Red-flag check for business-for-sale listings: payback, asking multiple, flags, seller questions.
Scored across 2 tools
check_listing performs the screening analysis while list_rules returns the static rule catalog; the two purposes are completely disjoint with no chance of misselection. The descriptions make the division of labor explicit and obvious.
Both tools follow a clean verb_noun snake_case pattern (check_listing, list_rules) that directly maps to their actions. There is no mixing of conventions or casing style.
Two tools is at the thin end, but the server's scope is genuinely narrow (run red-flag rules on a listing plus expose the rule list), and each tool earns its place. It is slightly under the typical 3-15 range rather than bloated or padded.
For a screening-only purpose the surface is solid: the checker runs all 33 rules and list_rules provides transparency into them. Minor gaps remain, since it explicitly cannot fetch listing URLs and there is no way to compare multiple listings in one call, but an agent can work around both by supplying text.
Runs FirstPass Memo's 33 published red-flag rules on a small online-business listing (SaaS, content site, ecommerce, Amazon FBA, newsletter, app; roughly $10k-$150k) from Flippa, Empire Flippers, Acquire.com or any broker. Pass the listing text the user provides and/or the key numbers. Returns payback in months, the asking multiple vs comparable listings, every flag that fired (severity, why it matters, link to the rule) and questions to ask the seller. Screening only: not advice, a valuation or verification. Does not fetch URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Business type. | |
| age_months | No | Months the business has been making money. | |
| asking_price | No | Asking price in USD. Overrides any price found in the text. | |
| listing_text | No | Listing text pasted by the user (asking price, profit, revenue, age, traffic, churn, etc.). Max 20,000 characters. | |
| monthly_profit | No | Average monthly net profit in USD. | |
| monthly_revenue | No | Average monthly revenue in USD. | |
| traffic_sources | No | Traffic mix, e.g. 'Organic search 70%, Direct 20%, Social 10%'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly, idempotent and closed-world behavior, so the bar is lower, yet the description adds real value: what the response contains (payback in months, asking multiple vs comparables, fired flags with severity and rule links, seller questions) and a key behavioral limitation (no URL fetching). It does not mention rate limits or the listing-size ceiling, which is documented only in the schema.
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?
Four tight sentences, front-loaded with what the tool does, followed by inputs, outputs and scope limits. Every sentence carries distinct information with no repetition of the schema.
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?
With no output schema, the description compensates by enumerating the return payload, and it covers scope, accepted inputs, sources and explicit non-goals. An agent has everything needed to decide to call it and to interpret the result.
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 seven parameters are already well documented and the baseline is 3. The description adds only a light nuance — that listing text and/or the key numbers may be supplied — without explaining any individual parameter beyond what the schema states.
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?
States a specific verb and resource ('Runs FirstPass Memo's 33 published red-flag rules on a small online-business listing') plus the exact market segment, price band and sources it targets. This clearly distinguishes it from the sibling list_rules, which only enumerates rules rather than applying them.
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?
Gives clear context for use ('Pass the listing text the user provides and/or the key numbers') and explicit exclusions ('Screening only: not advice, a valuation or verification'). It also rules out a plausible misuse ('Does not fetch URLs'), though it never explicitly names list_rules as the alternative for inspecting the rules themselves.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the 33 published rules the checker uses (code, title, group, severity, when it fires, explainer URL).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint and closed-world behavior, so safety is covered; the description adds real value by disclosing the exact payload shape (code, title, group, severity, firing condition, explainer URL) in the absence of an output schema. No pagination or staleness notes, hence not a 5.
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?
One sentence, zero filler, and the most decision-relevant facts (count and returned fields) are front-loaded before the parenthetical detail.
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?
With no parameters and no output schema, the description carries the return-shape burden and does so fully; the only minor omission is any note on whether the rule set is fixed at 33 or can change.
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 tool takes zero parameters, so the baseline is 4; there is nothing for the description to clarify beyond the no-argument nature, which the empty schema already conveys.
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?
Specific verb ('Returns') plus a concrete resource ('the 33 published rules the checker uses'), and it enumerates the returned fields, so an agent knows exactly what comes back. It does not explicitly contrast itself with the sibling check_listing, so it stops short of a 5.
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?
Usage is only implied: an agent can reasonably infer this is the discovery call to enumerate rules before (or instead of) invoking check_listing, but the description never states when to call it or when the sibling is the better choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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