Scopeweb

recommend_name

Read-only

Runs the whole naming job on the server and returns one recommended domain with its real price and a confirm_url, plus runners-up. From a concept it generates candidates, grades the unregistered ones on measurable properties with a trademark signal, re-checks the best against the registry live, and prices them in order until one quotes. For when the user wants a name picked rather than a list, for example 'what should we call this?'. The answer shows the count at every step, why the pick won, its trademark signal with the caveat, and any name refused along the way with the reason. The confirm_url is not a purchase: a person opens it and pays on scopeweb.io. Cannot spend money.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "concept": "<concept>"
    },
    "name": "recommend_name"
  }
}

Input Schema

Name	Required	Description
`tlds`	No	Endings to search. Defaults to com/io/ai/app/dev/co.
`concept`	Yes	What is being named, as a word or short phrase, e.g. 'calm inbox'.
`project_id`	No	Optional project for budget context on the quote.
`budget_cents`	No	Optional ceiling that reorders candidates; it never changes a price.

Output Schema

Name	Required	Description
`steps`	Yes	How many names each step saw.
`reason`	No
`concept`	Yes
`runners_up`	Yes
`spends_money`	No
`recommendation`	Yes	The pick with price and confirm_url, or null with a reason.
`refused_on_the_way`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 29, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnly/destructive=false, but the description adds substantive context: live registry re-check, pricing in order until one quotes, counts at every step, the trademark caveat, refused names with reasons, and the crucial clarification that confirm_url is not a purchase and no money can be spent. That last point directly resolves the highest-risk ambiguity for an agent reading 'price' and 'confirm_url'.

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 the outcome, then the pipeline, then the usage cue. Five sentences is on the long side, but the confirm_url/payment disclaimer and the refused-name behavior each carry real decision value, so only mild trimming is warranted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description needn't enumerate return fields, yet it still previews the answer shape (counts, winning rationale, trademark caveat, refusals). Combined with the safety disclaimer for a read-only, open-world tool, nothing an agent needs to call or interpret it is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so each of the four parameters is already documented, including the non-obvious budget_cents caveat that it only reorders and never changes a price. The description adds no parameter-level detail beyond implying pricing behavior, so the baseline 3 applies.

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?

States a concrete verb and outcome: it runs the full naming job and returns one recommended domain with price, confirm_url, and runners-up. This is clearly distinguishable from siblings like suggest_names (list) and score_name (single grading step), since the description enumerates the pipeline stages it composes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use it: 'when the user wants a name picked rather than a list,' with a sample utterance. The 'rather than a list' clause implicitly routes away from suggest_names, though it does not name that sibling explicitly or state when not to use this tool (e.g. checking a name the user already chose).

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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