cxone-wfm-intraday-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: snapshot, forecast comparison, risk analysis, capacity query, simulation, recommendation, data loading/reset, entity resolution, metrics listing. No overlapping functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_available_capacity, simulate_recovery_action, reset_intraday_data). No mixing of conventions.
Tool Count5/5
10 tools is well-scoped for a WFM intraday server. Each tool serves a needed function without being excessive or too sparse.
Completeness4/5
Covers snapshot, forecast comparison, risk analysis, capacity, simulation, recommendation, and data management. Minor gap: no direct tool for updating individual queue attributes, but load_intraday_data allows bulk replacement.
Average 3.3/5 across 10 of 10 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 21 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as permissions, side effects, rate limits, or whether the operation is read-only. It only states the purpose, leaving the agent uninformed about important behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but not structured. It does not waste words but also fails to provide any supplementary information beyond the core purpose.
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 absence of an output schema and annotations, the description is incomplete. It does not explain what the tool returns (e.g., list of queue names, capacity metrics) or how to interpret the results, which is essential for effective 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?
The schema has 0% description coverage for the parameter 'target_queue', and the tool description adds no additional meaning or format guidance. The agent receives no clarity on what values are valid or what 'target_queue' refers to.
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 'identify' and the resource 'queues with spare capacity', with the specific purpose of helping recover the target queue. It distinguishes itself from siblings like 'get_forecast_vs_actual' or 'simulate_recovery_action', 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 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, nor does it mention any conditions or constraints. There is no explicit advice on when to avoid using it or which sibling might be more appropriate.
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 provided; description carries full burden. It does not disclose whether the tool is read-only, modifies state, or requires authentication. The mention of 'recovery recommendation' implies computation but side effects are unclear.
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?
Single sentence, no fluff, front-loaded with key info. Could be slightly expanded to include parameter hints without losing conciseness.
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?
Description lists output components partially compensating for missing output schema. However, no parameter info and limited behavioral context leave gaps for a complete understanding.
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 sole parameter 'target_queue' is not described in either the schema or the tool description. With 0% schema description coverage, the agent has no guidance on what value to provide or its 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?
Description clearly specifies verb 'Return' and resource 'plain-English recovery recommendation' with components (what/why/action/impact/trade-offs). Does not explicitly distinguish from sibling tools like 'simulate_recovery_action', but purpose is sufficiently clear.
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 (e.g., after simulation or for reporting). No context about prerequisites or expected input state.
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 provided, and the description fails to disclose any behavioral traits such as read-only status, authentication needs, or side effects. The description is minimal and does not add context beyond the obvious function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it lacks structure. It is too brief to provide sufficient information, making it less effective despite being short.
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 no output schema, no annotations, and a minimal description, the tool definition is incomplete. It does not explain return values, interval details, or edge cases, leaving the AI agent with insufficient context to use the tool correctly.
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?
Schema coverage is 0%, meaning no descriptions for parameters. The description does not explain the 'intervals' parameter (e.g., format, meaning) or the 'queue' parameter's expected format. It only loosely implies queue is 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 verb 'Return' and the resource 'per-interval forecast vs actual volume, AHT, and staffing for a queue'. It distinguishes from sibling tools like get_available_capacity by focusing on forecast vs actual comparisons.
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 like list_intraday_metrics or get_available_capacity. The description does not mention prerequisites or usage context.
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 present, so the description must disclose behaviors. It fails to mention side effects, permissions, or limitations. For a read analysis, it should indicate no destructive actions, but does not.
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 sentence, efficient and front-loaded. However, it could add a bit more detail without becoming verbose, such as indicating the output structure.
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 tool has one parameter, no output schema, and no annotations, the description is too minimal. It lacks details on return values, prerequisites, or how the risk drivers are derived, making it incomplete for reliable agent use.
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?
Schema description coverage is 0%. The only parameter 'queue' is mentioned in the description but without additional meaning (e.g., format, example, or constraints). The description does not compensate for the lack of schema documentation.
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 'explain' and the resource 'queue', with an outcome 'ranked risk drivers with supporting data'. It distinguishes itself from sibling tools like 'get_intraday_snapshot' by focusing on risk explanation.
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 (e.g., 'get_intraday_snapshot'). There is no mention of context or exclusions, leaving the agent to infer usage.
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 must fully disclose behavior. It describes a read operation ('return') but does not mention whether it is read-only, any side effects, permissions needed, or data freshness guarantees.
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 sentence that conveys the core functionality without superfluous words. It is efficient, though slightly more detail could improve clarity without adding length.
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 1 parameter, no output schema, and a vague description, the tool definition leaves out critical details: what constitutes 'state', how 'highest-risk' is determined, and what the return format looks like. This incompleteness hampers correct agent invocation.
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?
The schema has 0% description coverage for the single parameter 'queues'. The description adds meaning by stating 'for all (or selected) queues', which clarifies its purpose, though it does not specify the format of queue identifiers or the effect of null.
