Bullhorn CRM MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation3/5
Tool purposes are mostly distinct, but list_candidates/list_jobs overlap with search_entities since search_entities can also query candidates/jobs with Lucene syntax, creating potential confusion for an agent.
Naming Consistency4/5
Names follow a verb_noun pattern (get_, list_, query_, search_), but verbs are not uniform (get vs list, query vs search). This is a minor inconsistency.
Tool Count5/5
Six tools is appropriate for a read-only CRM data access layer. Each tool has a clear purpose without unnecessary bloat.
Completeness2/5
The server covers only read operations (get, list, query, search) and provides no create, update, or delete tools. For a CRM server, this is a significant gap that will limit agent workflows.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed 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
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains return format ('JSON array of matching entities') and parameter behaviors (limit range, default). However, it does not disclose error handling, authentication requirements, or behavior for invalid entity types, leaving gaps.
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 well-structured with sections (Args, Returns, Examples) and is mostly concise. Each sentence adds value. Minor redundancy (e.g., 'JSON array' could be implied) but overall efficient.
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 tool's complexity (5 params, output schema exists), the description covers parameter details, return type, and examples. It could mention valid entity types explicitly, but the output schema may cover that. Overall complete for a query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It clearly explains each parameter: entity type with examples, WHERE clause syntax, limit range, fields as comma-separated, and order_by format. Examples further illustrate usage. This adds significant 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?
The description clearly states the tool's purpose: 'Query Bullhorn entities using SQL-like WHERE syntax.' It specifies the resource (Bullhorn entities) and the action (querying with WHERE syntax). Examples further clarify. It distinguishes from sibling tools like get_candidate or list_candidates by emphasizing the SQL-like query capability.
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 for complex filtering via WHERE syntax but does not explicitly contrast with siblings like search_entities or list_* tools. No when-not or alternative guidance is provided. Examples help, but explicit comparison is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It states the return type (JSON object) but does not mention error handling (e.g., job not found), authentication requirements, or side effects. For a simple read tool, it is minimally adequate.
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 very concise, using a docstring format with Args and Returns sections. It is front-loaded with the purpose and contains no redundant words.
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 tool's simplicity (2 params, no pagination), the description covers input and output adequately. An output schema exists. It could mention behavior on missing job ID, but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds full semantics: job_id is the ID, and fields is a comma-separated string with default behavior explained. This compensates completely for the missing schema descriptions.
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 gets details for a specific job by ID, using a clear verb and resource. It distinguishes itself from siblings like list_jobs and get_candidate.
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?
Usage is implied: use when you have a job ID and want details. But there is no explicit guidance on when not to use it or alternatives, such as using list_jobs for browsing.
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?
No annotations are provided, so the description carries full responsibility. It states it returns a JSON object with candidate details, implying a read operation. However, it does not mention error handling, permissions, or idempotency specifics, though the basic read behavior is clear.
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 concise and well-structured with 'Args' and 'Returns' sections. Every sentence is informative, with no redundant content.
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 presence of an output schema (not shown) and two well-described parameters, the description is mostly complete for a basic get operation. However, it could mention the typical workflow (e.g., use 'list_candidates' to obtain IDs) and provide some error handling notes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains 'candidate_id' as 'The Candidate ID' and 'fields' as 'Comma-separated fields to return (default: all common fields)', adding meaningful semantics beyond the schema's titles and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get details for a specific candidate by ID,' using a specific verb and resource. It distinguishes from siblings like 'list_candidates' and 'get_job' by targeting a single candidate via ID.
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 candidate ID but does not explicitly state when to use this tool versus alternatives like 'list_candidates' to find IDs. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It states returns a JSON array and explains parameters, but lacks details on pagination, sorting, error handling, or response size limits beyond the implied limit param. The examples help but do not cover all behavioral traits.
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 well-structured with a summary, Args, Returns, and Examples sections. It is concise yet informative, using bullet points and code examples to aid comprehension without redundancy.
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 description covers all parameters, return type, and usage patterns with examples. However, it omits details on error conditions, authentication requirements, or data freshness. Given moderate complexity and presence of an output schema, it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It defines each parameter with usage: query includes Lucene syntax examples, status is a filter, limit has range and default, fields describes comma-separated selection. This fully explains semantics beyond 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 clearly states the tool's purpose: 'List and filter candidates from Bullhorn CRM.' It specifies the action (list/filter), the resource (candidates), and the source. This distinguishes it from sibling tools like get_candidate (single candidate) and list_jobs (different entity).
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 includes parameter explanations and four usage examples, giving clear context for typical use cases. However, it does not explicitly state when to use this tool vs. alternatives (e.g., 'use get_candidate for a single candidate'), leaving some ambiguity.
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?
Despite no annotations, the description is transparent: it explains the search functionality, returns a JSON array, and uses Lucene syntax. It does not explicitly state read-only behavior, but the examples imply no side effects. The description adequately discloses the core behavior.
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 concise and well-structured: a one-line summary, then Args, Returns, and Examples. Every sentence is purposeful, no redundancy. The format is front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the essential aspects: entity types, query syntax, limit, fields, and return format. With an output schema present, the return description is sufficient. Minor omissions (e.g., pagination) are acceptable for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully compensates by detailing each parameter: entity types, Lucene query, limit range (1-500, default 20), and fields as comma-separated. Examples illustrate valid values, adding substantial meaning beyond 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 starts with 'Search any Bullhorn entity type using Lucene query syntax,' which clearly specifies the action (search) and the resource (any entity type). It distinguishes from sibling tools like get_candidate and list_candidates, which retrieve single entities or lists without search syntax.
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 a Lucene query is needed, but it does not explicitly state when to use this tool versus alternatives like query_entities. No exclusions or when-not-to-use guidance is provided, leaving the agent to infer context from examples.
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?
No annotations provided, so description fully explains behavior: returns JSON array, supports Lucene query, defaults to 20 results. It doesn't mention pagination or permissions, but non-destructive listing is implied.
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?
Description is structured with Args, Returns, and Examples. It is concise enough for a tool with 4 parameters, though slightly verbose. Front-loaded with purpose, making it easy to scan.
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 output schema exists and no annotations, description is complete: covers all parameters, provides examples, mentions defaults and return format. No gaps for this complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description compensates fully: explains query (Lucene), status (filter), limit (max 1-500, default 20), fields (comma-separated). Examples demonstrate usage, adding significant value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists and filters job orders from Bullhorn CRM. It distinguishes from sibling tools like get_job (single job) and list_candidates (different entity), making its purpose specific.
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 examples showing different usage patterns (no args, query, status), giving clear context. However, it does not explicitly state when not to use this tool or mention alternatives like query_entities.
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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