AI Business System Advisor
Server Details
Review AI workflow readiness, trust/control risks, and safer first steps.
- 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.
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.
Tool Definition Quality
Average 2.7/5 across 9 of 9 tools scored.
Each tool has a clearly distinct purpose covering different aspects of business analysis and advisory. There is no overlap; names like analyze_business_context, assess_trust_control_risks, and recommend_first_workflow are unambiguous.
All tools follow the consistent verb_noun pattern in snake_case, such as analyze_business_context, export_intake_packet, and recommend_next_step. No mixing of styles.
9 tools is well-scoped for a business system advisor. It covers analysis, risk assessment, reporting, and recommendations without being overwhelming or sparse.
The tool set provides a complete lifecycle from analyzing context to identifying bottlenecks, mapping touchpoints, assessing risks, evaluating opportunities, recommending workflows, and exporting reports. No obvious gaps.
Available Tools
9 toolsanalyze_business_contextAnalyze business contextCInspect
Summarize business model, target customer, offer, constraints, goals, missing information, and first AI opportunity hypotheses.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| offer | No | ||
| aiIdea | No | ||
| teamSize | No | ||
| goal90Days | No | ||
| constraints | No | ||
| currentGoal | No | ||
| businessType | No | ||
| revenueModel | No | ||
| riskConcerns | No | ||
| currentProblem | No | ||
| targetCustomer | No | ||
| currentWorkflow | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| confidence | Yes | |
| businessSnapshot | Yes | |
| readinessSignals | Yes | |
| missingInformation | Yes | |
| likelyBusinessModel | Yes | |
| targetCustomerSummary | Yes | |
| valuePromiseHypothesis | Yes | |
| primaryConstraintHypothesis | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only says 'summarize', implying a read-only operation, but lacks details on side effects, required permissions, or limitations. The agent has insufficient transparency about the tool's impact.
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 listing key elements, front-loaded with 'Summarize'. It is concise and avoids verbosity, though a slight structure improvement could enhance readability.
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 13 parameters, no required fields, no annotations, and no output schema details in the description, the tool description is lacking. It fails to explain parameter interactions, output format, or usage scenarios, leaving the agent with significant gaps.
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?
With 0% schema description coverage, the description must compensate. It maps some parameters (e.g., offer, targetCustomer, constraints, goals, aiIdea) but omits others like teamSize, businessType, revenueModel, and notes. The partial mapping provides minimal context, but many parameters remain opaque.
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 summarizes business model, target customer, offer, constraints, goals, missing information, and first AI opportunity hypotheses. It provides a specific verb ('summarize') and resource ('business context'), distinguishing it from more focused sibling tools like evaluate_ai_opportunities or identify_bottlenecks.
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 offers no guidance on when to use this tool versus alternatives, nor does it specify prerequisites or exclusions. It simply lists what it summarizes, leaving the agent without context for proper selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
assess_trust_control_risksAssess trust and control risksBInspect
Assess risk categories, data boundaries, human review rules, and escalation controls for AI-enabled workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| aiIdea | No | ||
| businessType | No | ||
| riskConcerns | No | ||
| workflowIdea | No | ||
| canAffectMoney | No | ||
| currentProblem | No | ||
| customerFacing | No | ||
| currentControls | No | ||
| currentWorkflow | No | ||
| proposedWorkflow | No | ||
| usesSensitiveData | No | ||
| canAffectBrandTrust | No | ||
| requiresExpertJudgment | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| riskLevel | Yes | |
| confidence | Yes | |
| riskSummary | Yes | |
| humanReviewRules | Yes | |
| requiredControls | Yes | |
| escalationTriggers | Yes | |
| missingInformation | Yes | |
| dataBoundaryWarnings | Yes | |
| notRecommendedActions | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only or has side effects, authentication needs, or rate limits. For a tool with 13 parameters, this is a significant gap.
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 concise sentence that front-loads the key purpose. While efficient, it sacrifices detail that could be included without bloat.
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 high parameter count (13) and lack of annotations or schema descriptions, the description is incomplete. It does not explain what the tool returns (though an output schema exists) or cover behavioral aspects.
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 0%, meaning no parameter descriptions are provided in the schema. The tool description does not explain any of the 13 parameters, leaving their meaning entirely ambiguous. This is insufficient for an agent to correctly fill them.
