B2Bhint MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: get_company_basic_data and get_company_full_data target different levels of company detail, while the three search tools each target different search criteria (email, company name, person name). The descriptions reinforce these distinctions, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case: get_company_basic_data, get_company_full_data, search_company_by_email, search_company_by_name, and search_person_by_name. The naming is predictable and readable throughout the set.
Tool Count5/5
With 5 tools, this server is well-scoped for its apparent domain of B2B company and person data retrieval. Each tool earns its place by covering distinct operations (basic/full data retrieval and multiple search types), avoiding bloat or thinness.
Completeness4/5
The tool set provides strong coverage for data retrieval and search in the B2B domain, with no dead ends. A minor gap exists in update/delete operations (e.g., no tools for modifying company data), but agents can likely work around this given the server's focus on querying.
Average 2.8/5 across 5 of 5 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 is passing
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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, so the description carries the full burden. It mentions returning a list but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or what happens with no matches. For a search tool with zero annotation coverage, this is a significant gap in transparency.
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 with no wasted words. It's front-loaded with the core action and result. However, it could be slightly more structured by explicitly naming key parameters or distinguishing from siblings, but it's concise overall.
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 annotations, no output schema, and a search tool with potential complexity (e.g., result format, error handling), the description is incomplete. It doesn't explain what the returned list contains, how results are ordered, or any limitations. For a tool with 2 parameters and siblings, more context is needed.
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 schema already documents both parameters (q and countryCode) well. The description adds minimal value beyond implying parameters exist, but doesn't provide additional semantics like search behavior or format details. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool searches for companies by parameters and returns a list, which clarifies the basic purpose. However, it's vague about what 'parameters' specifically means (only implied by the schema), and doesn't distinguish it from sibling tools like search_company_by_email or search_person_by_name. It's adequate but lacks specificity.
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 doesn't mention siblings like get_company_basic_data or get_company_full_data, nor does it explain when to use search_company_by_email instead. There's no context for exclusions or prerequisites, leaving the agent to guess based on tool names 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions returning a list but doesn't specify details like pagination, rate limits, authentication needs, or what happens with no matches. The description is minimal and lacks critical operational context for a search tool.
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, straightforward sentence with no wasted words. It's appropriately sized for a simple tool, though it could be more front-loaded with key details like the tool name or specific parameters to enhance clarity.
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 annotations, no output schema, and a search tool with two parameters, the description is incomplete. It doesn't explain return values, error handling, or behavioral traits, leaving gaps that could hinder an AI agent's ability to use the tool effectively.
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 schema already documents both parameters (q and countryCode) with examples. The description adds no additional meaning beyond what the schema provides, such as explaining query syntax or countryCode usage. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool searches for persons by parameters and returns a list, which clarifies the basic action. However, it's vague about what 'parameters' specifically means (only implied by the schema), and it doesn't differentiate from sibling tools like search_company_by_name, which perform similar searches but for different entities.
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. The description mentions 'parameters' but doesn't specify contexts, prerequisites, or exclusions. It doesn't reference sibling tools or clarify if this is the primary person-search method versus other potential options.
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 full burden but offers minimal behavioral context. It states this is a read operation ('return') but doesn't disclose authentication needs, rate limits, error conditions, or what 'other data' might include. The description is too vague about the tool's behavior beyond the basic retrieval claim.
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 that's reasonably concise but could be more front-loaded. It wastes words on redundant phrasing ('Get company details endpoint will return') and could more efficiently state the core functionality. The structure is adequate but not optimal.
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 annotations and no output schema, the description is insufficiently complete. It mentions 'contacts, financial reports and other data' but doesn't specify what format this data comes in, whether it's paginated, or what 'other data' includes. For a tool that presumably returns comprehensive company information, more detail about the response structure would be helpful.
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 100% schema description coverage, the baseline is 3. The description doesn't add any meaningful parameter semantics beyond what's already in the schema (internationalNumber and countryCode). It doesn't explain how these parameters relate to retrieving company data or provide context about their usage.
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 purpose: retrieving full company data including contacts, financial reports, and other data. It uses specific verbs ('Get', 'return') and identifies the resource ('company details'), but doesn't explicitly differentiate from its sibling 'get_company_basic_data' beyond implying 'full' vs 'basic' data.
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 'get_company_basic_data' for basic data or the search tools for finding companies. The description implies it returns comprehensive data but doesn't specify use cases 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 states that it 'will return all basic company data,' lacking details on permissions, rate limits, error handling, or response format. This is insufficient for a tool with two required parameters and no output schema.
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, straightforward sentence that efficiently conveys the core action. It's front-loaded with the main purpose, though it could be slightly more specific (e.g., clarifying 'basic' vs. 'full').
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 complexity (two required parameters, no output schema, and no annotations), the description is incomplete. It doesn't explain what 'basic company data' includes, how results are structured, or any behavioral traits like authentication needs. This leaves significant gaps for an AI agent to use the tool effectively.
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 schema fully documents the two parameters. The description adds no additional meaning beyond implying the tool fetches data based on these inputs, which aligns with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 purpose: 'return all basic company data on B2BHint.' It specifies the verb ('return') and resource ('company data'), though it doesn't explicitly differentiate from sibling tools like 'get_company_full_data' beyond the 'basic' qualifier.
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. The description mentions 'basic company data' but doesn't clarify what distinguishes it from 'get_company_full_data' or when to choose one over the other, nor does it mention prerequisites or exclusions.
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 states the tool returns 'a list of companies that match the selected email,' which implies a read-only, non-destructive operation, but doesn't cover aspects like error handling, rate limits, authentication needs, or what happens if no matches are found. The description is minimal and lacks critical behavioral details.
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, straightforward sentence: 'Search for a company by email will return a list of companies that match the selected email.' It is front-loaded with the core purpose and avoids unnecessary details. However, it could be slightly more polished (e.g., 'Searches for companies by email and returns a list of matches'), but it's efficient with zero waste.
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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It states the purpose and return type ('a list of companies'), but lacks context on usage guidelines, behavioral traits, or error handling. For a simple search tool, this might suffice, but gaps in transparency and guidelines prevent a higher score.
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 has 100% description coverage, with the 'email' parameter documented as 'Example value: rsing@tesla.com.' The description adds no additional meaning beyond what the schema provides, such as format requirements or search logic. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given the schema's completeness.
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 purpose: 'Search for a company by email will return a list of companies that match the selected email.' It specifies the verb (search), resource (company), and key parameter (email). However, it doesn't explicitly differentiate from sibling tools like 'search_company_by_name' beyond the parameter difference, which is implied but not stated.
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 doesn't mention sibling tools like 'search_company_by_name' or 'get_company_basic_data', nor does it specify scenarios where email-based search is preferred over name-based search or direct retrieval. Usage is implied by the description but not explicitly stated.
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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