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by org.directindex
OverviewDiscussionsAdmin

Search places

search_places
Read-onlyIdempotent

Find businesses in the city indexes by name (within a city) or domain (from any city), filtered by city, kind, grade, builder, booking or ordering vendor and finding (for example menu_pdf, edge_blocks, no_structured, booking_marketplace). Fifty results a page, best Agent-Direct Scores first, each with its report link. Free, no contacts. Page: https://directindex.org/search.

Input Schema

NameRequiredDescriptionDefault
qNoA name or part of one, which needs city; or a domain, which works without it
cityNoCity or index slug, for example berlin or berlin-cafes-bars
kindNo
pageNo
gradeNo
vendorNoVendor slug, for example thefork, zenchef, toast
builderNoBuilder slug, for example wordpress, wix, squarespace
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
findingNoFinding id, for example menu_pdf, edge_blocks, robots_blocks, no_structured, no_text_nojs, no_channel, booking_marketplace, ordering_marketplace, no_llms_txt
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
conversation_idNoPass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / q / description
      Previous value: -"A name, part of a name, or a domain"New value: +"A name or part of one, which needs city; or a domain, which works without it"
  2. Changed4 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • addedInput schema / required
      Added value: +[
+  "context",
+  "llm_model"
+]
  3. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: 50 results per page, ordering by Agent-Direct Score, a per-result report link, and that the call is free and returns no contacts — details no annotation conveys.

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?

A single dense sentence front-loads the core purpose, matching modes, and filters, followed by a short return-format clause and the site URL. Every clause carries information, though the filter enumeration is slightly list-like and could be trimmed given the schema already names them.

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

Completeness4/5

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

For an 11-parameter search with no output schema, the description supplies the essentials an agent would otherwise lack: result count per page, sort order, and that each hit links to its report. The analytics/context and conversation_id mechanics are fully documented in the schema, so no material gap remains.

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 73%, so the baseline is 3; the q, city, vendor, builder and finding parameters are already documented with examples in the schema. The description restates that same filter set (including finding examples) without adding syntax, combinability rules, or defaults, so it is roughly redundant with structured data.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (find businesses in the city indexes) with the two matching modes spelled out — name within a city, domain from any city. However, it never names or contrasts itself against the close siblings that could also surface businesses (find_direct_places, direct_record, assistant_recommendations), so an agent must infer the boundary.

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 encodes a real usage rule — a name query requires a city while a domain query does not — which is actionable guidance. But it offers no when-to-use/when-not guidance versus sibling lookup and reporting tools, and no exclusions; the reader must derive the calling context.

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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