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 that the tool assesses risk categories, data boundaries, human review rules, and escalation controls for AI-enabled workflows. The verb 'assess' and resource 'trust and control risks' are specific, and it distinguishes from sibling tools focused on business context or opportunity evaluation.
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 for AI workflow risk assessment but provides no explicit when-to-use or when-not-to-use guidance. No alternatives or sibling tools are mentioned, though context signals list relevant siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
evaluate_ai_opportunitiesEvaluate AI opportunitiesCInspect
Evaluate practical AI assistance opportunities and avoid high-risk full-autonomy patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| aiIdea | No | ||
| businessType | No | ||
| riskConcerns | No | ||
| currentProblem | No | ||
| businessContext | No | ||
| currentWorkflow | No | ||
| candidateUseCases | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| warnings | Yes | |
| confidence | Yes | |
| opportunities | Yes | |
| missingInformation | Yes | |
| recommendedFirstOpportunity | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only mentions evaluation and avoidance, omitting details like whether it reads or writes data, permissions needed, or 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
While very concise (one sentence), it sacrifices necessary detail. It lacks structure and fails to earn its place by omitting critical information about usage and parameters.
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 7 parameters and no detailed description, the tool is incomplete. The output schema helps but the description does not adequately explain what the tool does or how to use it.
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 schema has 7 parameters with 0% coverage, meaning no descriptions. The tool description adds no parameter meaning, leaving the agent to infer from names alone, which is insufficient.
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 evaluates AI opportunities and cautions against high-risk autonomy. It distinguishes from siblings like 'assess_trust_control_risks' by focusing on opportunities rather than risks alone.
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?
No guidance is provided on when to use this tool versus siblings like 'analyze_business_context' or 'recommend_first_workflow'. The description lacks contextual cues for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
export_intake_packetExport intake packetCInspect
Export a structured public-safe markdown and JSON intake packet for deeper review.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| offer | No | ||
| aiIdea | No | ||
| teamSize | No | ||
| userNotes | No | ||
| goal90Days | No | ||
| constraints | No | ||
| currentGoal | No | ||
| riskSummary | No | ||
| businessType | No | ||
| revenueModel | No | ||
| riskConcerns | No | ||
| touchpointMap | No | ||
| currentProblem | No | ||
| targetCustomer | No | ||
| businessContext | No | ||
| currentWorkflow | No | ||
| bottleneckSummary | No | ||
| preferredNextStep | No | ||
| opportunitySummary | No | ||
| recommendedNextStep | No | ||
| recommendedWorkflow | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| confidence | Yes | |
| packetJson | Yes | |
| packetMarkdown | Yes | |
| missingInformation | Yes | |
| recommendedPrivateReview | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It mentions 'public-safe' but does not disclose side effects, auth requirements, or whether the packet is returned directly or saved. The description lacks behavioral details beyond the output format.
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 concise sentence, but it is too brief to provide adequate guidance. It front-loads the verb and resource, but offers no additional structure or detail.
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 high parameter count (22), nested objects, and lack of schema descriptions or annotations, the description is severely incomplete. It does not explain what the intake packet contains, how to populate the parameters, or what the output looks like beyond the format.
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 has 22 parameters with 0% description coverage, and the description adds no information about them. It does not explain what each parameter means, how to use them, or which are relevant, leaving the agent entirely dependent on parameter names.
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 action ('export'), the resource ('intake packet'), and the output format ('markdown and JSON'), with a purpose ('for deeper review'). This distinguishes it from sibling tools, which are analysis-oriented and do not export.
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?
No explicit guidance on when to use this tool versus alternatives. The phrase 'for deeper review' hints at external use, but there is no mention of prerequisites, when not to use, or comparison with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_mini_reportGenerate mini reportCInspect
Generate a concise public-safe mini business system review with bottlenecks, opportunities, risks, first workflow, next step, and note.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| offer | No | ||
| risks | No | ||
| aiIdea | No | ||
| nextStep | No | ||
| teamSize | No | ||
| confidence | No | ||
| goal90Days | No | ||
| bottlenecks | No | ||
| constraints | No | ||
| currentGoal | No | ||
| businessType | No | ||
| revenueModel | No | ||
| riskConcerns | No | ||
| opportunities | No | ||
| currentProblem | No | ||
| targetCustomer | No | ||
| currentWorkflow | No | ||
| businessSnapshot | No | ||
| preferredNextStep | No | ||
| recommendedWorkflow | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| confidence | Yes | |
| disclaimer | Yes | |
| shortSummary | Yes | |
| reportMarkdown | Yes | |
| recommendedAction | Yes | |
| missingInformation | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, permissions, or defaults. The term 'public-safe' is vague. The output content is described, but execution behavior remains opaque.
