Sentinel Signal MCP

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by sentinelsignal

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.5
Disambiguation4/5
Tools are mostly distinct, but score_workflow and score_batch both deal with scoring and could be confused without careful reading of their descriptions. get_usage and get_limits are also related but easily differentiated.
Naming Consistency5/5
All tool names follow a consistent lowercase verb_noun pattern (get_, score_, submit_, list_, validate_). This makes the set highly predictable.
Tool Count5/5
With 8 tools, the server covers scoring (single and batch), validation, schema retrieval, workflow listing, feedback, usage, and limits. This is well-scoped for the API's purpose.
Completeness4/5
The tool surface covers the main lifecycle of workflow scoring: single/batch scoring, validation, schema, workflow list, feedback, and account info. A minor gap might be a tool to retrieve historical scoring results, but this is likely handled by the score responses themselves.
Average 3.5/5 across 8 of 8 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- Last stable release on March 7, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the endpoint and operation name, failing to mention auth requirements, rate limits, side effects (e.g., quota consumption), or response characteristics. The behavior is almost entirely opaque.
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 efficient sentence that front-loads the verb and resource, and includes the endpoint for quick identification. While it is concise, it is somewhat under-specified, but conciseness itself is handled well.
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 an output schema and a moderately complex input (nested objects, required workflow/payload), the description does not explain how the parameters relate, when this tool is preferred over siblings, or what makes it 'unified'. The information is insufficient for a 3-parameter tool with no parameter descriptions.
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 0% description coverage and the description adds no parameter information. The meaning of 'workflow', 'payload', and 'options' is completely unexplained, and the description does not compensate for this gap despite having three parameters, two of which are required.
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 ('Call') and the resource ('Sentinel Signal unified workflow scoring') with a specific endpoint (POST /v1/score). It is clear but does not explicitly differentiate from sibling tools like score_batch, so it lacks full sibling distinction.
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 such as score_batch or validate_workflow_payload. There is no mention of prerequisites, typical use cases, or exclusions, leaving the agent to infer when to invoke it.
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?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'sequentially' and the batch limit, but does not disclose side effects, error handling, rate limits, or the meaning of the continue_on_error parameter. This leaves the safety profile and failure behavior unclear.
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 sentence that front-loads the action and a key constraint (limit of 25). It contains no unnecessary words and is appropriately sized for a simple tool.
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?
Even with an output schema present, the description omits critical context: what each workflow item should contain, how errors are handled, and the effect of continue_on_error. For a tool with two parameters and no schema descriptions, this level of detail is insufficient for an agent to invoke it correctly.
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 explain the 'items' array structure or the 'continue_on_error' parameter. The item objects are only typed as additionalProperties: true, leaving the agent without any guidance on required fields or formats.
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's function ('Score up to 25 workflow items sequentially in one request') and includes the endpoint, which distinguishes it from the sibling tool score_workflow. The action and resource are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have up to 25 workflow items to score in a single request, but it does not explicitly contrast with score_workflow or state when to prefer one over the other. No alternatives or exclusions are mentioned, so guidance is only implicit.
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 states the HTTP method (GET), implying a read-only operation, but does not disclose error handling, authentication requirements, or the structure of the response beyond naming the contents (fields and example payloads). This is adequate but not rich.
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 sentence that leads with the action ('Fetch'), names the resource ('workflow'), and immediately provides the endpoint. Every word earns its place; there is no padding or repetition.
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 tool is simple (one required parameter, safe read operation) and an output schema exists, reducing the need to describe return formats. However, the description omits usage context and parameter semantics, leaving the agent without enough information to invoke it confidently in the right situations. It is minimally complete but has clear gaps.
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 and the description adds little detail about the 'workflow' parameter. It appears in the URL pattern ({workflow}) but the description does not clarify whether it is an ID, name, or path. No additional semantics are provided to compensate for the schema's lack of explanation.
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's function: fetching required fields, optional fields, and example payloads for a workflow. It includes the HTTP endpoint (GET /v1/workflows/{workflow}/schema), which makes the resource and action unambiguous. This distinguishes it from siblings like validate_workflow_payload or score_workflow.
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. It does not mention use cases such as inspecting a workflow before sending payloads or constructing requests, nor does it exclude scenarios like validation or scoring. The only context is the tool's own purpose, leaving the agent to infer applicability.
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?
With no annotations, the description must disclose behavioral traits. It only states the action 'submit' without any details about side effects, permissions, rate limits, or what happens after submission. This is insufficient for a tool that likely creates a record.
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 that includes the verb, resource, and endpoint. It is front-loaded and contains no unnecessary words.
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 tool is simple with one parameter and an output schema, so return values need not be explained. However, the feedback structure is completely unspecified, leaving a gap in how to construct the input. The description is adequate but not fully complete.
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 schema has one parameter 'feedback' which is an open object with additionalProperties true, and schema description coverage is 0%. The description only says 'structured feedback' without specifying any structure, making it hard for an agent to know what to send.
