BizVerify MCP Server

Official

by BizVerify

Overview Schema Related Servers Score Discussions

TypeScript

Hybrid

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: configuration, jurisdiction listing, account info, exact verification, fuzzy search, async job polling, cached entity retrieval, entity history, and credit purchasing. No overlapping functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (get_config, list_jurisdictions, verify_business, etc.) using lowercase with underscores. Verbs are descriptive and uniform.
Tool Count5/5
With 9 tools, the server is well-scoped for the business verification domain. Each tool serves a necessary function without bloat, covering configuration, discovery, verification, and account management.
Completeness5/5
The tool set provides full lifecycle coverage: discover jurisdictions, find entities, verify businesses (sync/async), cache retrieval, history, account info, and credit purchasing. No obvious gaps like missing update/delete operations, as the domain is query-focused.
Average 4.6/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=false, openWorldHint=true), description adds credit cost per jurisdiction and authentication needs. It clarifies the tool is discovery-oriented, which aligns with openWorldHint but doesn't contradict readOnlyHint (cost is a side effect).
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?
Three sentences, each serving a distinct purpose: purpose/sibling differentiation, usage condition, cost/auth. No fluff.
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?
While usage context is strong, the absence of output schema means description should ideally hint at return value structure (e.g., list of entities with key fields). It doesn't, leaving a completeness gap.
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?
Input schema already describes all 5 parameters (100% coverage). Description does not add parameter-specific meaning beyond what schema provides. Baseline score of 3 is appropriate.
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 discovers candidate businesses when the exact entity is unknown, framing it as a listing/discovery tool, not a verification tool. It distinguishes from sibling verify_business by explicitly naming it.
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?
Provides explicit when-to-use (browse/list, partial name) and when-not-to-use (specific company verification) with alternative sibling tool. Also mentions cost and authentication requirements.
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?
Annotations already declare readOnlyHint and idempotentHint. Description adds valuable behavioral info: costs 5 credits, requires authentication, and returns newest first. No contradictions.
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?
Two sentences: first states return value and ordering, second adds cost, auth, and usage advice. Extremely concise and front-loaded with key information.
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?
No output schema, but description specifies snapshot fields (timestamp, name, status). Could be slightly more explicit about structure, but is sufficient for agent understanding.
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?
Schema coverage is 100% with descriptions. Description adds meaning by stating 'newest first' ordering, which is not in schema. Does not detail each parameter but provides useful context.
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?
Clearly states the tool returns chronological verification snapshots with timestamp, name, status, newest first, with pagination. Explicitly differentiates from siblings by focusing on history vs. current state.
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?
Tells when to use it: 'to see how a company's status or details have changed over time.' Does not explicitly mention when not to use, but the sibling tools provide context for alternatives.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that the tool is 'Free and read-only' and 'requires authentication,' which provides some additional context beyond the annotations. However, it does not elaborate on other behavioral aspects like rate limits or data freshness.
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 three sentences long, efficient, and front-loaded. It lists return fields first, then adds context about cost and authentication, and ends with a practical usage tip. No wasted words.
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?
Despite lacking an output schema, the description comprehensively lists all return fields (email, verification status, plan, credit balance, member-since date, API keys). It also covers usage context and auth requirements, making it fully informative for an 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?
The tool has no parameters, so the description cannot add parameter-specific details. According to the rubric, zero parameters yields a baseline of 4. The description does not need to explain parameter semantics.
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 explicitly states that the tool returns 'BizVerify account summary' and enumerates the specific fields (email, verification status, plan, credit balance, member-since date, API keys). This distinguishes it from sibling tools like verify_business or get_config, which 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using the tool to 'check your remaining credit balance before running paid verifications,' providing clear context for when to invoke it. It also notes that it is 'Free and read-only,' implicitly guiding against using it for paid or write operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds significant behavioral context beyond annotations: the async nature (not immediate charge/credit), the Stripe checkout session creation, automatic credit addition after payment, and authentication requirement. No contradiction with annotations.
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?
Two sentences with no filler. Front-loaded with the core purpose, then clarifies key behaviors. Every sentence is necessary and informative.
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?
For a tool with one parameter and no output schema, the description covers authentication, workflow stages, and price reference. Annotations provide safety profile. Complete for selection and 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?
Schema coverage is 100% with a well-documented enum parameter. The description lists the package options and references get_config for prices, which adds minor value. Baseline 3 is appropriate as the schema already fully describes the parameter.
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: 'Starts a credit purchase: creates a Stripe checkout session and returns a payment URL.' It specifies the verb (starts), the resource (credit purchase), and the output (payment URL). It distinguishes from siblings by being the only purchase tool among retrieval/verification 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?
