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Glama

LiveDataLink

local_search

Read-onlyIdempotent

Find local businesses, restaurants, services, and places near any location. Returns name, type, address, phone, website, hours, cuisine, and distance. Use this for 'find restaurants near me', 'coffee shops in downtown Houston', 'gas stations near 60601', 'best pizza in Chicago', 'pharmacies nearby', 'hotels in Austin', 'find a mechanic', 'gyms near me', or any local business or place discovery question. Supports: restaurants, cafes, bars, gas stations, pharmacies, hospitals, doctors, dentists, gyms, hotels, grocery stores, banks, schools, parks, libraries, auto repair, salons, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat to find (e.g., 'restaurants', 'coffee', 'gas station')
radiusNoSearch radius in miles (default: 1.5)
locationYesWhere to search (city, zip, or address)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds useful context about the supported categories and default-radius behavior through schema. It does not disclose response pagination, unavailability of certain categories, or cutoff ranges, so it adds some but not rich behavioral context. With annotations covering the core traits, a 3 is fair.

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 focused and front-loaded with the core action, followed by the return fields, then usage examples, then supported categories. Every sentence contributes something useful, with no redundant restatement of the tool name or title.

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?

Annotations cover safety and idempotence, the schema covers all three parameters, and the description covers scope, return fields, examples, and category list. The main gap is lack of explicit mention of result limits or fallback behavior for unknown locations, but for a read-only local discovery tool this is adequate and nearly complete.

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 parameter is already documented. The description adds practical value by listing the output fields and many example query strings, but it does not deepen the meaning of the parameters beyond the schema. Baseline 3 is appropriate since the schema carries the semantic load.

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 uses a specific verb ('Find') plus a clear resource ('local businesses, restaurants, services, and places near any location') and enumerates return fields. It distinguishes itself from the large sibling set by naming the exact query patterns it serves, so an agent can immediately recognize what it does.

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 many concrete example queries and explicitly says 'Use this for... or any local business or place discovery question.' It does not name sibling alternatives like recreation_nearby or realestate_search to exclude, but the examples plus 'any local discovery' provide strong practical guidance for when to select 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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TDQS

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.