PriceTrack

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

Annotations already provide readOnlyHint=true. Description adds useful context: compares 2-5 products, requires slugs, and references upstream tool for finding slugs. It does not cover potential return format or pagination, but for a read-only comparison 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?

Two focused sentences front-load the core purpose and usage, then add context on prerequisites. Every sentence is essential, no 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?

Given the simple single-parameter schema, no output schema, clear annotations, and focused task, the description is complete. It tells the agent what it does, when to use it, and how to find inputs. No gaps remain.

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 description coverage is 100%, so baseline is 3. The description explains what slugs are (product slugs) and hints they come from search_products, which adds some value beyond the schema's '2-5 product slugs' label. However, it doesn't elaborate on slug format or behavior with invalid slugs.

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 verb 'Compare' and resource 'pricing plans' are specific, and the description clearly distinguishes from siblings like search_products by mentioning 'product slugs' and directing the agent to find them with search_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?

Explicitly states when to call: 'when the user is choosing between named products'. Provides direct alternative: 'find them with search_products', making usage guidance very clear.

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?

The description adds context beyond the readOnlyHint=true annotation by noting the tool shows only the 10 most recent changes and that deeper history requires a free API key. It does not contradict the annotation (readOnlyHint=true is consistent with a query). No contradictory 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?

Three sentences, no wasted words. Each sentence serves a distinct purpose: stating what the tool does, when to use it (with prerequisite), and a behavioral constraint (history limit). Front-loaded with the core action.

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?

Given the simple tool (1 param, no output schema, high schema coverage, single sibling differentiation), the description is complete. It covers purpose, usage context, parameter origin, and a critical limitation (10 recent changes, deeper history requires API key). No gaps.

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 already has full coverage (100% of param has a description), so baseline is 3. The description adds extra meaning by explaining how to obtain the slug via 'search_products' and including a real example ('notion'), which helps the agent use the parameter correctly.

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 verb 'get' and the specific resources: 'pricing plans' and 'most recent price changes'. It distinguishes itself from siblings by focusing on a single product's pricing and changes, whereas siblings like 'compare_products' and 'recent_price_changes' handle broader comparisons or lists.

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 states when to use this tool: 'Call this when the user asks what a specific product costs, what plans it offers, or whether its price changed recently.' It also directs the user to find the slug with 'search_products', providing clear prerequisites 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?

The readOnlyHint annotation already signals a safe read operation. The description adds valuable behavioral context by noting 'Fixed snapshot of up to 25 entries per group — no pagination,' which informs the agent about data limits and lack of iterability. It does not contradict the annotation and adds meaningful detail beyond 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?

The description is three sentences: it leads with the core purpose, then gives explicit invocation cues, and closes with a behavioral note. Every sentence carries meaning, with 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, read-only, no-parameter tool, the description covers purpose, usage, and a key behavioral trait (fixed snapshot). The only minor gap is the ambiguous term 'group' (e.g., group of what?), but this does not significantly impede tool selection or invocation.

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 tool has zero parameters, and the description does not need to explain any. The baseline for 0 parameters is 4, and the description appropriately focuses on behavior and usage rather than inventing parameter details.

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 returns 'the most recent verified SaaS price changes and the biggest movers of the last 30 days, across the whole catalogue.' This gives a specific verb (returns), resource (price changes), and scope (whole catalogue), effectively distinguishing it from sibling tools like search_products or get_product.

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 'Call this when the user asks what changed in SaaS pricing lately, who raised prices recently, or for examples of price increases.' This provides clear context for when to use the tool, but it does not explicitly mention when not to use it or name alternatives, so it stops short 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?

The annotations include readOnlyHint: true, which already communicates this is a safe read operation. The description adds useful behavioral details beyond this, including that it returns up to 20 matches and specifies the fields returned (slug, category, starting price). There is no contradiction with annotations. A score of 5 would require mentioning any rate limits or pagination behavior, which are not present.

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 concise sentences that front-load the purpose, then usage guidance, then what is returned. No filler words. Every sentence adds value, and the structure is logical.

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?

Given the low complexity (1 parameter, simple string input, no output schema), the description covers the core requirements: what to search, when to call it, what it returns, and the next step (get_product). It doesn't mention sorting order or what happens with empty results, but the limited scope and good annotations make this a minor gap.

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 description coverage is 100% (the single 'query' parameter has a description). The description adds context about what kinds of queries are appropriate (name, description, or tool type description) and hints that results are limited to 20 matches, which adds value beyond the schema. However, it doesn't specify how to format multi-word queries or whether special characters are supported, so a 4 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 it searches PriceTrack's catalogue of tracked SaaS products by name or description, using a specific verb and resource. It distinguishes itself from siblings like get_product (which returns full plan details for a specific slug) and compare_products, so 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 explicitly states when to call this tool: 'when the user asks what a SaaS product costs and you need its slug, or when they describe a kind of tool and want priced options.' It also provides an alternative: 'Use get_product with a returned slug for full plan details,' which gives clear guidance on when to use this vs. a sibling tool.

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