accept
Accept one quote on a request. The chosen model executes the task. Poll get_deliverable for the result.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| quote_id | Yes | ||
| request_id | Yes |
QuoteFirst
TDQS
A3.8/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description reveals that the tool triggers model execution and instructs to poll 'get_deliverable' for the result, indicating asynchronous behavior. Annotations are not contradicted: readOnlyHint=false aligns with mutation, destructiveHint=false is plausible.
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?
Two sentences with no filler, front-loading the core action and providing actionable follow-up. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple action with no output schema, the description covers the main purpose and async behavior. However, it lacks details on prerequisites, side effects, or error states, which are partially mitigated by sibling tools and annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no parameter descriptions), and the description only mentions 'one quote on a request', implicitly referencing request_id and quote_id but not explaining their roles or format. This adds minimal value 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 verb 'accept' and the resource 'quote', distinguishing it from siblings like 'quote' (creating quotes) and 'get_deliverable' (retrieving results). It explicitly notes that the chosen model executes the task, adding context.
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 after a quote has been obtained but does not explicitly state when to use this tool vs. alternatives like 'quote' or 'rerun'. No exclusions or comparison with siblings is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4.1/5.0
Disambiguation5/5
Each tool serves a distinct purpose in the quote-to-deliverable workflow: initiating quotes, accepting, estimating costs, polling results, rerunning with quality guarantee, and checking spending. No overlapping functionality.
Naming Consistency5/5
All tool names use lowercase with underscores for compound names (e.g., get_deliverable, estimate_cost). Single verbs like quote and spend are also consistent with the pattern.
Tool Count5/5
With 6 tools, the server covers the essential operations for managing AI model quotes and deliverables without unnecessary complexity. The count is well-scoped for a focused service.
Completeness4/5
The lifecycle from quote to deliverable is well-covered, but a cancellation or rejection tool is missing, which could be needed if a user changes their mind after accepting a quote.