The description explains the workflow: does not charge immediately, credits added after payment, requires authentication. It does not explicitly contrast with alternative tools, but the context of purchasing credits vs. other retrieval tools is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses important behavioral traits beyond annotations: requires authentication, credit deduction even if business not found (no-match is a result), refunds for failed calls, and available tiers. No contradiction with annotations (readOnlyHint=false, etc.).
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 front-loaded with the primary purpose and usage guidance. It is thorough but slightly verbose; some details like deep availability and credits could be condensed. Still well-structured and each sentence adds value.
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 complexity (6 parameters, no output schema), the description covers all essential aspects: behavior, tiers, credits, refunds, authentication, and return information for quick vs deep. This provides sufficient context for an AI agent to use the tool correctly.
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 baseline 3. The description adds context about the 'level' parameter's credit costs and suboptions (force_refresh with deep), but doesn't significantly expand on what the schema already provides for other parameters.
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 purpose: 'Confirm a specific, named business in one jurisdiction — the PRIMARY tool whenever the user wants to verify, check, confirm, or look up a company's existence, status, good standing, or details.' It also distinguishes from the sibling tool search_entities by explicitly stating not to fall back to it.
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?
Provides explicit guidance: 'If the user has verification intent but has not given the exact company name, ASK them for the name and use THIS tool — do NOT fall back to search_entities.' Also explains the two tiers (quick vs deep) and when deep is available, giving clear context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent, open-world. Description adds that it's free, requires auth, and explains return states, adding value beyond annotations.
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?
Two sentences, no waste. Purpose first, then details. Every sentence earns its place.
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?
Single parameter fully covered, annotations present, description explains return behavior. No gaps for agent to infer.
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?
Schema coverage is 100%. Description adds context by specifying job_id comes from verify_business async response, enhancing meaning.
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?
Specific verb 'poll' with resource 'long-running verification job' and clear differentiator from siblings like verify_business. States it returns result, failure, or status.
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?
Explicitly states when to use (after verify_business returns job_id) and how to poll. No explicit when-not, but context is sufficient.
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?
Annotations already indicate idempotent and readOnly hints. The description adds that the operation is free and requires no authentication, providing useful context beyond annotations. No behavioral contradictions.
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?
Two sentences efficiently convey purpose, contents, prerequisites, and usage advice. No redundant information; every part adds value.
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 no output schema, the description comprehensively explains what the tool returns, including specific items like jurisdictions, costs, credits, and links. It also covers ordering (call first), prerequisites (none), and integrates well with the sibling tool 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?
No parameters exist, and schema coverage is 100%. The description does not need to elaborate on inputs; it correctly focuses on the output contents and usage context.
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?
Clearly states the tool returns BizVerify's public configuration as readable text, listing specific contents (jurisdictions, costs, credits, etc.). Distinguishes from siblings like list_jurisdictions (which returns only jurisdictions) and verify_business (which performs verification).
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?
Explicitly advises to call this tool first to discover supported jurisdictions and operation costs before verifying, and notes it is free and requires no authentication. This provides clear when-to-use guidance and sets expectations for subsequent operations.
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?
Annotations already declare readOnlyHint and idempotentHint. Description adds context: free, no live fetch, requires auth, and lists returned fields. No contradictions.
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?
Two well-structured sentences: first covers purpose and returns, second covers usage constraints and alternatives. No wasted words.
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?
For a simple tool with one param and no output schema, the description covers input source, cache behavior, required fields, and authentication. Differentiates from siblings.
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?
Only one parameter, 100% schema coverage. Description adds value by explaining the source of entity_id (prior verify_business or search_entities call), beyond the schema description.
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 it fetches a cached entity by ID, distinguishing it from verify_business (live) and search_entities (ID retrieval).
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?
Explicitly states when to use (with prior entity_id) and when not to (for live data, use verify_business with force_refresh). Also mentions it's free and read-only.
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?
Annotations already declare readOnlyHint and idempotentHint, so safety is clear. Description adds valuable context: 'Free and requires no authentication', which annotations don't cover. No contradictions.
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?
Two concise sentences. First sentence front-loads the action and output details. Second sentence gives usage guidance. No fluff.
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?
Despite no output schema, the description fully explains what is returned (code, status, capabilities). This is sufficient for a simple listing tool.
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 correctly omits parameter details. Schema coverage is 100% (trivial). Baseline 4 is appropriate for a zero-parameter tool.
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 jurisdictions with specific attributes (code, status, capabilities). It distinguishes itself from sibling tools like verify_business and search_entities by describing what it offers.
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?
Explicitly tells when to use: before calling verify_business or search_entities, to confirm support and verification tiers. This provides clear context and alternatives.
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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