B2Brilliant MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct resource and action: user business vs target business, discovery vs refinement, compatibility checking, and campaign creation vs refinement. The user/target and action distinctions are clearly separated.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case. The naming clearly separates 'user_business' from 'business' and pairs discover/refine, and create/refine for campaigns.
Tool Count5/5
Seven tools is well-scoped for a business compatibility and campaign creation workflow. Each tool serves a distinct step in the process without redundancy or bloat.
Completeness4/5
The tool set covers the core workflow: discover and refine both businesses, check compatibility, and create/refine campaigns. Minor gaps exist such as no campaign listing or deletion, but these are not critical for the stated purpose.
Average 3.1/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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?
With no annotations, the description must disclose behavioral traits. It only says 'assess compatibility' without clarifying whether this is a read-only analysis, what side effects exist, or what the output format is. The description does not add meaningful behavioral detail beyond the action itself.
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 directly states the purpose. It is concise with no unnecessary words or repetition, making it easy to parse.
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 nested objects and no output schema, the description should explain what 'compatibility' means and what the return value is. It provides none of this, leaving the agent without a clear understanding of the tool's behavior or results. The description is too minimal for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, as each parameter has a description ('Your business object', 'Target business object'), though these are minimal. The tool description adds no additional semantic detail beyond what the schema provides, so it meets the baseline but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Assess') and resource ('compatibility between your business and a target business'). It is distinct from sibling tools (discover/refine business, create_campaign), which focus on different actions, so the purpose is clear and differentiated, though it does not explicitly name alternatives.
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. It does not mention prerequisites, typical use cases, or exclusions. The description simply states what the tool does, leaving the agent to infer when it is 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 are provided, so the description carries the full burden of behavioral disclosure. It does not state whether the tool is read-only, whether it fetches external data, what side effects exist, or what the output will be. The options (deepSearch, findBranding, findCompetitors) hint at behavior but are not explained in the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundant information. It efficiently communicates the core purpose, though it could be slightly more specific without adding much 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?
The tool performs a potentially complex task (discovering and analyzing businesses from URLs) but the description gives no indication of expected outputs, prerequisites, or limitations. With no output schema and no annotations, the one-line description is insufficient for an agent to fully understand the tool's capabilities or invocation results.
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 input schema provides complete descriptions for all parameters (100% coverage), so the baseline is 3. The description adds context that the URLs are for a 'target business', but otherwise does not enrich the parameter meaning beyond what the schema already states.
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 uses specific verbs ('Discover and analyze') and identifies the resource ('a target business from website URLs'), making its primary function clear. However, it does not explicitly differentiate from sibling tools like discover_user_business, though the phrasing implies a general target rather than a user-specific one.
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?
There is no guidance on when to use this tool versus alternatives. No mention of when to choose discover_business over discover_user_business or refine_business, nor any exclusions or prerequisites.
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 of behavioral disclosure. It only says 'refine', which implies a mutation, but gives no details about side effects, idempotency, whether the original business object is modified, or what the response will be. This is a significant gap for a write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundant words. It effectively communicates the tool's action without over-explaining, earning the highest score for efficiency.
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's mutation nature, no output schema, and no annotations, the description is incomplete. It does not explain the workflow context (e.g., after discover_business), what 'refine' means in terms of output, or how the feedback is applied. The minimal information leaves the agent to infer too much.
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 documents both parameters with descriptions, and the parameter descriptions add context beyond the schema: 'business' references discover_business, establishing a clear data source, and 'feedback' explains its purpose. This cross-tool reference is useful, though it does not detail the expected structure of the business object or feedback format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'refine' on 'target business information' with feedback as the input, clearly stating what the tool does. However, it does not distinguish itself from the sibling tool 'refine_user_business', which likely has a very similar purpose.
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 like refine_user_business or discover_business. The description implies a workflow of refining an existing business object with feedback, but does not state prerequisites, exclusions, or fallback tools.
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 the full burden. It only says 'refine' and 'based on feedback' without disclosing whether the original campaign is modified, what is returned, or any 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single seven-word sentence with no filler, making it very concise. However, it may be too terse for a tool with four nested objects, so it loses a point for appropriateness of size.
