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

Annotations already declare readOnlyHint=true, and the description adds useful context about comparing current pricing and the valid product count. It does not mention return format or any edge behavior (e.g., unknown slugs), but that is not a serious gap for a simple read-only comparison tool.

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?

Three short sentences front-load the purpose, then provide a when-to-use trigger and parameter guidance. No filler or repeated schema information.

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?

With one parameter, strong schema coverage, a readOnly annotation, and a clear purpose, the description is sufficient for an agent to select and invoke the tool. It could add a brief note about the output format, but the 'prices in one view' phrase already implies the result.

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 a clear '2-5 product slugs' description, so the schema carries the parameter meaning. The description goes beyond the schema by telling the agent that slugs can be found with search_products, adding integration guidance.

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 uses a specific verb ('Compare'), resource ('current pricing plans'), and scope ('2 to 5 SaaS products side by side'), which clearly differentiates it from sibling tools like search_products or get_product. It also ties the tool to named-product decisions.

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?

It explicitly states when to call the tool: 'when the user is choosing between named products and wants their prices in one view.' It stops short of naming sibling alternatives or stating when not to use it, so it lacks the explicit exclusion guidance of a 5.

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, so the safe read nature is covered. The description adds valuable behavioral context: it returns only the 10 most recent changes and mentions the API key requirement for deeper history. It doesn't contradict annotations, and the extra detail about history depth is useful.

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 fluff. It packs purpose, usage, parameter guidance, and a limitation into a compact, front-loaded format. 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?

For a tool with one parameter, a clear schema, and readOnly annotations, the description fully covers the tool's function, when to use it, how to get the slug, and its output limitation. No output schema is present, but the description conveys the result (pricing plans and recent changes) sufficiently. Nothing essential 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 a clear description for the single 'slug' parameter ('Product slug, e.g. "notion"'). The description reinforces this by pointing to search_products for finding slugs, adding practical guidance beyond the schema. This exceeds the baseline for high coverage.

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 it retrieves a SaaS product's current pricing plans and recent price changes, distinguishing it from siblings like search_products (which finds products) and recent_price_changes (which may be a broader filter). The verb 'get' and specific resource 'product pricing' are precise.

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 call: 'when the user asks what a specific product costs, what plans it offers, or whether its price changed recently.' Also instructs to find the slug via search_products, and notes the 10-change limit with an API key requirement for deeper history. This provides clear context and alternatives.

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, so safety is covered. The description adds valuable context: 'fixed snapshot of up to 25 entries per group—no pagination' and 'verified' data, informing the agent about result limits and data quality. 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.

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

Two sentences cover purpose, usage triggers, and constraints with no redundancy. The main action is stated first, followed by usage guidance.

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 parameterless tool with readOnlyHint, the description fully explains what it returns (recent changes, biggest movers), the time frame (30 days), scope (whole catalogue), and the limitation (up to 25 per group, no pagination). This is sufficient for an agent to decide when to use it.

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?

There are zero parameters, so schema coverage is 100%. The baseline for zero-param tools is 4; no parameter-specific descriptions are needed.

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 provides recent verified SaaS price changes and biggest movers over 30 days, and it distinguishes itself from sibling tools (search_products, get_product, compare_products) which have different purposes.

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 specifies when to use: 'Call this when the user asks what changed in SaaS pricing lately, who raised prices recently, or for examples of price increases.' Also notes the fixed snapshot and lack of pagination so the agent knows not to expect more results.

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?

Beyond the readOnlyHint annotation, the description explains the tool returns up to 20 matches and specifies the fields (slug, category, starting price). It also indicates that full plan details require using get_product, adding useful behavioral context. However, it does not mention potential edge cases like no matches or errors, though for a simple read tool this is sufficient.

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, front-loaded with the primary purpose, and every sentence adds value: the first defines the search functionality and the second gives usage guidelines and output details. There is no redundancy or filler.

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 tool with one parameter and no output schema, the description is comprehensive. It covers what the tool does, when to use it, what it returns, and how to proceed with the results. The absence of an output schema is mitigated by describing the return fields and limits.

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?

The schema description for 'query' says 'Product name or keywords', but the tool description enriches this by stating search matches by name or description and implies semantic search when the user describes a kind of tool. This adds meaning beyond the raw schema definition, covering the parameter's intent effectively.

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 searches PriceTrack's catalogue of SaaS products by name or description. It specifically mentions the verb 'Search', the resource, and the return of up to 20 matches with slug, category, and starting price, distinguishing it from sibling tools like get_product and compare_products.

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 explicitly says when to call this tool: when the user asks what a SaaS product costs and needs its slug, or when they describe a kind of tool and want priced options. It also directs the user to use get_product with a returned slug for full plan details, providing clear guidance on alternatives.

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