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CaseyTTS

Bullhorn MCP Agent

by CaseyTTS

find_records

Search Bullhorn entities like candidates, jobs, and placements using structured AND filters, returning matching canonical records with pagination and field selection.

Instructions

Find canonical records of one entity with structured, AND-combined filters.

Only canonical names are accepted (never Bullhorn field names or query text). Every filter is applied exactly; a filter, operator or sort without a verified Bullhorn mechanism returns status "unsupported" and nothing is read.

Args: entity: candidate, job, submission, placement, client_corporation, client_contact, appointment or user. filters: 0 to 10 objects {"field": , "op": , "value": ...}. Operators by type: id/reference eq, in; string eq, in, is_null; boolean eq; integer/number eq, in, gt, gte, lt, lte; datetime gte, lt, is_null (ISO-8601 with offset, or a date = midnight in the reporting timezone). "in" takes 1 to 50 values. concept: Optional tenant concept (e.g. client_submission, interview_completed, offer_extended): only records whose current value matches the tenant's mapping. fields: 1 to 30 canonical field names to return (default: a standard set). sort: {"field", "direction"}; no sort is verified yet (returns "unsupported"). include_deleted: Include deleted records (default false; not verified, returns "unsupported"). limit: 1 to 100 (default 25). cursor: The next_cursor of a previous call with the same arguments.

Returns: JSON with status, entity, records, count, truncated, complete, next_cursor, consistency, reporting_timezone, provenance and warnings.

Input Schema

NameRequiredDescriptionDefault
sortNo
limitNo
cursorNo
entityYes
fieldsNo
conceptNo
filtersNo
include_deletedNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it discloses that unsupported filters/operators/sorts return status 'unsupported' with nothing read, that include_deleted is unverified, default limit of 25, and cursor-based paging. It does not cover auth/permission requirements, but the failure-mode transparency is strong.

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?

Front-loaded with purpose, then cleanly sectioned into Args and Returns. Length is justified by 8 undocumented parameters, and each line carries information, though the Returns block restates what the existing output schema already defines.

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 complex 8-parameter, zero-schema-coverage tool the description is nearly complete, covering inputs and failure semantics. An output schema exists so the return-value enumeration is redundant rather than essential, leaving little genuinely missing.

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 and does: it enumerates entity values, documents the filters object shape and the valid operators per value type, 'in' bounds (1-50), fields range (1-30), concept semantics, sort shape, include_deleted default, limit range (1-100) and cursor reuse. This adds meaning far beyond the bare schema.

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?

States a specific verb+resource ('Find canonical records of one entity') with an AND-combined filter scope. It implicitly distinguishes itself from query-based siblings by specifying 'Only canonical names are accepted (never Bullhorn field names or query text)', though it never names search_entities or query_entities directly.

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 through the constraint that only canonical field names (not query text) are accepted, which steers an agent away from the query-style siblings. However, it never explicitly says when to prefer find_records over search_entities or query_entities, leaving the routing decision to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.