Run analytics queries against BambooHR datasets, applying optional filters to retrieve targeted workforce insights.
Run an analytics query against a BambooHR dataset with optional filters
| Name | Required | Description | Default |
|---|---|---|---|
| fields | No | Comma-separated list of fields to include in the results | |
| datasetId | Yes | The dataset ID to query | |
| filtersJson | No | JSON object of filters, e.g. '{"department": "Engineering"}' |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It indicates an analytics query but does not state whether it is read-only, what the response contains, whether pagination exists, or any permissions/rate-limit considerations. This is a minimal behavioral signal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loads the primary action, and contains no filler. Every phrase contributes meaning: what it runs, the target system, and the key optional behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple three-parameter tool with full schema coverage, the description is minimally viable for basic invocation. However, with no annotations, no output schema, and no sibling differentiation, it leaves gaps around return value expectations and tool selection guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the structured schema already documents all parameters. The description adds a small reinforcement of 'optional filters', matching filtersJson, but it does not provide additional semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Run' with a specific resource ('analytics query against a BambooHR dataset') and mentions optional filters. It does not explicitly distinguish itself from sibling tools like run-adhoc-report or run-custom-report, but the dataset-focused wording is sufficiently specific for basic purpose clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives such as run-adhoc-report, run-custom-report, or discover-datasets. The description implies usage for dataset queries but provides no context, exclusions, or selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.