DataForB2B MCP Server

Official

by DataForB2B

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource type (people, companies, posts, jobs) or a distinct action (search vs. enrich). The descriptions clearly differentiate search_people from enrich_profile and search_company from enrich_company, with no overlapping purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: search_* for discovery and enrich_* for data augmentation. This predictable structure makes it easy to infer function from the name alone.
Tool Count5/5
With 6 tools, the set is well-scoped for a B2B data platform. Each tool covers a distinct data operation (search people, companies, posts, jobs; enrich profiles, companies) without redundancy or bloat.
Completeness5/5
The tool surface covers the full expected lifecycle for a B2B data provider: discovery via search across all major entity types, and enrichment for deeper data on individuals and companies. No obvious dead ends or missing critical operations like retrieving by ID or exporting results.
Average 4.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context by specifying the data comes from public sources and enumerates fields (description, industry, headquarters, employee count, funding, offices), which helps set expectations about what will be returned. 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?
The description is a single, front-loaded sentence that clearly states the action and resource, followed by a parenthetical list of examples. Every word adds value and there is zero wasted text.
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?
The tool has one parameter with a fully descriptive schema and an output schema, so the description doesn't need to explain return values. It clearly communicates the single-company scope and the breadth of enrichment data. A slightly more explicit note about alternatives or limitations would make it complete, but it is already strong.
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%, and the schema already thoroughly describes the company_identifier parameter, including accepted formats. The description does not add additional parameter semantics beyond what the schema provides, matching the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb ('enrich') with a clear resource ('a single company') and specifies the type of data ('full data from public sources...'). This distinguishes it from siblings like search_company (which likely searches/finds companies) and enrich_profile (which enriches a person).
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 a specific company identifier and need comprehensive enrichment data. However, it does not explicitly compare to alternatives like search_company or state when not to use it. The context 'single company' gives some guidance but no exclusion criteria.
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, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds the hiring-signal use case and platform scope but does not disclose additional behavioral traits such as pagination behavior, rate limits, or platform-specific response differences. It neither contradicts annotations nor enriches them significantly.
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 two compact sentences with no filler. It front-loads the core action and resource, enumerates the main filter categories, and ends with a practical use-case sentence—each part 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?
Given the rich annotations (read-only, idempotent), a fully described 14-parameter schema, and the presence of an output schema, the description is sufficient for an agent to understand the tool's scope and value. The hiring-signal context adds orientation beyond what structured metadata provides, making the description complete for this complexity level.
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 for all 14 parameters, including explicit per-parameter explanations and enum values. The description's filter list (employment type, remote/onsite, seniority, freshness, employer) simply restates a subset of the schema terms without adding new meaning or usage guidance beyond what the schema already provides.
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 the specific verb 'Search' on 'job postings on LinkedIn or Indeed' and enumerates the key filter dimensions (keyword, location, employment type, remote/onsite, seniority, freshness, employer). It clearly distinguishes this tool from sibling tools like search_people, search_company, and search_posts by focusing on job postings and hiring signals.
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 useful context about when to use the tool ('Useful for hiring signals: which companies are recruiting for what roles, where') and indicates the platforms it supports. It does not explicitly mention when not to use it or name alternative tools, but the sibling list and the clear platform/resource scope make the intended use obvious.
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=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context beyond annotations: it returns paginated companies and that matching depends on the FilterGroup's op (ALL/ANY). This informs the agent about response shape and filter semantics without contradicting the 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?
The description is compact: three sentences, front-loaded with the core purpose. It avoids restating schema details and instead directs the agent to the schema for specifics. Every sentence contributes to either identifying the tool's purpose or guiding its usage.
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 complexity of nested filter groups and the richness of the input schema, the description sufficiently covers the tool's search semantics, pagination, and filter-group behavior. It leverages the schema's extensive field-level documentation rather than duplicating it. A minor gap is the lack of any mention of output schema or response fields, but the output schema is present and reduces the need for that in the description.
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%, so the baseline is 3. The description adds value by telling the agent to translate natural-language user intent into structured filters and by pointing to the filters parameter for available columns and operators. It also highlights the role of FilterGroup.op in combining conditions, which helps the agent construct valid filter objects.
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 opens with 'Search companies using structured filters,' using a specific verb and resource that clearly distinguishes it from sibling tools like search_people or search_posts. It further reinforces the scope with 'Use this for any company search,' making the tool's purpose unambiguous.
