Deterministic security preflight for AI agents. Check URLs, files and shell commands before acting.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.3/5 across 4 of 4 tools scored.
Each tool targets a distinct input type (tool call, URL, command, file), but analyze_tool_call could overlap with inspect_command when the tool call is a shell command. Descriptions are specific enough to mostly disambiguate.
All tools follow a verb_noun pattern, but they mix 'analyze_' and 'inspect_' as verbs. The structure is consistent across tools, though the verb choice is not uniform.
Four tools covering the core analysis targets (action, URL, command, file) is well-scoped for a security-analysis utility, not too thin and not overbuilt.
The surface covers the main entities an agent would need to analyze, but there are minor gaps such as a tool for managing policies or analyzing other data types (e.g., IP addresses or free-form text).
analyze_tool_callAAnalyze a proposed AI-agent tool call before execution and return a deterministic, policy-aware decision (allow, notice, confirm or block) covering destructive actions, sensitive-data exposure, external transmission, privilege changes and irreversible operations, under the applicable policy — permissive, balanced or strict, balanced by default. Filesystem paths outside a known workspace_root are treated as higher risk than paths inside it.
| Name | Required | Description | Default |
|---|---|---|---|
| policy | No | Optional policy used to compute the decision: permissive, balanced or strict (default: balanced). | |
| context | No | Optional declarative context about the proposed operation. | |
| arguments | Yes | The proposed tool arguments. They are analyzed as data and never executed. | |
| tool_name | Yes | The exact name of the proposed tool. |
| Name | Required | Description |
|---|---|---|
| risk | Yes | |
| valid | Yes | |
| checks | Yes | |
| signals | Yes | |
| decision | Yes | |
| tool_name | Yes | |
| risk_score | Yes | |
| policy_applied | Yes | |
| schema_version | Yes | |
| ruleset_version | Yes | |
| context_completeness | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context by listing the categories of risk assessed (destructive actions, sensitive-data exposure, external transmission, privilege changes, irreversible operations) and explaining the policy tiers and workspace_root risk. This goes beyond the annotations.
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 sentence that is information-dense and well-structured. It front-loads the core purpose and then lists covered aspects, with no redundant filler. Every clause contributes value.
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?
The tool is complex with nested objects and an output schema, but the description covers the purpose, decision types, policy modes, and risk factors. It gives the agent enough to understand when and how to use it, and the output schema explains return values. The description is 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.
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 repeats some parameter details (policy levels, workspace_root) but adds no new meaning beyond the schema. It does not compensate for any gaps because there are none.
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 function: analyzing a proposed AI-agent tool call before execution and returning a decision. It specifies the resource (tool call), the action (analyze), and the output (allow/notice/confirm/block), distinguishing it from sibling tools like inspect_file or analyze_url_risk which target different resource 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 explicitly says 'before execution,' indicating when to use the tool. It also describes the policies and risk factors, giving clear context. However, it does not explicitly mention when not to use it or name alternatives, though sibling names imply a division of labor.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
analyze_url_riskAAnalyze a URL for structural and security risk signals and return a deterministic, policy-aware decision (allow, notice, confirm or block) under the applicable policy — permissive, balanced or strict, balanced by default.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute URL to analyze. | |
| policy | No | Optional policy used to compute the decision: permissive, balanced or strict (default: balanced). | |
| context | No | Optional declarative context. |
| Name | Required | Description |
|---|---|---|
| port | Yes | |
| risk | Yes | |
| input | Yes | |
| valid | Yes | |
| checks | Yes | |
| domain | Yes | |
| signals | Yes | |
| decision | Yes | |
| hostname | Yes | |
| protocol | Yes | |
| subdomain | Yes | |
| risk_score | Yes | |
| public_suffix | Yes | |
| normalized_url | Yes | |
| policy_applied | Yes | |
| schema_version | Yes | |
| ruleset_version | Yes | |
| context_completeness | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds value by calling the analysis 'deterministic' and 'policy-aware', plus specifying the decision set. This is consistent with annotations and provides behavioral context beyond the safety flags.
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 dense sentence that includes the core action, the decision types, the policy levels, and the default policy. Every phrase earns its place, with no filler or repetition.
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 read-only tool with rich annotations and an output schema, the description is largely complete. It explains the main purpose and decision semantics, and the output schema covers return details. A slight gap is the lack of detail on 'structural and security risk signals', but this is acceptable given the annotation and schema coverage.