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 efficiently communicates the tool's purpose and output components. It could be slightly improved by separating the output list, but remains concise.
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 21 parameters with no schema descriptions and no required fields, the description fails to provide sufficient context for agent usage. The existence of an output schema mitigates return value explanation, but the input structure and usage scenario are inadequately covered.
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 0% for 21 parameters. The description mentions a few parameter names (bottlenecks, opportunities, etc.) in the output context, but does not link them to input semantics or clarify the role of many other parameters.
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 generates a 'concise public-safe mini business system review' with listed components. The verb 'generate' and object are specific, but it does not explicitly differentiate from sibling tools that cover individual components.
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?
No guidance on when to use this tool vs. the sibling tools that perform individual analyses (e.g., identify_bottlenecks, recommend_first_workflow). The description lacks context on prerequisites or when this composite tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
identify_bottlenecksIdentify bottlenecksCInspect
Identify likely revenue, operations, customer experience, and trust/control bottlenecks from the supplied context.
| Name | Required | Description | Default |
|---|---|---|---|
| aiIdea | No | ||
| metrics | No | ||
| businessType | No | ||
| riskConcerns | No | ||
| currentProblem | No | ||
| teamPainPoints | No | ||
| businessContext | No | ||
| currentWorkflow | No | ||
| customerComplaints | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| confidence | Yes | |
| bottleneckSummary | Yes | |
| missingInformation | Yes | |
| revenueBottlenecks | Yes | |
| mostLikelyRootCause | Yes | |
| operationalBottlenecks | Yes | |
| trustControlBottlenecks | Yes | |
| customerExperienceBottlenecks | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits, but it only states 'identify' without indicating whether the tool is read-only, modifies data, or has side effects; agent cannot assess safety or permissions.
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, concise but lacking structure; no bullet points or separation of key information, though it is not overly verbose.
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 tool with 9 unannotated parameters and an output schema, the description is insufficient; it fails to explain what each parameter contributes or how the output can be used, leaving significant gaps for an agent.
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 description provides no explanation for any of the 9 parameters (e.g., aiIdea, metrics, businessContext), and the schema has 0% description coverage, leaving the agent completely unaware of parameter meanings and usage.
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 identifies bottlenecks in specific areas (revenue, operations, customer experience, trust/control) from supplied context, distinguishing it from sibling tools like assess_trust_control_risks or evaluate_ai_opportunities, but lacks specificity on what 'supplied context' refers to.
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?
No guidance on when to use this tool versus alternatives; no mention of prerequisites or when not to use it, leaving the agent to infer based on the name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
map_customer_touchpointsMap customer touchpointsCInspect
Map likely customer journey stages, trust moments, service recovery opportunities, and human review rules.
| Name | Required | Description | Default |
|---|---|---|---|
| aiIdea | No | ||
| touchpoints | No | ||
| businessType | No | ||
| riskConcerns | No | ||
| salesProcess | No | ||
| currentProblem | No | ||
| supportProcess | No | ||
| currentWorkflow | No | ||
| customerJourney | No | ||
| deliveryProcess | No | ||
| recoveryProcess | No | ||
| retentionProcess | No | ||
| onboardingProcess | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| confidence | Yes | |
| touchpoints | Yes | |
| humanCriticalAreas | Yes | |
| missingInformation | Yes | |
| automationSafeAreas | Yes | |
| trustSensitiveMoments | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lists outputs but does not disclose behavioral traits such as whether it reads data, mutates state, or requires authentication. With no annotations, the description carries the full burden, which it fails to meet.
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 lists key outputs, making it concise. It is front-loaded with the verb 'Map'. However, it could be more structured by grouping related outputs.
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 tool has 13 parameters with no schema descriptions and no annotations, the description is too brief to provide complete guidance. It does not explain how to use the parameters or what the output schema contains.
