get_date_pricesThis job's quote priced across a ~2-week window from the mover's date rules. flat=true means every day prices the same. Scope: jobs:read.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job id (ULID) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/destructive annotations, the description adds useful behavioral context: the ~2-week window derivation, the meaning of flat=true, and the required jobs:read scope. This helps an agent understand what the result means and what permission context applies, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and mostly front-loaded, with the main purpose stated first. The flat=true clarification and scope note earn their place. The minor redundancy with the title is not a significant issue.
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?
The tool is simple with one parameter, and the description conveys the core purpose and the flat flag's meaning. However, there is no output schema and the description does not specify the full response shape, such as how prices are keyed by date or formatted, leaving some interpretation to inference.
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?
The schema already fully documents the only parameter, job_id, with 100% coverage. The description adds no additional parameter-level detail, but it does not need to since the schema carries the meaning. The mention of flat=true is output semantics, not parameter semantics.
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 names a specific operation: retrieving a job's quote prices across a ~2-week window based on mover's date rules. It is clear about the resource (job quote prices) and the scope (~2-week window), though it does not explicitly distinguish itself from sibling tools like get_availability or get_job.
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, nor are any exclusions or prerequisites mentioned beyond the scope line. The usage context is implied by the description, but there is no explicit decision guidance for an agent.
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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Each tool targets a distinct action or resource: job lifecycle, upload steps, quote variants, key management, and webhook replay are clearly separated. Even the three pricing tools are differentiated by context (pre-job ballpark, direct quote, date-window pricing).
Almost all tools follow a verb_noun snake_case pattern (create_job, list_jobs, set_contact, rotate_key). Minor deviations like ballpark_estimate (no verb) and get_cube_sheet_csv (format suffix) keep it from being perfectly consistent.
32 tools is a large surface for a survey tool, crossing the 25+ threshold. Many key-management and webhook-delivery tools feel like a separate API admin surface rather than core survey workflow.
The core survey flow is well covered: create job, capture contact/access/date, upload video, edit inventory, approve, book, and report outcome. Minor gaps like no cancel/delete job or webhook subscription management are workarounds.