Restaurants and bars about to open in 7 US metros, from new liquor license filings. No key needed.
Scored across 2 tools
metro_stats returns aggregate counts/sources for a metro while search_openings returns individual venues with evidence trails; the resources and outputs are clearly different. An agent would not confuse an aggregate-stats call with a venue search.
Both names are snake_case and readable, but the patterns differ: metro_stats is noun_noun while search_openings is verb_noun. This is a minor deviation rather than a real inconsistency.
Two tools is thin for an opening-intelligence domain — stats and search are the core entry points, but there is no room for venue lookup, metro discovery, or alerts. It sits at the borderline of 'too few' rather than being well-scoped.
The surface has notable dead ends: there is no way to fetch a single venue's full record, list supported metros, or check detail pages beyond links returned in search results. Agents can partially work around this via the embedded links, but core lifecycle operations are missing.
Summary stats for a metro: tracked venues, new this week, sources.
| Name | Required | Description | Default |
|---|---|---|---|
| metro | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It hints at the returned fields but says nothing about whether this is a read operation, how an unsupported metro is handled (the enum may not exhaust the domain), staleness of 'new this week', or response shape.
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?
A single front-loaded sentence with no filler, listing the three metric groups efficiently. It is arguably under-specified rather than bloated, which is the right failure direction for conciseness.
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?
For a one-parameter tool with no output schema, the description covers what the summary contains, which is the main need. It omits return format, numeric vs. list shape for 'sources', and metro-value semantics, leaving gaps an agent would want closed.
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 0%, so the description is the only place parameter meaning could be added, and it never mentions the 'metro' parameter. The schema enum lists valid values, but nothing clarifies whether these are the only supported metros or what 'metro' scoping means for a region like waco-temple.
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 resource (a metro) and enumerates the metrics returned: tracked venues, new-this-week count, and sources. That makes it distinct from the sibling search_openings, which is a lookup tool rather than an aggregate, although the description never says so explicitly.
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?
There is no guidance on when to call this instead of search_openings, no prerequisites, and no exclusions. The agent must infer that this is the aggregate/summary path while search_openings is the record-level path.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search upcoming restaurant/bar openings in a metro (pre-opening signals from liquor license & permit filings). Returns venues with estimated opening window, owner and evidence trail. Without an API key: the public tier (filings older than 30 days, block-level address, no owner contacts), each with a link to its page.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| metro | Yes | ||
| status | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Because there are no annotations, the description carries the full burden. It excels here by disclosing the behavioral difference in data granularity based on API tier, detailing specifics like 'filings older than 30 days' and 'block-level address' for public access.
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 tool is densely packed with critical information. While slightly lengthy, the structure is front-loaded with the primary function and immediately follows with the critical behavioral caveats regarding tiered data, making every sentence valuable.
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 description is very complete for a complex information-gathering tool. It defines the scope (metro-specific openings), the data source (filings), the output content (estimated window, evidence trail), and the key operational limitations (API key tiers and resulting data quality).
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?
Despite providing rich behavioral context, the description adds little semantic value to the structured parameters (limit, metro, status). Since schema description coverage is 0%, the description should ideally explain what the status enum values (new, approved, etc.) represent, which it fails to do.
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 purpose is stated immediately and specifically: to search for upcoming restaurant/bar openings. It differentiates its data source by mentioning 'pre-opening signals from liquor license & permit filings,' which is highly specific and distinguishes it from general business directory tools.
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 provides crucial usage guidelines regarding operational constraints, specifically linking data quality and scope to the presence or absence of an API key. This sets clear expectations for the user based on their access level.
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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