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 it returns 'current Intraday state' for queues and the highest-risk queue, with a specific verb and resource. It distinguishes itself from sibling tools like 'get_intraday_risk_drivers' by focusing on a snapshot of state rather than risk details.
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 does not mention prerequisites, exclusions, or context for selecting 'all or selected queues'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description lacks any behavioral details such as side effects, permissions, rate limits, or reliability. For a listing tool, even basic read-only hints are absent, making it opaque.
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, efficient sentence that conveys the core function without extraneous words. Perfectly concise.
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 no parameters, the tool lacks an output schema, and the description only vaguely indicates the output contains definitions and units. The agent lacks sufficient context about the return format, which is critical for further processing.
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 tool has no parameters, and schema coverage is 100%, so the description cannot add parameter semantics. The baseline for zero parameters is 4; the description mentions 'definitions and units,' which is about output, not parameters, so no deduction.
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 available intraday metrics with definitions and units. The verb 'List' and the resource 'Intraday metrics' are specific, and it distinguishes itself from sibling tools which are more about data retrieval or actions.
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 usage guidance is provided. The description does not indicate when to use this tool versus alternatives like get_available_capacity or get_intraday_snapshot, leaving the agent without context for optimal selection.
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 full responsibility. It does not mention any behavioral traits such as side effects, permissions needed, error handling, or what happens if the name cannot be resolved.
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 wasted words. It front-loads the action and includes helpful examples.
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?
For a simple one-parameter tool with no output schema, the description covers the basic purpose. However, it lacks details on possible name formats, error cases, or what a 'canonical queue' represents, leaving some ambiguity for the agent.
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?
Although schema coverage is 0%, the description adds significant meaning to the 'name' parameter by explaining it is a 'user-friendly name' and the tool resolves it to a 'canonical queue'. This clarifies the parameter's purpose beyond the bare 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?
The description clearly states the verb 'resolve' and the resource: converting a 'user-friendly name' to a 'canonical queue'. The examples ('billing', 'GE') aid understanding. However, it does not differentiate from sibling tools, though siblings appear to serve 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 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. Sibling tools are listed but no context is given about their use cases or when to prefer one over another.
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?
Given no annotations, the description must fully disclose behavioral traits. It states 'simulate' implying no actual state change, but it does not explicitly confirm read-only behavior, mention any side effects, or describe the output format. Lack of annotation makes this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence of 13 words that immediately conveys the core action. Every word serves a purpose with no redundancy or extraneous detail.
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?
The description provides the essential idea for a straightforward simulation tool. However, it omits what the simulation returns (e.g., predicted metrics) and any conditions under which the simulation is valid. Given the lack of output schema, this information is necessary for complete understanding.
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 adds meaning by mapping 'N agents' to the agents parameter, 'source queue' and 'target queue' to source and target, and 'duration (minutes)' to minutes. However, it does not specify valid values or constraints such as queue name format or integer ranges.
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 simulates moving a number of agents from one queue to another for a specified duration. This verb+resource combination is distinct from sibling tools which focus on capacity, forecasts, and intraday metrics.
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 instructions are provided on when to use this tool versus alternatives such as get_available_capacity or get_intraday_snapshot. The description does not specify prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 discloses the action of restoring demo data but does not mention potential destructive side effects (e.g., overwriting custom data) or prerequisites.
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?
Single sentence, efficient, front-loaded with key action. 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?
For a parameterless tool with no output schema and no annotations, the description provides sufficient context (purpose and relation to sibling). Could mention what happens to existing data, but overall adequate.
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?
No parameters exist, so the description adds value by explaining the tool's effect beyond the empty schema. Baseline for zero parameters is 4, and the description meets it.
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 restores the built-in demo Intraday dataset and explicitly positions itself as the undo operation for load_intraday_data, distinguishing it from sibling 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?
Description implies usage as a revert action after load_intraday_data, providing clear context. However, it does not explicitly state when not to use or mention alternative approaches.
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?
Since no annotations are present, the description carries the full burden. It discloses that the tool 'sets the dataset for the whole server process, not per-conversation' and mentions the restorative sibling, which is good. However, it could explicitly state that it overwrites existing data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat lengthy due to the detailed parameter specification, which is necessary. However, it is front-loaded with the main purpose and well-structured. Still, it could be more concise by moving parameter details to a structured example.
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 that there is no output schema and the input is complex, the description covers all necessary context: what the tool does, how it affects other tools, how to restore the default, and the exact structure of the input data. No additional explanations are needed for an agent to use this tool 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 input schema has 0% description coverage for properties, but the description provides a comprehensive specification: required fields, optional fields, data types (list of strings, objects), and constraints (percentages 0-100, times in seconds, staffing counts). This fully compensates for the schema's lack of detail.
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 'Replace the Intraday dataset with the user's own queues', using a specific verb and resource. It also distinguishes itself from sibling tools like reset_intraday_data by explaining that this loads custom data while that restores demo data.
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 says when to use this tool: 'so every other tool analyzes THEIR data instead of the built-in demo'. It also provides an alternative: 'Call reset_intraday_data to restore the demo dataset', giving clear when-not guidance.
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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