A
license-
qualityA
maintenancePublic read-only MCP server for turva.dev's agent-readiness audit, enabling AI agents to query service catalog, security evidence, and engagement principles via structured JSON.
1
MIT
Agent Observatory Digital Twin
Server Details
Public MCP digital twin with synthetic systems and an agent firewall. No customer data.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
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.
100% free. Your data is private.
Tool Definition Quality
C2.9/5.0
Tool DescriptionsC
Average 2.5/5 across 6 of 6 tools scored. Lowest: 1.6/5.
Server CoherenceA
Disambiguation5/5
Each tool targets a distinct aspect of the synthetic digital twin environment: inspecting cloud resources, inspecting identity, listing projects, querying customer data (decoy), reading runbooks, and rotating tokens (decoy). No overlapping purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case: inspect_*, list_*, query_*, read_*, rotate_*. No mixing of conventions.
Tool Count5/5
With 6 tools, the server is well-scoped for monitoring and interacting with a digital twin. The count is neither too thin nor too heavy for its apparent purpose.
Completeness4/5
The tool set covers key inspection and interaction actions for a digital twin, including environment, identity, projects, runbooks, and decoy boundaries. Minor gaps like a general 'list' or 'search' tool are acceptable for the synthetic context.
Available Tools
6 toolsinspect_cloud_environmentCInspect
Inspect synthetic cloud resources, workloads, storage, and recent activity.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ||
| project | No |
Tool Definition Quality
C2.3/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description implies a read-only operation ('inspect') but does not disclose side effects, required permissions, rate limits, or whether resources are modified. With zero behavioral cues from annotations or description, transparency is low.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, but it is vague and under-specified. It conveys minimal information without detail, making it insufficiently concise for effective tool selection.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero annotations, no output schema, and no parameter descriptions, the description fails to provide a complete understanding of the tool's behavior, inputs, or outputs. It is inadequate for confident use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention parameters (region, project) or add any meaning to them. The agent gains no insight into how these inputs affect the inspection.
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 states it inspects synthetic cloud resources, workloads, storage, and recent activity, giving a clear verb and target domain. However, it does not differentiate from sibling tools like 'inspect_identity_access' or 'list_internal_projects', leaving ambiguity about scope and overlap.
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 guidance on when to use this tool versus alternatives. The description lacks context about prerequisites, exclusions, or scenarios where other tools are more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_identity_accessCInspect
Inspect synthetic users, roles, and service-account relationships for a project.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| project | No |
Tool Definition Quality
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description alone must disclose behavioral traits. It only implies a read operation via 'inspect', but does not explicitly state non-destructiveness, authorization needs, rate limits, or any side effects. This is minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundancy. It front-loads the action and resource, making it efficient. However, it may be too terse for a tool with no other documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having only one parameter and no output schema, the description does not explain the return format, pagination, or any additional context needed to interpret results. It is insufficient for an agent to correctly invoke the tool without further knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'project' lacks description in both the schema (0% coverage) and the description. The description mentions 'for a project' but does not clarify if it refers to project ID, name, or how to format it, nor whether it is required or optional.
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 inspects synthetic users, roles, and service-account relationships, using a specific verb and resource combination. It implicitly distinguishes from sibling 'inspect_cloud_environment' by resource type, but lacks explicit differentiation.
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, no prerequisites, and no exclusion criteria. It fails to help an agent decide between this and sibling tools like 'read_internal_runbook' or 'query_customer_export'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_internal_projectsAInspect
List projects in the synthetic company workspace.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
A3.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It only states 'List projects' without indicating read-only nature, authentication needs, or side effects. Minimal transparency for a list operation.
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 a single, concise sentence with no unnecessary words. It is front-loaded and efficient.
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 zero-parameter list tool with no output schema, the description adequately states its purpose. However, it could be improved by noting if any filtering or pagination exists, or by describing the return format.
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?
There are no parameters, so the schema coverage is 100%. Per calibration, 0 parameters warrants a baseline of 4. The description adds no parameter details, but none are needed.