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?
With four required parameters, no output schema, and no annotations, the description is too sparse to give an agent enough context about the workflow, return values, or prerequisites. It mentions feedback and campaigns but omits essential operational details.
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 provides 100% coverage with clear descriptions for all four parameters (userBusiness, targetBusiness, campaign, feedback), so the baseline is met. The description itself adds no additional parameter-level meaning beyond mentioning feedback.
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 uses a specific verb ('refine') and resource ('marketing campaigns'), clearly distinguishing it from sibling tools like create_campaign and refine_business. However, it does not explicitly differentiate its scope from alternatives, so it stops short of a perfect score.
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 phrase 'based on feedback' implies the tool is used when feedback exists to improve a campaign, but there is no explicit guidance on when to use it versus create_campaign or when not to use 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 disclosing side effects, permissions, or return behavior. It only says 'create' without explaining what happens on success, whether it mutates data, or what output to expect.
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 is front-loaded with the action and resource. It contains no redundant words or filler, making it easy to parse quickly.
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 three parameters, two nested objects, no output schema, and no annotations, the description offers minimal context. It does not explain the relationship between userBusiness and targetBusiness, what the campaign types entail, or what happens after creation, leaving the agent under-informed.
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 input schema already describes all three parameters with 100% coverage, including the default behavior of campaignTypes. The description adds no extra semantic detail beyond the schema, so the baseline of 3 applies.
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 action (create), the resource (marketing campaigns), and the target (a target business). It distinguishes itself from sibling tools like discover_business and refine_campaign, which imply different 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?
The description provides no guidance on when to use this tool versus alternatives such as check_compatibility or refine_campaign. It does not mention prerequisites, sequencing, or conditions under which this tool is 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?
With no annotations, the description carries full responsibility for behavioral disclosure. It does not reveal what refining entails, whether it modifies the existing business object, what the output looks like, or any side effects. The terse wording lacks critical behavioral context.
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 with no filler or redundancy. It efficiently communicates the core purpose, though at the cost of depth, which is the appropriate trade-off for this terse format.
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?
For a tool with two parameters and no output schema, the description does not explain the refinement process, return value, or dependencies on discover_user_business. The context is too sparse for an agent to predict the tool's behavior or integrate it effectively alongside sibling tools.
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 input schema already provides full descriptions for both parameters (business object from discover_user_business, feedback string). The description adds only the phrase 'based on feedback', which mirrors the schema, so no meaningful additional semantic value is provided beyond the structured fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('refine') on a specific resource ('your business information') motivated by 'feedback'. It distinguishes from sibling 'refine_business' by adding the qualifier 'your' and the feedback trigger, though it doesn't explicitly contrast with the generic sibling.
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 phrase 'based on feedback' implies usage after receiving feedback, but the description gives no explicit guidance on when to use this versus the sibling 'refine_business' or 'discover_user_business'. No exclusions or alternatives are mentioned.
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, the description carries the burden of behavioral disclosure. 'Discover and analyze' implies a read-only analysis without side effects, which is reasonably transparent for a discovery tool. However, it does not mention potential limitations such as network dependency, result size, or handling of invalid URLs, leaving some behavioral ambiguity.
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 conveys the core purpose without any filler. Every word serves a functional role, making it highly concise and 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?
The tool has a nested options object and no output schema, so the description should clarify what 'analyze' returns. The description is vague about the output shape or content, and does not mention the optional behaviors like deep search or competitor discovery. However, the schema covers options, and the core purpose is understandable, so it is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters and options. The description adds no additional semantic depth beyond mentioning 'website URLs', which maps directly to the 'urls' parameter. This matches the baseline for full schema coverage.
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 uses specific verbs 'Discover and analyze' with a clear resource 'your business information' and source 'website URLs'. It clearly indicates what the tool does, but does not explicitly distinguish it from the sibling tool 'discover_business', relying on the 'user' in the name and 'your' in the description to imply a difference.
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 intended use case is implied: when you have website URLs and want to discover business information. However, there is no explicit guidance on when to use this tool instead of alternatives like 'discover_business' or 'refine_user_business', nor any exclusions or prerequisites.
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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