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 explicitly states 'Use this for any company search' and provides concrete guidance on translating user intent into structured filters. It does not explicitly name alternatives or exclusion cases, but the sibling tools operate on different entities, so the usage context is clear enough for an agent to select it appropriately.
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, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context beyond this: 'each reactor / commenter is a lead, with their profile' and that include attaches engagers, which is not evident from annotations alone.
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 paragraphs, front-loaded with the core action, followed by a clear list of modes. No filler; every sentence contributes to understanding purpose or usage.
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 tool with 17 parameters and an output schema, the description covers primary use cases (keyword search and single-post retrieval with leads). Platform-specific limitations like Reddit's comment-only engagers are in the schema. The description is sufficient for an agent to select and invoke the tool correctly.
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 description coverage is 100% for all 17 parameters, so the baseline is 3. The description adds high-level value by explaining the keyword vs post_url modes and the include/engager workflow, which helps an agent understand how parameters work together beyond individual schema entries.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Search social posts by keyword on LinkedIn, Twitter/X, or Reddit — or target a single post by URL' with specific verb+resource+scope. It distinguishes from sibling tools like search_people and search_company by focusing on posts and lead generation from engagers.
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?
Provides clear context: 'Great for intent signals' and explains two modes (keyword vs URL) and the include option. However, it does not explicitly name alternatives or exclusion criteria like 'use search_people for profiles,' so it stops short of a 5.
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 readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by stating that each enrichment flag controls one data source and bills separately, warning about cost implications and independent data sources. This is useful behavioral information not present in the structured fields.
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 two sentences with no fluff. The first sentence explains what the tool does, and the second provides an important billing/behavioral note. Every word earns its place, and it is front-loaded with the core purpose.
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 rich schema (100% parameter coverage), the presence of an output schema, and comprehensive annotations (readOnly, idempotent, openWorld), the description is complete. It covers the core purpose, the single-profile scope, and the billing behavior. Nothing critical is omitted.
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 description coverage is 100%, so parameter semantics are fully documented in the schema. The description adds extra meaning by clarifying that each enrichment flag maps to a distinct data source and incurs separate billing, which is not evident from individual parameter descriptions. It also reinforces that the profile_identifier is the entry point.
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 enriches a single professional profile with specific data types (detailed profile data, work email, personal email, GitHub profile). It uses a specific verb ('enrich') and resource ('single professional profile'), and the mention of 'single' differentiates it from search tools and sibling enrich_company.
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 clearly implies when to use: when you have a specific profile identifier and want to enrich it. The phrase 'single professional profile' provides clear context, and the sibling enrich_company makes the distinction obvious. However, it does not explicitly state exclusions or mention alternatives like search_people for discovery.
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?
The description discloses key behavioral traits: pagination of results, ALL/ANY matching based on FilterGroup `op`, and the risk of matching unrelated companies when using `current_company` by name. It also warns that `keyword` searching on a company trait can return unrelated people, adding significant context beyond the readOnly/openWorld annotations.
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 a one-line purpose, followed by logically separated paragraphs covering usage, pagination, and advanced filtering tips. While longer than a minimal description, each sentence carries practical weight for a tool with a complex nested filter schema, and the structure aids readability.
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 complex schema and rich output schema, the description covers the primary use cases, points to the schema for parameter details, and explains common pitfalls. It addresses pagination, group semantics, and specific intent translation (e.g., converting natural language to filters), making it complete for an agent to invoke correctly.
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 input schema already provides exhaustive detail on columns, operators, and value formats (100% coverage). The description adds strategic filter-construction semantics, such as using OR groups for same-column alternatives and choosing between `current_company_category` and `current_company_keyword` based on niche fit, which is not merely a restatement of the schema.
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 opens with 'Search professional profiles using structured filters,' clearly identifying the verb (search), resource (professional profiles), and method (structured filters). It distinguishes itself from sibling tools like search_company and search_posts by focusing exclusively on people search.
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?
It explicitly states 'Use this for any people search' and provides actionable guidance such as using `current_company_id` instead of `current_company` to avoid noise, and contrasting `keyword` (person headline) with `current_company_keyword` (employer). It also references sibling tools (search_company, enrich_company) for obtaining company IDs.
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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