, the second explains certification and specifies return details. It is appropriately sized with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is complete enough: it explains what the tool does, the scope of results, and the return format. It could improve by mentioning any prerequisites or error cases, but for a simple list tool, it covers essential context adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, maintaining focus on the tool's purpose and output. Baseline is 4 for 0 parameters, as it avoids unnecessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List skills that have been verified and certified by Skvil admins') and resource ('certified skills'), distinguishing it from siblings like 'skvil_register' (likely for registration) or 'skvil_verify' (likely for verification processes). It specifies the scope of 'certified' skills versus other types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving certified skills with details like level and date, but does not explicitly state when to use this tool versus alternatives such as 'skvil_catalog' (which might list all skills) or 'skvil_stats' (which might provide statistics). It provides context but lacks explicit guidance on exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Register for a free Skvil API key. The key is automatically cached locally for future use. No sign-up or account required. Other tools (skvil_scan, skvil_report) will use the cached key automatically.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's a registration tool with no sign-up/account required, the key is automatically cached locally, and other tools will use it automatically. It doesn't mention rate limits or error handling, but covers core functionality adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose, followed by important behavioral details in three concise sentences with zero wasted words, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is nearly complete: it explains what it does, how it behaves, and its relationship to siblings. It could mention what happens on failure or the key format, but for this context, it's sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema coverage, the baseline is 4. The description adds value by explaining that no input is needed ('No sign-up or account required'), which clarifies the parameterless nature beyond what the empty schema indicates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Register for a free Skvil API key') and resource ('Skvil API key'), distinguishing it from siblings like skvil_scan or skvil_report by focusing on key acquisition rather than scanning or reporting functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool ('Register for a free Skvil API key') and provides clear alternatives/context by mentioning that other tools (skvil_scan, skvil_report) will use the cached key automatically, indicating this should be run first for key setup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report a suspicious or malicious AI agent skill to Skvil admins for review. Requires an API key (use skvil_register first). Reports are reviewed by admins and confirmed findings lead to certification revocation.
| Name | Required | Description | Default |
|---|---|---|---|
| hash | Yes | SHA-256 composite hash of the skill (e.g. "sha256:4a2f8b...c81e") | |
| reason | Yes | Why this skill is suspicious (10-1000 characters) | |
| details | No | Additional details or evidence (optional, max 5000 characters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a write operation (reporting), requires authentication (API key via skvil_register), involves admin review, and has consequences (certification revocation). It doesn't mention rate limits or error handling, but covers the essential mutation and auth aspects well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: purpose, prerequisites, and outcome. Each sentence adds critical information without redundancy. It's front-loaded with the main action and appropriately concise for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description provides strong context: purpose, prerequisites, process, and consequences. It doesn't detail the response format or error cases, but given the schema's 100% coverage and the clear behavioral disclosure, it's largely complete. A minor gap in output specifics prevents a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain hash format or reason constraints). Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Report a suspicious or malicious AI agent skill') and the target resource ('to Skvil admins for review'), distinguishing it from siblings like skvil_catalog (list), skvil_verify (check), or skvil_register (register). It explicitly mentions the purpose of reporting for admin review and potential certification revocation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: it states when to use ('Report a suspicious or malicious AI agent skill'), prerequisites ('Requires an API key (use skvil_register first)'), and the outcome ('Reports are reviewed by admins and confirmed findings lead to certification revocation'). This clearly differentiates it from other tools like skvil_scan or skvil_stats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit security scan results for an AI agent skill to the Skvil reputation network. Contributes to the community reputation score (EMA). Requires an API key (use skvil_register first). The server recomputes the score from findings — always provide accurate findings.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Skill name | |
| composite_hash | Yes | SHA-256 composite hash of the skill (e.g. "sha256:4a2f8b...c81e") | |
| file_count | Yes | Number of files in the skill | |
| file_hashes | Yes | Map of relative file paths to their SHA-256 hex hashes | |
| score | Yes | Computed security score (0-100) | |
| risk_level | Yes | Overall risk assessment | |
| findings | No | Security findings detected in the skill | |
| frontmatter | No | SKILL.md frontmatter metadata (optional) | |
| skill_url | Yes | Source URL of the skill (e.g. "https://github.com/user/repo") | |
| provider | Yes | Platform hosting the skill | |
