mcp-security-toolkit

Server Quality Checklist

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Latest release: v0.3.1
Disambiguation5/5
Each tool has a clearly distinct purpose, from auditing agent tool schemas to generating PHP gadget chains. No two tools overlap in functionality or target domain.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with a domain-specific prefix (e.g., graphql_introspect, jwt_inspect, wordlist_gen), making them predictable and easy to distinguish.
Tool Count5/5
14 tools is a well-scoped collection for a security toolkit. Each tool serves a distinct purpose without redundancy, and the count is neither too small nor too large for the domain.
Completeness3/5
The toolkit covers a wide range of security utilities, but there are notable gaps for a generic security toolkit (e.g., no port scanning, exploitation beyond PHP gadget chains, or vulnerability scanning). The focus seems skewed toward MCP and LLM security, but the presence of generic tools like default_creds_lookup suggests a broader scope, which is not fully covered.
Average 4.3/5 across 14 of 14 tools scored. Lowest: 3.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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 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.
This repository includes a glama.json configuration file.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. States it 'Reads' interactions and returns a PollReport, which implies non-destructive read. However, no mention of whether polling consumes data, rate limits, or other behavioral traits.
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?
Extremely concise: two sentences plus structured Args/Returns. No fluff, front-loaded with purpose. Every sentence earns its place.
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?
Adequate for a simple tool with one parameter. Includes return description (PollReport). Lacks detail on what PollReport contains, but acceptable given low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description adds crucial context: token is 'returned by interactsh_register'. This links it to the registration process, adding value beyond the schema.
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?
Clearly states verb 'Read' and resource 'OOB interactions for a previously-registered token'. Purpose is well-defined, but does not explicitly differentiate from sibling tools like interactsh_register or interactsh_stop.
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 usage after registration (token from interactsh_register), but lacks explicit when-to-use or when-not-to-use guidance. No alternatives mentioned despite related siblings.
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 full burden. It explicitly states 'Pure function, no I/O, no chaining' and lists input formats and report categories, offering good transparency. Could mention error handling for malformed schemas.
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 front-loaded with purpose and structured with a list of reports, Args, and Returns. It is moderately concise, though the bullet points are presented as plain text, which is acceptable but slightly less readable.
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 tool's complexity (auditing for schema risks) and absence of output schema, the description provides substantial context: input format, output type, and behaviors checked. It is sufficiently complete for an agent to understand usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no description for the parameter, but the description explains 'schema: A tool definition as a dict', adding meaning beyond the schema's bare 'object' type. It clarifies acceptable formats (OpenAI, Anthropic, etc.).
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 verb 'audit' and the resource 'a single agent tool definition', and lists specific risk categories it checks, distinguishing it from siblings like mcp_server_audit or prompt_injection_audit.
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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or how it compares to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions returning a PhpggcReport and that `available` is False if phpggc is missing, but fails to disclose behavioral traits like executing shell commands, security implications, or potential side effects of generating exploit payloads.
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, with a clear Args section and Returns section. Every sentence is informative, and there is 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?
Given the tool's complexity (5 parameters, no annotations, no output schema), the description is fairly complete. It explains inputs, output structure (PhpggcReport with payload and available flag), and a note on installation. However, it could benefit from mentioning potential risks.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining each parameter: chain (with example), command, encoding (listing options), fast_destruct (adding raw argument), and extra_args. It adds meaning beyond the schema's type/constraints.
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 ('Generate') and resource ('PHP unserialize gadget chain via `phpggc`'), clearly distinguishing the tool from its siblings which are unrelated security tools.
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 lists all parameters and explains how to use them, including a hint to run `phpggc -l` locally. It implicitly covers when to use (generating gadget chains) but does not explicitly state when not to use or provide alternatives.
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?
The description explicitly states it is a pure function with no LLM call, no I/O, and no chaining, which is valuable transparency given no annotations. It also details the analysis types, though it lacks explanations of performance or size limits.
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 and well-structured: a one-sentence overview, bullet-point list of reports, and a final line for behavioral traits. Every part adds value 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?
Given the tool has one parameter, no output schema, and no annotations, the description is mostly complete. It covers purpose, behavior, and return format (Structured AuditReport). Minor omissions like prompt length limits are acceptable.
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 provides a basic definition for the single parameter 'prompt: The system prompt or template text.' This is adequate but does not add format or length constraints.
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's purpose: statically analyze a system prompt for prompt-injection surface. It lists specific reports (placeholders, missing delimiters, dangerous patterns, precedence inversion), making it distinct from siblings like owasp_llm_classify.
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 for security analysis of prompts but does not explicitly state when to prefer this tool over siblings or provide exclusions. The context from sibling names gives some guidance, but direct advice is missing.
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 all behavioral traits: decoding, auditing specific claim issues, and optional weak secret checking. It states it returns a structured report. However, it does not mention if the tool has any side effects or is purely read-only.
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 front-loaded with the purpose and uses bullet-style listing for details. Although slightly verbose, each sentence adds value. It could be tightened but remains well-structured.
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 complexity of JWT inspection, the description covers most essential aspects. However, the lack of an output schema means the agent does not know the exact structure of the returned inspection report, which is a gap.