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 lists projects within a specific workspace ('synthetic company workspace'). It uses a specific verb ('List') and resource ('projects'), and is distinct from sibling tools that inspect environment or identity.
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 guidance is provided on when to use this tool vs. alternatives. There is no mention of context or exclusions, leaving the agent without direction on selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_customer_exportDInspect
Query a staged synthetic customer export. This is a monitored decoy boundary.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
Tool Definition Quality
D1.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description hints at monitoring ('monitored decoy boundary') but does not disclose important traits such as potential alerts, consequences, or whether it returns real data. This is insufficient for a tool with no structural metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences but lacks substance. It is under-specification rather than concise efficiency. Every sentence adds minimal value and leaves significant gaps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (2 parameters, no output schema, no annotations), the description is grossly incomplete. It fails to explain return values, side effects, or usage constraints, leaving the agent unable to safely or correctly invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description adds no meaning to the parameters ('limit' and 'query'). Without any textual explanation, the agent must rely solely on the schema, which lists types but no semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Query a staged synthetic customer export' which identifies the verb and resource, but 'staged synthetic' and 'decoy boundary' are ambiguous. It does not clearly define what the tool does in practical terms or how it differs from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The mention of 'monitored decoy boundary' implies caution but does not provide explicit usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_internal_runbookCInspect
Read a synthetic operations runbook for a service or incident topic.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No |
Tool Definition Quality
C2.4/5.0
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states 'Read', implying idempotence, but does not confirm safe, read-only behavior, mention any side effects, or describe the return format. This is insufficient.
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?
A single sentence with no extraneous information. It is concise and front-loaded with the core action. However, it could be slightly expanded with critical details without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description completely fails to inform the agent about the tool's return value, data structure, or possible side effects. For a tool with one parameter, this is critically incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage for the single parameter 'topic'. The description adds that the runbook is 'for a service or incident topic', providing context for what 'topic' means, but does not specify expected format, examples, or constraints. Some value added, but minimal.
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 verb 'Read' and the resource 'synthetic operations runbook', specifying the context as 'for a service or incident topic'. This differentiates from sibling tools like inspect_cloud_environment or list_internal_projects, but could be more precise about what a 'synthetic operations runbook' entails.
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 guidance on when to use this tool versus alternatives. The description implies usage for reading runbooks related to services or incidents, but lacks exclusion criteria or comparison with siblings like query_customer_export or rotate_service_token.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rotate_service_tokenCInspect
Attempt to rotate a synthetic service credential. This is a contained decoy boundary.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes |
Tool Definition Quality
C2.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. The word 'attempt' suggests uncertainty, and 'contained decoy boundary' hints at a sandboxed environment, but side effects, authorization requirements, or outcomes are not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence), but it sacrifices essential details. It is under-specified rather than concise, omitting critical information about the tool's operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient. It fails to explain return values, error conditions, or the practical implications of 'decoy boundary', leaving the agent without necessary context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter 'service' with no description, and schema description coverage is 0%. The description does not mention the parameter or clarify its meaning (e.g., which service, required format).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'rotate' and the resource 'synthetic service credential', making the purpose understandable. However, the phrase 'contained decoy boundary' is vague and may confuse an AI agent about whether this is a real operation or a test.
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 guidance is provided on when to use this tool versus alternatives. The sibling tools have different intents (inspect, list, query, read), but the description does not specify the context or prerequisites for rotation.
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityBmaintenanceExposes live industrial IoT telemetry to any MCP client, streaming simulated sensor data from a fleet of machines and detecting anomalies, with the ability to inject faults on demand.4MIT
- Alicense-qualityCmaintenanceMCP server exposing distributed industrial asset data (battery storage, EV chargers, solar arrays) with tools for asset status, geospatial search, alerts, anomaly explanation, and load simulation.1,014MIT
- Flicense-qualityAmaintenanceAn MCP server that gives an AI agent read and write access to a live SysML v2 model through the vendor-neutral OMG SysML v2 REST API.