A
license-
qualityC
maintenanceComputes multi-jurisdictional AI compliance readiness scores with sourced penalty math, enabling gap analysis and audit tier recommendations.
MIT
Sophon Consulting
Server Details
Read-only, deterministic AI triage and readiness tools implementing Sophon's published rubrics.
- 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
A4.7/5.0
Tool DescriptionsA
Average 4.7/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
Both tools have clearly distinct purposes: one for organization-wide AI readiness assessment, the other for evaluating a single AI opportunity. Each description explicitly states what it is not for, eliminating ambiguity.
Naming Consistency5/5
Both tool names follow a consistent verb_noun pattern: 'assess_ai_readiness' and 'triage_ai_opportunity', making them predictable and easy to distinguish.
Tool Count5/5
With only 2 tools, the server is sharply focused on two key consulting activities—organizational readiness and individual opportunity assessment. This is an appropriate scope for the domain.
Completeness4/5
The tools cover the initial assessment and triage phases well, but there is a minor gap: no tool for follow-up actions or detailed implementation planning, which might be needed after the verdict.
Available Tools
2 toolsassess_ai_readinessAssess AI readinessARead-onlyIdempotentInspect
Assess an organization's readiness to run AI in production across five dimensions (data readiness, workflow ownership, governance and controls, delivery capacity, executive sponsorship), each scored 1-5 with evidence or marked unknown, plus classified candidate use cases. Returns a deterministic verdict — Ready, Ready with conditions, Not yet ready, or Insufficient evidence (thresholds: ready needs no dimension below 3; 2+ dimensions at 2 or below is not yet ready) — with a handoff-ready Markdown assessment. Read-only; not for triaging a single AI idea (use triage_ai_opportunity).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| risks | No | Optional risk register entries; scoring gaps should each become one. | |
| useCases | Yes | Candidate AI use cases, each classified start-now, prepare-first, or defer. | |
| dimensions | Yes | All five readiness dimensions, each scored 1-5 with evidence, or marked unknown. | |
| sequencing | No | Optional 30-60-90 day sequencing recommendation. | |
| preparedFor | No | Optional sponsor or requester the assessment is prepared for. | |
| organizationName | Yes | The organization or team being assessed. |
Tool Definition Quality
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true, idempotentHint=true) are consistent with description. Description adds detailed behavioral context: deterministic verdict, threshold logic, and that it's read-only, beyond what annotations provide.
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 well-structured with front-loaded purpose, detailed thresholds, and sibling reference. Slightly verbose but every sentence adds value; could be more concise but not excessively long.
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?
Despite no output schema and complex nested inputs, the description covers the assessment logic, verdict determination, and output format (Markdown). It is thorough enough for an AI to understand tool behavior and prerequisites.
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 coverage is 100% and description does not reiterate parameters, but it adds meaning by explaining the dimensions (data readiness, workflow ownership, etc.) and the scoring/verdict logic, which helps the AI understand how to use the input schema effectively.
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?
Description clearly states it assesses an organization's AI readiness across five dimensions, each scored 1-5, with a deterministic verdict and Markdown output. Distinguishes from sibling tool triage_ai_opportunity by specifying when not to use it.
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?
Explicitly states not for triaging a single AI idea and directs to sibling tool triage_ai_opportunity, providing clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
triage_ai_opportunityTriage an AI opportunityARead-onlyIdempotentInspect
Score one proposed AI workflow across bottleneck severity, value capture, integration readiness, and risk posture. Returns a deterministic go-deeper or hold recommendation and a handoff-ready Markdown artifact. Read-only; not for organization-wide readiness reviews or production rollout planning.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| lenses | Yes | ||
| followUps | No | Up to five questions ranked by how much their answers could change the decision. | |
| confidence | No | Optional confidence override. The rubric supplies a conservative default. | |
| requestedBy | No | Optional person or team requesting the triage. | |
| workflowName | Yes | The specific workflow being evaluated. | |
| decisionContext | Yes | The concrete decision this triage will inform. | |
| canAnswerTopFollowUpInOneBusinessDay | No | Needed only when the total is 10-13: whether the highest-impact follow-up can plausibly be answered within one business day. |
Tool Definition Quality
A4.6/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations by specifying the tool is read-only (consistent with readOnlyHint), deterministic (consistent with idempotentHint), and outputs a Markdown artifact with a go-deeper or hold recommendation.
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 sentences, front-loaded with the main action, no redundancy, every word 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?
Given the tool's complexity (7 params, nested objects) and no output schema, the description covers the main purpose, output, and scope. It omits optional parameters but schema covers them.
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 coverage is 86%, so baseline is 3. The description lists the four lenses but adds no additional parameter semantics beyond what the schema already provides.
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 scores an AI workflow across four dimensions and returns a recommendation. It explicitly distinguishes from the sibling tool 'assess_ai_readiness' by stating it is not for organization-wide reviews.
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 (score a proposed AI workflow) and when not to use (not for org-wide readiness or production rollout), providing clear context and excluding alternatives.
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
CaseMargin MCP Serverofficial
AlicenseAqualityAmaintenanceExposes support case reasoning tools — generate structured case briefs and check escalation readiness from any MCP-compatible AI client.24971MIT
AIReady MCP Serverofficial
Alicense-qualityDmaintenanceEnables AI agents to assess AI-readiness and improve AI leverage through analysis tools, resources, and prompts for project scanning and remediation.75MIT- AlicenseBqualityAmaintenanceLocal-first production-readiness MCP server for AI-built apps. It runs read-only checks, produces an evidence-based readiness score, and guides fixes before launch.95Apache 2.0