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?
With 0% schema coverage, the description adds essential meaning: token is described as 'JWT string (three dot-separated base64url segments)' and check_weak_secrets explains its default and behavior. This fully compensates for the missing schema descriptions.
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 explicitly states 'Decode and audit a JWT' and lists specific checks (algorithm issues, expiry, missing claims, suspicious kid, weak secret check), making the purpose clear and distinct from sibling tools like graphql_introspect or owasp_llm_classify.
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?
While the description implies usage for JWT analysis, it does not explicitly state when to use this tool versus alternatives or provide exclusions. No guidance on preconditions like token format validation.
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 burden. It discloses key behaviors: spawning a detached process, capturing the URL, persisting a session descriptor, and returning a token. It does not detail cleanup, rate limits, or error handling, but covers the main operation sufficiently.
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 and front-loaded, with no unnecessary words. Each sentence earns its place: purpose, behavior, return value, and parameters.
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 tool's complexity (spawning a CLI, persisting state, returning a token), the description covers the main aspects: what it does, parameters, and return type. No output schema exists, so the description's mention of RegisterReport with callback_url and token is helpful. Missing error conditions or prerequisites, but adequate overall.
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 description coverage is 0%, so the description must compensate. It explains both parameters (server and timeout) with their purposes and defaults, adding meaning beyond the schema's type definitions.
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 tool registers a callback URL via interactsh-client, naming the verb and resource. It hints at the workflow by mentioning the returned token pairs with interactsh_poll, but does not explicitly differentiate from siblings like interactsh_poll or interactsh_stop.
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 by stating the token pairs with interactsh_poll, implying the sequential use of register then poll. No exclusions or when-not-to-use guidance are given, but the workflow hint is valuable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states it performs static analysis ('Does NOT execute the target file'), enumerates risk categories (shell execution, filesystem writes, etc.), and warns that absence of findings is not proof of safety. This exceeds what annotations would provide (none exist).
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 concise and front-loaded with the core action. The prose is clear and avoids redundancy, though it could be slightly more terse by removing parenthetical clarifications.
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?
Given the simplicity (2 parameters, no output schema, no annotations), the description is complete: it covers parameters, behavior, return structure (coverage/limitations), and key constraints. No critical information is omitted.
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?
With 0% schema description coverage, the description fully compensates: it explains 'path' as an absolute path to a Python file and 'max_bytes' as a DoS protection mechanism with default value and advice for large files.
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's purpose: 'Statically audit an MCP server Python source file.' It specifies the exact resource and action, and distinguishes from siblings (e.g., agent_tool_risk_audit) by focusing on FastMCP-style decorators and risk findings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is provided. The description does not mention sibling tools or alternative scenarios, leaving the agent to infer usage solely from the purpose.
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 burden. It transparently states it performs no network activity or scanning, indicating safe, read-only behavior. No side effects or rate limits are mentioned, but for this tool, that's acceptable.
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 and well-structured: a clear purpose sentence, bullet-like input explanation, and a behavioral note. Every sentence adds value 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?
Despite no output schema, the description mentions the return type (LookupReport with matched_keys and credentials) and explains input formats comprehensively. For a simple lookup tool, it provides sufficient context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter 'query' with 0% schema coverage. The description compensates by explaining it accepts short product names, full keys, or substrings, adding significant meaning beyond the schema's type-only definition.
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 explicitly states the tool returns known default credentials, explains input formats (short product name, full key, substring), and output structure, clearly distinguishing it from sibling tools like 'sensitive_files_list' or 'wordlist_gen'.
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 notes 'Pure data lookup — no network, no scanning,' indicating when to use it (passive lookup) and when not (for active scanning). It does not explicitly name alternatives but provides clear context.
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 full burden. It states termination of the process (best-effort) and removal of session descriptor and log file (by default). This is fairly transparent, though it does not mention graceful vs forceful termination or other side effects.
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 (four sentences) with a clear structure: main purpose, details, then Args and Returns. Every sentence adds value with no redundancy.
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 stop/cleanup tool with 2 parameters (1 required) and no output schema, the description covers the action, inputs, and return format completely. The returns are explicitly described in a structured note.
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 coverage is 0%, but the description adds significant value: token is described as 'token returned by interactsh_register', and delete_log is described as 'also remove the session log file (default True)'. This fully explains the parameters' purposes.
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 explicitly states the tool stops a previously-registered interactsh-client session and cleans up. It uses specific verbs ('stop', 'clean up') and resource ('interactsh-client session'). The sibling tools include interactsh_register and interactsh_poll, making this distinct.
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 use after registration and polling, but does not explicitly state when to use this tool versus alternatives (e.g., interactsh_register, interactsh_poll). No when-not or alternative guidance is 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?
With no annotations, the description fully explains the behavioral traits: it is rule-based using keyword and regex patterns with weights, and returns top matches with evidence snippets and confidence. It also clarifies the return format when no matches are found.
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 (9 lines) and front-loaded with the core purpose. Every sentence adds value, with 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?
