AgentAudit

Official

by agentaudit-dev

Overview Schema Related Servers Score Discussions

JavaScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v3.14.0
Disambiguation4/5
Most tools have clearly distinct purposes: discover_servers is for listing installed servers, audit_package for deep code audits, check_package for registry lookups, etc. However, check_package and consensus_analysis both relate to registry lookups and could be confused, though descriptions clarify that one is for initial checks and the other for multi-auditor consensus.
Naming Consistency4/5
Six of the seven tools follow a consistent verb_noun pattern (discover_servers, audit_package, submit_report, check_package, search_packages, scan_tool_poisoning). The one deviation is 'consensus_analysis', which uses a noun phrase instead of a verb_noun form, creating a minor inconsistency.
Tool Count5/5
With 7 tools, the set is well-scoped for the security-audit domain. Each tool serves a distinct stage (discovery, lookup, search, audit, submission, consensus, poisoning scan), fitting comfortably within the ideal 3-15 range without bloat or deficiency.
Completeness4/5
The toolset covers the full workflow from discovering installed servers to auditing, submitting reports, and obtaining consensus. While there is no tool to edit or delete submitted reports, this is a minor gap and the core lifecycle is complete.
Average 4.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under AGPL 3.0.
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions scanning and extraction from code but omits important behaviors such as network access/repo cloning (only mentioned in the schema's source_url parameter description), potential side effects, and what the output format is. This is a significant gap for a tool that may clone external repositories.
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, front-loaded with the main action, and contains no redundant words. It efficiently conveys the purpose, usage context, and input methods.
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?
The description covers the purpose and inputs well, but it lacks information about the output format and edge cases (e.g., what happens if neither tool_definitions nor source_url is provided). With no output schema to compensate, this leaves some gaps, though the core functionality is adequately described.
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 the baseline is 3. The description adds value by clarifying the OR relationship between tool_definitions and source_url, which is not explicit in the schema. However, it doesn't add further semantics beyond that, so a 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 uses a specific verb ('scan'), a clear resource ('MCP tool definitions'), and a detailed scope (hidden instructions, unicode tricks, obfuscated payloads, manipulation patterns). This clearly distinguishes the tool from siblings like discover_servers or audit_package.
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 states 'Use this to check if a server's tools contain poisoning indicators', providing a clear context for when to use it. It also explains the two input modes (tool_definitions or source_url). It doesn't mention exclusions or alternatives, but the sibling tools serve different purposes, so the context is sufficient.
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?
No annotations are provided, so the description must convey behavioral traits. The verbs 'Get' and 'Shows' imply a read-only operation, but the description does not explicitly state that it avoids side effects, permissions, or rate limits. It partially discloses behavior by describing the output, but lacks a clear safety guarantee.
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 concise sentences. The first sentence immediately states the core function, and the second adds useful details about what is shown and a usage hint. There is no redundant or filler content.
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 tool with one parameter and no output schema, the description adequately explains the return content: number of auditors/models, agreement, and consensus risk level. It lacks details about edge cases or how to interpret risk levels, but overall it is sufficiently complete for an agent to invoke correctly.
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?
The schema covers 100% of parameter descriptions, including an example ('fastmcp', 'mongodb-mcp-server'). The tool description adds minimal extra meaning beyond mentioning 'package' and the context of security posture. Since the schema already does the heavy lifting, the 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 starts with a specific verb+resource: 'Get multi-auditor consensus analysis for a package.' It clearly explains what the tool does and distinguishes itself from single-audit tools by emphasizing 'multi-auditor' and 'consensus.' The additional detail about showing counts, agreement, and risk level makes 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 clear context: 'Useful for getting a second opinion on any package's security posture.' This signals when to use the tool, particularly as a cross-check on findings. However, it does not explicitly mention alternatives or state when not to use this tool, so it misses the highest bar for sibling differentiation.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the return content (matching packages with risk scores and audit status), but it omits details like result ordering, pagination, or whether the search is fuzzy/exact. This is sufficient but not rich.
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 two sentences. The first sentence front-loads the action and result, and the second provides usage guidance. Every word earns its place with no redundancy.
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 one-parameter search tool with no output schema, the description is nearly complete: it explains the purpose, the return content, and when to use it. The only minor gap is not discussing result ordering or limit behavior, but these are not essential for basic usage.
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?
The schema already fully describes the single 'query' parameter with examples, so the description adds little new meaning about the parameter itself. It reinforces that the query targets packages but does not go beyond the schema, matching the baseline for high 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 uses a specific verb 'Search' with a clear resource ('AgentAudit security registry for packages') and states what is returned (risk scores and audit status). This effectively differentiates it from sibling tools like audit_package or check_package, which imply checking a known package rather than searching by name.
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 states when to use this tool: 'when you need to find a package but don't know the exact name.' It provides clear context but does not name specific alternative tools or when not to use it, so it falls just short of a full 5.
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 carries the full burden. It discloses that it clones the repo, returns source code, and mentions a 3-pass methodology. However, it is slightly ambiguous whether the methodology is executed by the tool or is a guide for the agent, and it does not mention side effects like repository size limits or auth needs. Still, the core behavior is transparent.
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 three sentences, each serving a purpose: the first defines the core action, the second describes the workflow, and the third provides usage guidance. It is concise, front-loaded, and free of 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?
