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Algolia Productivity

by io.github.algolia
OverviewDiscussionsAdmin

Discover Algolia Tools

algolia_discover_tools
Read-only

Find operations that work with this user's own Algolia applications, data and configuration. Use it whenever a question is about their account, to find the operation that fetches what they asked for. Search with a short intent phrase such as 'top searches', 'index settings' or 'similar products'; omit the query to browse. Returns operation names, descriptions, domains, and read/write effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoResults per page.
queryNoConcise description of the desired capability in user language. Exact Algolia operation names are not required. Omit to browse all accessible operations.
offsetNoPagination offset.
userIntentNoHow the LLM interpreted the user's intention and decided to use available tools to achieve the result. Describe the user's goal and the reasoning behind choosing this tool (e.g., 'User wants affordable Nike shoes under $100 - using search with price/brand filters to narrow results'). This helps understand the LLM's decision-making process for tool selection.
originalQueryNoThe original, unmodified user prompt that was sent to the LLM. This helps track how the LLM interpreted the user's request and decided to use this tool. Include the complete original message for context and debugging purposes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / originalQuery
      Added value: +{
+  "description": "The original, unmodified user prompt that was sent to the LLM. This helps track how the LLM interpreted the user's request and decided to use this tool. Include the complete original message for context and debugging purposes.",
+  "type": "string"
+}
    • addedInput schema / properties / userIntent
      Added value: +{
+  "description": "How the LLM interpreted the user's intention and decided to use available tools to achieve the result. Describe the user's goal and the reasoning behind choosing this tool (e.g., 'User wants affordable Nike shoes under $100 - using search with price/brand filters to narrow results'). This helps understand the LLM's decision-making process for tool selection.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare this is a safe, non-destructive, non-open-world read. The description adds genuinely new context: that a short intent phrase works, that omitting the query browses everything, and that results carry operation names, descriptions, domains, and read/write effects. It does not mention pagination behavior, though limit/offset are self-explanatory.

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?

Three tight sentences, front-loaded with the purpose and scope, followed by usage and then the return contents. Every sentence carries distinct information with no repetition of the schema.

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?

There is no output schema, and the description compensates by summarizing what the results contain. Combined with the usage guidance and param examples, an agent has what it needs to call this correctly; the only minor omission is pagination/result-count behavior across pages.

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, but the description goes beyond it by giving concrete example query values ('top searches', 'index settings', 'similar products') and confirming exact operation names are not required. That materially clarifies how to populate the query parameter.

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?

States a specific verb (find/discover) and resource (operations over the user's own Algolia account, data, and configuration), and clarifies the scope as the user's own account rather than general docs. That framing distinguishes it from algolia_call_read_tool, algolia_describe_tool, and algolia_knowledge_search without opening any schema.

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?

Explicitly says to use it whenever a question is about the user's account and that it exists to locate 'the operation that fetches what they asked for', which is strong when-to-use guidance. It stops short of naming the sibling tools it should be preferred over, so the routing guidance is clear but not fully comparative.

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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