Algolia Productivity

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description reinforces read-only behavior and adds that it returns the operation's result. No contradiction, and the added return info is useful context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero waste. Purpose is front-loaded, and the usage workflow is described efficiently. Every sentence earns its place.

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

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

The description references the full workflow (discover, describe, then call), making it self-sufficient for an agent. The absence of an output schema is mitigated by the statement that it returns the operation's result. Complete for a generic read-operation caller.

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

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

Schema coverage is 100% for both parameters, so the schema documents them fully. The description only instructs to pass operationName and arguments exactly, which adds no new semantic meaning beyond what the schema provides, so a baseline of 3 is appropriate.

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

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

The description clearly states the tool runs one read-only Algolia operation, distinguishing it from sibling tools by specifying it is used after discovery and description tools. It names the exact verb, resource, and scope, making the purpose unambiguous.

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

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

Explicitly states when to use this tool: after algolia_discover_tools and usually after algolia_describe_tool, when the operation has effect read. This provides clear routing guidance relative to siblings, leaving no ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds value by specifying exactly what the tool returns (exact input schema, read/write effect, domain, title, description) and implies it performs no mutation. This exceeds the annotation baseline without contradicting it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero fluff. The core purpose is front-loaded, and the prerequisite is stated in the same breath. Every word contributes to agent understanding.

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

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

For a simple one-parameter tool with a clear return payload described in the description, nothing is missing. The description covers what the agent needs to invoke it correctly and what to expect in response, even without an output schema.

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

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

Schema coverage is 100% and the parameter description already states to call discover first. The tool description reinforces this by saying the operation name comes from algolia_discover_tools, adding context about where the value originates. This extra guidance justifies a slight elevation above the baseline.

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

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

The description states a clear action (inspect one Algolia operation) and resource (operation returned by algolia_discover_tools). It distinguishes itself from siblings by naming the discover tool as the source and the call tool as the alternative for execution. The agent knows exactly what this tool does and how it differs.

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

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

The description gives explicit when-to-use guidance: before calling the operation, and it instructs to call algolia_discover_tools first to obtain operation names. This effectively tells the agent when to use this tool vs. alternatives, leaving no ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already cover readOnlyHint=true and destructiveHint=false, so the description does not need to repeat safety. It adds value by explaining the output format (compact operation names, descriptions, domains, read/write effects) and the browse behavior. It does not mention pagination details, but those are in the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. It front-loads the purpose and usage, then states the output. Every word contributes to the agent's understanding.

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

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

For a read-only discovery tool with no output schema, the description fully covers what it does, how to use it, what it returns, and the parameter semantics. There is nothing an agent needs to know to call it correctly that is missing.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description enhances the query parameter with concrete examples ('find records', 'zero-result queries') and explains the browse option when omitted. This goes beyond the schema's basic 'concise description of the desired capability' and helps the agent craft effective queries.

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

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

The description clearly states the tool's purpose: discovering Algolia operations without needing exact names. It lists what it returns (operation names, descriptions, domains, read/write effects) and implicitly distinguishes itself from sibling tools like algolia_call_read_tool and algolia_describe_tool by focusing on discovery rather than execution or detailed description.

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

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

The description provides explicit guidance on how to use the tool: search with concise intent phrases and omit the query to browse. It gives concrete examples and implies that when exact names are known, other tools are more appropriate. However, it does not explicitly name the sibling alternatives or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.