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Search Locations

search_locations
Read-only

Search Instagram for locations by keyword. Returns raw result sections to extract location URLs, names, and details.

Instructions

Search for Instagram locations.

Args: query: Search query (e.g., "New York", "Paris cafe") ctx: FastMCP context for progress reporting max_results: Maximum number of results to return (default 50)

Returns: Dict with url, sections (name -> raw text), and optional references. The LLM should parse the raw text to extract individual locations and their details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds valuable behavioral context by specifying the return structure (url, sections, optional references) and instructing the LLM to parse raw text for locations. No contradiction with annotations.

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 a well-structured docstring with a summary, args, and returns sections. Every sentence adds relevant information, and there is no redundant filler.

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 a simple search tool, the description covers the query parameter, result limit, and response format. The output schema and annotations fill in much of the remaining context, though it could benefit from usage differentiation guidance.

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?

With 0% schema description coverage, the description compensates by explaining 'query' with examples and clarifying 'max_results' as a default-limited count. It also mentions 'ctx' for progress reporting, adding meaning beyond the bare schema fields.

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 clearly states 'Search for Instagram locations' with a specific verb and resource. It distinguishes itself from sibling tools like 'search_users' by targeting locations as the search subject.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as get_location_posts or search_users. The description only lists parameters, leaving the usage context entirely implied by the tool name.

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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