edit_itemsEdit inventory (quantity, exclude, add by item_key). Response carries the re-priced quote. Scope: jobs:write.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | Non-empty. Any edit clears the customer's approval and the e-signature. | |
| 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?
Annotations already declare readOnlyHint=false and destructiveHint=false. The description adds meaningful behavioral context beyond that: the response carries the re-priced quote and the operation requires jobs:write scope. It does not contradict 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 three short clauses, front-loaded with the core action, followed by response and scope. Every sentence earns its place with no filler.
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?
Given a rich schema that documents all parameters and side effects, and no output schema, the description adequately conveys the tool's purpose and response. It leaves detailed behavioral rules to the schema, which is acceptable.
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%, including nested oneOf structures, defaults, constraints, and side effects. The description itself adds no parameter-level detail, so the baseline score of 3 is appropriate.
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 verb ('Edit'), resource ('inventory'), and the exact operations ('quantity, exclude, add by item_key'). The added note that the response carries the re-priced quote distinguishes it from quote-only or approval siblings.
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?
The description implies a usage context: edit inventory and receive a re-priced quote. However, it does not explicitly state when to use this tool versus alternatives like quote_inventory or approve_inventory, nor does it provide any exclusions.
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.