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 0%, and the description does not mention any of the 13 parameters. It lists outputs but fails to link them to input parameters, providing no additional semantic value.
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 maps customer journey stages, trust moments, etc. The verb 'map' and the resource 'customer touchpoints' provide a clear purpose. However, it does not explicitly differentiate from siblings like 'assess_trust_control_risks' which might overlap in trust moments.
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?
No usage guidelines are provided. The description does not indicate when to use this tool over alternatives such as 'identify_bottlenecks' or 'recommend_first_workflow', nor does it specify prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_first_workflowRecommend first workflowBInspect
Recommend a narrow, measurable, human-reviewable first AI-assisted workflow with roles, review rule, escalation rule, and metrics.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| offer | No | ||
| risks | No | ||
| aiIdea | No | ||
| teamSize | No | ||
| goal90Days | No | ||
| bottlenecks | No | ||
| constraints | No | ||
| currentGoal | No | ||
| businessType | No | ||
| revenueModel | No | ||
| riskConcerns | No | ||
| opportunities | No | ||
| currentProblem | No | ||
| targetCustomer | No | ||
| currentWorkflow | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| aiRole | Yes | |
| humanRole | Yes | |
| confidence | Yes | |
| reviewRule | Yes | |
| escalationRule | Yes | |
| successMetrics | Yes | |
| expectedOutcome | Yes | |
| whyThisWorkflow | Yes | |
| workflowCategory | Yes | |
| missingInformation | Yes | |
| recommendedWorkflow | Yes | |
| firstImplementationScope | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It states 'recommend' which suggests non-destructive read, but lacks explicit statement about side effects, data usage, or permissions. Adequate but not thorough.
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?
Single sentence with no wasted words, but could benefit from listing key components or a brief note on input parameters. Still, it's efficiently brief.
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?
With 16 parameters and a rich output schema, the description should clarify how inputs affect the recommendation and what the output looks like. It does neither, leaving the agent underinformed.
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 0% for all 16 parameters, and the description does not explain any parameter. No guidance on how to fill fields like `currentGoal`, `businessType`, etc. Completely insufficient.
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 recommends a first AI-assisted workflow with specific attributes (narrow, measurable, human-reviewable) and components (roles, review rule, escalation rule, metrics). This specificity distinguishes it from sibling tools that analyze context or assess risks.
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?
No explicit guidance on when to use this tool versus alternatives like recommend_next_step. The description implies it's for first workflows but doesn't define conditions, prerequisites, or when to avoid it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_next_stepRecommend next stepCInspect
Recommend a neutral next-step category without pricing or hardcoded sales offers.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| offer | No | ||
| aiIdea | No | ||
| teamSize | No | ||
| timeline | No | ||
| userGoal | No | ||
| readiness | No | ||
| riskLevel | No | ||
| goal90Days | No | ||
| preference | No | ||
| constraints | No | ||
| currentGoal | No | ||
| businessType | No | ||
| revenueModel | No | ||
| riskConcerns | No | ||
| currentProblem | No | ||
| targetCustomer | No | ||
| currentWorkflow | No | ||
| wantsDoneForYou | No | ||
| wantsSelfGuided | No | ||
| problemComplexity | No | ||
| hasExistingAutomation | No | ||
| implementationReadiness | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| reason | Yes | |
| confidence | Yes | |
| readinessLevel | Yes | |
| recommendedPath | Yes | |
| suggestedAction | Yes | |
| alternativePaths | Yes | |
| missingInformation | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only mentions 'neutral' and 'without pricing or hardcoded sales offers', but omits side effects, return behavior, or auth needs.
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 one short sentence, front-loaded with purpose, but lacks necessary detail for a tool with 23 parameters.
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?
Despite having an output schema, the tool's high complexity (23 parameters, no descriptions) is not addressed; the description does not provide enough context for effective use.
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 0% and the description does not explain any of the 23 parameters, leaving their meaning entirely implicit.
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 action 'Recommend' and the resource 'next-step category', specifies it is neutral and excludes pricing/sales offers, which distinguishes it from sibling tools like 'recommend_first_workflow'.
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?
No guidance is provided on when to use this tool vs alternatives; missing context for prerequisite conditions or when not to use.
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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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.
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