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 submits structured feedback for a prior scoring request, which is a specific verb+resource and context. This distinguishes it from sibling tools that handle scoring, usage, and workflow operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning 'prior scoring request', but it lacks explicit when-to-use guidance or alternatives. No exclusions or comparisons to siblings are provided, so the usage is only implied.
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?
Since no annotations are provided, the description carries the full burden. It mentions the HTTP GET method and 'current API key', which imply read-only behavior and authentication context. However, it does not disclose potential quirks like error handling or whether the month format is strictly validated, though the output schema may cover return values.
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, front-loaded sentence that includes the purpose, endpoint, and parameter format without any wasted words. It is highly concise and well-structured.
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 simple one-parameter read tool with an output schema present, the description adequately covers what the tool does and how to specify the month. It lacks usage guidelines, but the tool's simplicity and the presence of an output schema reduce the need for extensive additional context.
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 schema has zero description coverage, so the description's note that month is optional with a YYYY-MM format adds critical meaning beyond the raw schema, which only indicates string|null with a default of null. This gives the agent enough to construct a valid call.
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 returns usage for the current API key, with a specific verb and resource, and references the endpoint (GET /v1/usage). It is distinct from siblings like get_limits, which concerns limits rather than usage.
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 such as get_limits. There are no exclusions or context about typical usage scenarios, leaving the agent to infer from the name and description alone.
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?
With no annotations provided, the description carries the full burden for behavioral disclosure. It mentions the GET endpoint, which suggests read-only behavior, but does not explicitly state that the operation is safe, side-effect-free, or non-mutating. Minimal details about behavior beyond the return content are given.
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 that includes both the action and the endpoint. It is front-loaded with the purpose and contains no unnecessary words or jargon, making it easy to parse.
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 simplicity of the tool (no parameters) and the presence of an output schema, the description is largely complete. It states what is listed (workflows and model versions) and the endpoint. It does not elaborate on return format or caveats, but the output schema covers that, and the lack of annotations is not critical for such a simple list operation.
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 zero parameters, so the baseline is 4. The description does not need to explain parameter semantics, and it correctly adds no parameter-related information, focusing instead on the endpoint and return value.
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 uses a specific verb 'List' and clearly identifies the resource as 'supported workflows and current model versions', including the endpoint GET /v1/workflows. This distinctly separates it from sibling tools like score_workflow or get_workflow_schema, which focus on individual workflow operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for discovering available workflows and their model versions, but it does not explicitly state when to use it compared to alternatives or list any exclusions. There is no mention of when not to use it or references to sibling tools, leaving the usage context to be inferred.
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?
With no annotations provided, the description carries the burden of behavioral disclosure. It discloses the key behavior (no scoring call consumption) and mentions normalization, but it does not discuss side effects, error handling, or whether the operation is read-only. This is adequate but lacks depth.
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, compact sentence that starts with the primary action and includes the endpoint for technical precision. There is no redundancy or wasted words, making it highly efficient.
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?
Given the tool's simplicity (2 parameters, output schema present), the description covers the core purpose and the critical no-cost aspect. However, it lacks details on parameter semantics and error behavior, and with no annotations, an agent might not fully understand how to construct the payload. It is minimally viable but has clear gaps.
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%, so the description must compensate. It only vaguely references 'workflow payload' and does not explain the 'workflow' parameter (string identifier) or the expected structure of 'payload' (an object with additionalProperties). The description adds minimal parameter-level meaning beyond the schema's names and types.
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's function with a specific verb ('Validate and normalize'), identifies the target resource ('workflow payload'), and explicitly differentiates it from scoring calls. The mention of the HTTP endpoint adds specificity, and this distinguishes it from sibling tools like score_workflow.
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?
The description provides clear usage context by emphasizing that validation does not consume a scoring call, implying this tool is for pre-flight checks. However, it does not explicitly name alternatives or state when not to use it, which prevents a perfect score.
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 scope (current API key) and the HTTP method, but does not mention potential side effects, error conditions, or authorization requirements beyond the API key. For a simple GET endpoint, this is adequate but limited.
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, front-loaded with the action and includes the endpoint for reference. No unnecessary words or redundant information.
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 the tool's simplicity (no parameters, output schema present), the description sufficiently covers the essential information: what is returned and for whom. The scope is clearly stated, and the output schema handles return value details.
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, so the description does not need to explain parameter meanings. The baseline for zero parameters is 4, and the description appropriately omits parameter details.
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 uses a specific verb 'Return' with a clear resource 'plan limits' and scope 'current API key', making the tool's function immediately obvious. The included REST endpoint reinforces the purpose and distinguishes it from sibling tools like get_usage, which focuses on usage rather than limits.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives like get_usage. The use case is implied by the name and description, but no exclusions or alternative tool suggestions are mentioned.
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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