Given no output schema, the description adequately explains the return type (ClassifyReport with matches and unmatched flag). It covers input constraints and expected output, though additional details on confidence scoring could be useful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining both parameters: 'observation' as free-form text describing findings, and 'top_n' as the number of matches with a default of 3. This adds meaning beyond 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 clearly states the tool maps findings to OWASP LLM Top 10 categories, with a specific verb and resource. It distinguishes itself from sibling tools, which are unrelated security audit tools.
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 for when to use the tool (mapping observations to OWASP categories) and lists example inputs. It does not explicitly state when not to use it, but the sibling tools cover different tasks, making the intent clear.
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 no network is performed and describes the return format (FilesReport with paths). This provides good transparency about the tool's behavior and non-destructive nature.
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, with a clear summary line followed by structured argument definitions. Every sentence adds information without redundancy or fluff.
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?
Given the simple nature of the tool (2 parameters, no output schema), the description covers all necessary aspects: input parameters, default behavior, return structure, and side-effect information (no network). It is complete for an agent to use correctly.
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?
The schema has 0% description coverage, but the description fully explains both parameters: stack lists supported keys, and include_common describes its default and effect. This adds significant value beyond the schema's type and default fields.
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 'Return curated sensitive-path lists for a given tech stack,' specifying the verb, resource, and context. It effectively distinguishes this tool from siblings like graphql_introspect or http_diff, which have different purposes.
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 explains how to use the stack and include_common parameters, and notes that no network is performed. While it doesn't explicitly list when not to use the tool or alternatives, the provided context is sufficient for typical usage.
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?
Without annotations, the description carries the full burden. It explicitly states 'Pure function. No network.' which is a key behavioral trait. It also describes the return type (GenReport with sample) and the hard cap via max_size. However, it doesn't mention if there are any limits on input sizes or potential errors.
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 highly concise and well-structured: a one-line summary, then bulleted mode descriptions with clear transformations, followed by a parameter list. Every sentence adds value, and the format is easy to parse for an AI agent.
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?
Given 6 parameters, no output schema, and no annotations, the description covers all essential aspects: purpose, mode details, parameter roles, behavioral traits (pure, no network), and return type. There are no obvious gaps that would hinder correct invocation.
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?
The input schema has no descriptions (0% coverage), but the tool description fully compensates by explaining each parameter: mode enum, brand used across modes, names for usernames, keywords for passwords/subdomains, years for passwords, and max_size as a cap. This provides all necessary semantic context beyond the raw 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 clearly states the tool's purpose: 'Generate a wordlist tailored to the target surface.' It lists three distinct modes (passwords, usernames, subdomains) with specific transformations, making it easy to understand the output. The tool's function is well-differentiated from sibling tools which focus on audits, creds lookup, etc.
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 usage scenarios by mode (e.g., combine brand/keywords with leet substitution for passwords). It doesn't state when to avoid using the tool, but the mode descriptions implicitly guide appropriate contexts. No explicit alternatives are given, but the sibling tools are clearly different.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description fully carries the burden. It discloses that it is a single HTTP POST, read-only, has SSRF protection with override, redirects disabled, and residual DNS rebinding risk. This is comprehensive and leaves no ambiguity about behavior.
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?
Well-structured with paragraphs and bullet points. Every sentence adds value without redundancy. The length is appropriate for the complexity, and key information is front-loaded.
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?
No output schema exists, but the Returns section summarizes the return type and error case. All aspects (purpose, behavior, parameters, security, errors) are covered, making the description complete for the tool's complexity.
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 coverage is 0%, but the Args section describes each parameter in detail: url, timeout (clamped), insecure (for self-signed certs), allow_private (default False). This adds significant meaning beyond the bare schema properties.
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?
Clearly states it runs a GraphQL introspection query against a URL and summarizes the schema. The verb 'run' and resource 'GraphQL introspection query' are specific, and the tool is clearly distinguished from sibling tools which cover different security audit tasks.
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 context for when to use (introspect GraphQL APIs) and includes guidance on SSRF protection and the allow_private flag for internal audits. Does not explicitly list exclusions or alternatives, but sibling tools cover different areas making the use case obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: stateless operation, input format options (strings or dicts), and detailed report content including status transitions, header diffs, cookie attribute diffs, and body diff. It is transparent about what the tool does.
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 well-structured with line breaks and bullet points. It front-loads the main purpose and uses clear, efficient language. Every sentence adds value.
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?
Given there is no output schema, the description fully explains the report contents (status transitions, header diffs, cookie diff, body diff). Input formats are well-specified. The tool is simple (stateless, two inputs) and the description covers all needed context.
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?
The input schema provides only generic titles for parameters. The description adds crucial meaning: 'Inputs may be raw HTTP response strings (status line + headers + body) or dicts shaped...' This clarifies the expected structure beyond 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 clearly states the tool diffs two HTTP responses with security-relevant findings. It specifies the resource (HTTP responses) and the action (diff). No sibling tool duplicates this function, so it distinguishes effectively.
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 implies when to use (comparing HTTP responses for security analysis) and notes statelessness. It doesn't explicitly state alternatives or when not to use, but the unique functionality makes the context clear.
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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