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 repeats the policy options and default already present in the schema but adds no new parameter meaning. The URL and context parameters are fully documented in the schema, so this is acceptable.
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 uses a specific verb ('Analyze') and resource ('URL'), and clearly states the outcome: a deterministic policy-aware decision (allow, notice, confirm or block). It distinguishes from sibling tools (which analyze tool calls, commands, or files) by the URL focus.
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 when a URL needs risk assessment, and it mentions the policy options with default, but it does not explicitly state when to use this tool over alternatives or provide any 'do not use' conditions. No exclusions or alternative references are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_commandAAnalyze a shell command before execution and return a deterministic, policy-aware decision (allow, notice, confirm or block) without running it, under the applicable policy — permissive, balanced or strict, balanced by default. Paths outside a known workspace_root are treated as higher risk than paths inside it.
| Name | Required | Description | Default |
|---|---|---|---|
| cwd | No | Optional working-directory context. It is never accessed. | |
| shell | No | The command shell, when known. | |
| policy | No | Optional policy used to compute the decision: permissive, balanced or strict (default: balanced). | |
| command | Yes | The complete command text to inspect without executing it. | |
| context | No | Optional declarative context. |
| Name | Required | Description |
|---|---|---|
| cwd | Yes | |
| risk | Yes | |
| shell | Yes | |
| valid | Yes | |
| checks | Yes | |
| command | Yes | |
| signals | Yes | |
| decision | Yes | |
| risk_score | Yes | |
| policy_applied | Yes | |
| schema_version | Yes | |
| ruleset_version | Yes | |
| context_completeness | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context: the decision types, default policy, and the higher-risk treatment of paths outside workspace_root, enriching behavioral understanding.
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 concise, two sentences with no redundancy, and front-loads the core purpose. Every sentence contributes valuable 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?
The description covers the essential behavior, policy options, and risk model. With an output schema present, it need not detail return values; overall it is sufficiently complete for an agent to select and use the 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 coverage is 100%, so parameters are fully documented. The description adds only minor elaboration (e.g., workspace risk), but the schema already explains each field adequately; no significant extra semantics are provided.
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 analyzes a shell command and returns a policy-aware decision, distinguishing it from sibling tools like analyze_tool_call and analyze_url_risk. The verb 'analyze' and resource 'shell command' are explicit and specific.
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?
Clear context is provided: use before command execution to get a decision. However, no explicit exclusions or comparisons with alternatives are mentioned, leaving some room for inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_fileAInspect a Base64-encoded file locally for deterministic structural and security risk signals without executing it, and return a policy-aware decision (allow, notice, confirm or block) under the applicable policy — permissive, balanced or strict, balanced by default.
| Name | Required | Description | Default |
|---|---|---|---|
| policy | No | Optional policy used to compute the decision: permissive, balanced or strict (default: balanced). | |
| context | No | Optional declarative context. | |
| filename | Yes | The original filename, including its extension. | |
| content_base64 | Yes | The complete file content encoded as canonical Base64 (maximum decoded size: 1 MiB). |
| Name | Required | Description |
|---|---|---|
| risk | Yes | |
| valid | Yes | |
| checks | Yes | |
| sha256 | Yes | |
| signals | Yes | |
| decision | Yes | |
| filename | Yes | |
| risk_score | Yes | |
| size_bytes | Yes | |
| detected_type | Yes | |
| policy_applied | Yes | |
| schema_version | Yes | |
| ruleset_version | Yes | |
| declared_extension | Yes | |
| context_completeness | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable context such as 'locally' and 'without executing it', and specifies the decision types and policy default. This goes beyond the annotations without contradicting them.
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, information-dense sentence with no wasted words. It front-loads the core purpose and efficiently covers safety, decision types, policy levels, and default.
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 rich input schema and the presence of an output schema, the description adequately covers the tool's core behavior, safety characteristics, and policy handling. It is complete for an AI agent to select and invoke this tool effectively.
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 provides 100% parameter description coverage, including details like maximum decoded size and the context parameter's no-effect note. The description only adds the policy options and default, which are already in the schema, so no significant additional meaning is provided.
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 function: inspecting a Base64-encoded file locally for deterministic structural and security risk signals and returning a policy-aware decision. It distinguishes itself from sibling tools by focusing on file inspection rather than command or URL analysis.
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 appropriate usage: when a user needs to analyze file content safely without execution, and mentions policy options and default. However, it does not explicitly name alternatives or exclusions beyond the implicit distinction from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