The description covers the tool's purpose, when to use it, and the follow-up action, which is sufficient for a one-parameter tool with no output schema. However, it leaves some details ambiguous, such as the exact output format and how the 3-pass methodology manifests, but overall the context is well covered.
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?
The schema fully documents the single parameter 'source_url' with a description and example. The tool description does not add specific parameter-level details beyond what the schema already 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a 'deep security audit of a Git repository' with a specific verb and resource. It distinguishes itself from siblings by explicitly mentioning check_package and submit_report, positioning this tool as the core auditing step.
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?
The description gives explicit guidance: 'Use check_package FIRST to see if an audit already exists — only use this for unaudited packages or when a fresh audit is requested.' It also instructs to call submit_report with findings after analysis, clearly defining when and how to use the tool.
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 takes on the transparency burden. It discloses the tool's output (risk score, findings, status) and its not-found behavior (suggesting an audit), adding valuable behavioral context. However, it does not explicitly confirm that the operation is read-only or mention any side effects, though 'look up' strongly implies a non-destructive query.
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 efficiently structured with the key purpose and usage priority up front. It delivers useful information in every sentence, though there is slight redundancy between 'USE THIS FIRST' and 'go-to tool' that could be tightened.
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 lookup tool with one parameter and no output schema, the description covers all essential aspects: purpose, return values, usage timing, and fallback behavior. It is contextually complete for an agent to decide when and how to invoke it.
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?
The input schema fully documents the single parameter package_name with a helpful description and examples (100% coverage). The tool description adds context about the package being an MCP server or package but does not provide additional parameter-specific semantics beyond the schema. A 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 identifies the action ('look up a package'), the resource ('AgentAudit security registry'), and the expected return info (risk score, findings, audit status). It also explicitly distinguishes itself from audit_package by positioning itself as the first-stop lookup tool, making it a clear purpose.
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?
The description explicitly states when to use this tool ('USE THIS FIRST whenever the user wants to install, add, evaluate, or learn about a specific MCP server or package') and frames it as the go-to for safety questions. It also implies an alternative by noting that if the package isn't found, it suggests running an audit, which indirectly points to audit_package.
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 carries full responsibility for disclosing behavioral traits. It clearly states that 'The report becomes publicly available,' which is a critical irreversible side effect. However, it does not mention whether the submission is permanent or if any authentication is required, though the public-availability disclosure is the most important trait.
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 three sentences, each earning its place: purpose, when to use, and why it matters. There is no redundancy or fluff, and the most important information is 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 the simple interface (one parameter, fully documented) and no output schema, the description covers the essential context: the action, prerequisite, and public impact. It doesn't mention error handling or return values, but these are not required when the schema is self-sufficient. Minor gaps like potential validation or failure modes prevent a perfect score.
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?
The schema already provides 100% coverage of the 'report' parameter, including required fields and their types. The description adds the concept of 'completed' and ties the report to the output of audit_package, but does not add further syntactic or structural detail. This aligns with the baseline of 3 for high 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 uses the specific verb 'Submit' with a clear resource: 'completed security audit report to the AgentAudit registry (agentaudit.dev)'. This distinguishes it from sibling tools like audit_package and check_package, which are about analysis rather than submission. The mention of being 'after audit_package' further clarifies its role.
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?
It gives an explicit when-to-use instruction: 'Call this after you have analyzed the code from audit_package.' This is a clear prerequisite that helps the agent choose the correct timing. It also implies not to call before an audit is complete, effectively excluding premature use.
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 carries the full burden. It clearly implies a read-only operation by using 'scan' and 'list', and specifies the source as local config files. It does not explicitly state 'read-only' or mention any side effects, but the verbs make the passive nature clear. Lacks detail on what happens after scanning (e.g., output format), but the core behavior is transparent.
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 tightly packed sentences: the first states the tool's purpose and scope, the second provides usage boundaries. No fluff or redundancy. Front-loaded with the primary 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?
The tool is simple (one optional parameter, no output schema, no annotations). The description fully covers what the tool does, when to use it, and when not to. It even names the alternative. The lack of an output schema is mitigated by the description's 'list' wording, which implies the return type.
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% (the single parameter check_registry is described in the schema). The description adds no additional semantics about the parameter, but the baseline is 3 when schema coverage is high, and the parameter's purpose is clear from the schema.
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 states a specific verb ('list') and resource ('ALREADY INSTALLED MCP servers') with explicit sources (Claude Desktop, Cursor, Windsurf, VS Code). It clearly distinguishes from sibling tools by instructing to use check_package for install/evaluate/lookup tasks.
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?
Provides explicit when-to-use ('Use ONLY when the user wants to review/list their existing servers') and when-not-to-use ('Do NOT use this when the user wants to install, evaluate, or look up a specific package'), and names the alternative tool (check_package). This is exemplary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![agentaudit-mcp MCP server](https://glama.ai/mcp/servers/agentaudit-dev/agentaudit-mcp/badges/card.svg)](https://glama.ai/mcp/servers/agentaudit-dev/agentaudit-mcp)

Score Badge

Copy to your README.md:

[![agentaudit-mcp MCP server](https://glama.ai/mcp/servers/agentaudit-dev/agentaudit-mcp/badges/score.svg)](https://glama.ai/mcp/servers/agentaudit-dev/agentaudit-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/agentaudit-dev/agentaudit-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server