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NYCfoodie

by app.railway.up.nycfoodie-production
OverviewDiscussionsAdmin

get_restaurant

Read-only

Get the full picture for one restaurant in one call: Infatuation rating (0–10), price tier, address, reservation link, booking intel, review summary, tags and every guide it appears in. Use when the user names a specific restaurant. editorial_blurbs carries verbatim Eater guide excerpts (guide title, URL, position, blurb, captured_at) for any venue with Eater guide entries — Eater-only venues have no rating or price, only this prose plus tags. Full review prose is opt-in via include_prose (default: headline and summary only). review.headline is the source's actual headline when one exists, otherwise null — use review.summary for the descriptive text. match_type is 'exact' when the id or name matched verbatim, 'fuzzy' when it was resolved from a partial/typo'd name — never present a fuzzy match as the venue the user named without saying so. booking is null when the source has no booking intel (not the same as walk-in-only); a reservation link alone never implies a booking policy. data_as_of is the dataset vintage and crawled_at is when this venue was last crawled — caveat fast-decaying claims (closures especially) when these are old.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoCanonical restaurant id, or a name to resolve
cityYesCity slug, always required. Currently 'new-york', covering the five boroughs plus the immediate metro (within 30 km of Manhattan).
nameNoAlias for id: the restaurant's exact name
include_proseNoInclude the full review text (default false: headline + summary only)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / name
      Added value: +{
+  "description": "Alias for id: the restaurant's exact name",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "id",
-  "city"
-]New value: +[
+  "city"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"City slug, always required. Currently 'new-york'."New value: +"City slug, always required. Currently 'new-york', covering the five boroughs plus the immediate metro (within 30 km of Manhattan)."
  3. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only provide readOnlyHint=true, so the description carries the behavioral burden and exceeds it: it explains fuzzy vs exact matching, the null booking semantics, Eater-only venues lacking ratings, include_prose opt-in behavior, and data_as_of/crawled_at caveats. These are genuinely non-obvious behavioral details an agent needs.

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?

The description is long, but every sentence carries a distinct, decision-relevant detail — no filler or repetition. It is front-loaded with the core purpose and proceeds through high-value caveats that directly affect how an agent should interpret results.

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?

There is no output schema, so the description must explain the return shape and gotchas. It covers the main output categories, explains null/ambiguous fields, distinguishes match_type semantics, and warns about timestamp-based staleness. For a single-record lookup tool, this is complete.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by clarifying that include_prose defaults to headline/summary only and by explaining how id/name resolution affects match_type (exact vs fuzzy). It does not deeply elaborate every parameter, but the schema already covers each one.

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 opens with a specific verb and resource ('Get the full picture for one restaurant in one call') and enumerates exactly what is returned (rating, price tier, address, review summary, tags, guides). It also distinguishes this tool from search-oriented siblings by stating 'Use when the user names a specific restaurant.'

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?

The description gives a clear trigger condition: 'Use when the user names a specific restaurant.' This implies the tool is for lookups rather than searches or comparisons, but it does not explicitly name sibling tools like search_restaurants or compare_restaurants or state when not to use it.

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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