ChangePacket

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

With no annotations, the description carries full behavioral burden. It discloses that it returns a checkout URL only, does not charge directly, and does not expose payment credentials—key traits for a purchase-related tool. This goes beyond the bare minimum.

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 and front-loads the usage condition, followed by the key return value. Every word earns its place with no redundancy.

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 tool's simplicity (no parameters, no output schema), the description fully covers what it does, when to use it, and its side-effect behavior. It is complete for the context.

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, so the baseline is 4. The description adds meaning by explaining the fixed quantity and price (2,000 calls for $9), which is useful context even though no parameters exist.

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 a Stripe checkout URL for 2,000 prepaid ChangePacket calls for $9, providing a specific verb and resource. It distinguishes from siblings like check_mcp_surface_changes and check_url_changes by addressing a purchase action rather than content changes.

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 the tool: only when free usage is exhausted or persistent production use is needed. It also sets expectations that the tool does not charge or expose payment credentials, which guides appropriate usage.

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?

With no annotations provided, the description carries full behavioral burden and does so well. It discloses the stateful first-call baseline storage, later diff behavior (added/removed/modified), and compact risk hints, plus scope limits regarding public HTTPS endpoints.

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, front-loaded with the primary use case, then behavioral detail, then a caution. Every sentence earns its place with no fluff or repetition.

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 single-parameter tool with no output schema and no annotations, the description provides sufficient context: purpose, usage trigger, stateful behavior, result content, and exclusions. It is complete enough for an agent to correctly select and invoke the tool.

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 single parameter endpoint_url has 100% schema description coverage ('Public HTTPS MCP endpoint that supports tools/list'), so the schema already explains the parameter. The description reinforces the public HTTPS constraint but adds no new parameter-specific syntax or format 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 checks whether an MCP server's tool catalog changed, with a specific verb+resource ('check MCP tool-surface changes'). It distinguishes itself from siblings like check_url_changes by focusing on the MCP tools/list catalog rather than generic URL changes.

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 use it ('when an agent depends on a remote public HTTPS MCP server and needs to know whether its tool catalog changed') and when not to ('Avoid for private/authenticated MCP endpoints or as a substitute for a full security audit'). It does not name an alternative tool explicitly, so it misses the full 5-point criteria.

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?

Without annotations, the description carries the burden of disclosing behavior. It effectively reveals the stateful nature: first call stores a baseline, later calls return added/removed text lines. It does not detail persistence duration or exact first-call output, but it covers the core side effect (baseline storage).

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, front-loaded with purpose and use cases. Every word contributes value, and the structure flows from when to use to what it does to what to avoid.

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 one-parameter stateful tool with no output schema and no annotations, the description provides sufficient context: state behavior, return contents, and exclusions. It does not explain baseline expiration or first-call return behavior, but given the tool's simplicity, it is nearly complete.

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 only parameter, url, is fully described in the schema (public HTTP(S), private/unsafe rejected). The description adds that the same URL is reused across calls, but this is a usage nuance rather than new parameter semantics. With 100% schema coverage, 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 specifies the tool's function: tracking changes on a public webpage across repeated calls, with a baseline on first call and added/removed text lines on subsequent calls. It distinguishes itself from siblings like 'check_mcp_surface_changes' by focusing on webpage content rather than MCP surface.

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 (repeated research on a single public webpage needing only changes) and when to avoid (authenticated/private pages, binary files, one-time reading). However, it does not name specific alternative tools, so it falls short of the full 5.

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