| agent | Yes | Agent platform submitting the scan (e.g. "claude", "codex", "openclaw") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and discloses several behavioral traits: it's a write operation ('Submit'), has a prerequisite (API key from skvil_register), contributes to community reputation, and the server recomputes scores. It doesn't mention rate limits, authentication details beyond the API key, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste: first states purpose, second provides prerequisite and community impact, third gives critical behavioral guidance. Each sentence earns its place and information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 11-parameter write tool with no annotations and no output schema, the description provides good context about purpose, prerequisites, and behavioral expectations. However, it doesn't explain what happens after submission (success response, error cases) or how the reputation score is computed from findings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description doesn't add specific parameter meaning beyond what the schema provides, though it implies that 'findings' should be 'accurate' and that the tool processes security scan results which relate to the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Submit security scan results'), target resource ('AI agent skill'), and destination ('Skvil reputation network'). It distinguishes from siblings by focusing on submitting scan results rather than cataloging, certifying, registering, reporting, or verifying.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use ('Requires an API key (use skvil_register first)'), providing a clear prerequisite and alternative tool. The instruction to 'always provide accurate findings' offers behavioral guidance for proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get aggregate statistics from the Skvil community network: total skills scanned, trusted count, critical findings, and certified skills.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation, implying it's likely read-only, but doesn't confirm this or mention other traits like authentication needs, rate limits, or what happens if the network is unavailable. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and lists key metrics. It avoids redundancy and wastes no words, though it could be slightly more structured by explicitly noting the lack of parameters or differentiating from siblings.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains what statistics are retrieved, but without annotations or output schema, it doesn't cover behavioral aspects like safety or return format. For a read-only stats tool, this is minimally viable but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, but it implicitly confirms this by not mentioning any parameters, aligning with the schema. Baseline is 4 for 0 parameters, as no compensation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('aggregate statistics from the Skvil community network'), listing the specific metrics returned. However, it doesn't explicitly differentiate this statistical retrieval tool from its siblings like skvil_report or skvil_scan, which might also involve data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like skvil_report and skvil_scan that might involve reporting or scanning data, there's no indication of when this aggregate statistics tool is preferred over those options, nor any prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if an AI agent skill is safe before installing it. Returns reputation score, risk level, certification status, and community scan data. Use this to verify any skill by its SHA-256 composite hash.
| Name | Required | Description | Default |
|---|---|---|---|
| hash | Yes | SHA-256 composite hash of the skill (e.g. "sha256:4a2f8b...c81e") |
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 that the tool returns reputation score, risk level, certification status, and community scan data, which adds useful behavioral context. However, it lacks details on error handling, rate limits, authentication needs, or what happens if the hash is invalid, leaving gaps for a safety verification tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose, return values, and usage without any wasted words. Every sentence earns its place by adding essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (safety verification with one parameter) and no annotations or output schema, the description is fairly complete: it explains what the tool does, what it returns, and how to use it. However, it could improve by detailing output structure or error cases, but it adequately covers the basics for a verification tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single 'hash' parameter with its pattern and example. The description adds minimal value by mentioning 'SHA-256 composite hash' but does not provide additional semantics beyond what the schema specifies, such as how to obtain the hash or its significance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Check') and resource ('AI agent skill'), specifying it evaluates safety before installation. It distinguishes from siblings by focusing on verification using a hash, unlike cataloging, certification, registration, reporting, scanning, or statistics tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to verify any skill by its SHA-256 composite hash'), implying it's for pre-installation safety checks. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, such as when to use skvil_certified instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no overlap: catalog browsing, certified listing, registration, reporting, scanning, statistics, and verification. The descriptions specify unique functions, making misselection unlikely.
All tools follow a consistent 'skvil_verb' pattern (e.g., skvil_catalog, skvil_register), using snake_case throughout. This predictability aids agent selection and readability.
With 7 tools, the set is well-scoped for the domain of AI skill security and reputation management. Each tool serves a specific role, covering discovery, verification, reporting, and community interaction without bloat.
The toolset covers core workflows: discovery (catalog, certified), verification (verify, scan), reporting (report), and community stats. A minor gap is the lack of a tool for managing or revoking API keys, but agents can work around this.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Pay-per-call safety checks for AI agents: screen a crypto address or URL before you transact.
Pay-per-call cybersecurity for AI agents: vuln scans, threat intel, compliance, code security.
Signed security scores for what an AI agent runs and reads: skills, MCP servers, prompts, tokens.
Agent reputation registry: check, register, and endorse AI agents
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Skvil-IA/skvil-mcp'
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