MCP Automations

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

With no annotations, the description carries the burden of disclosure. It explains it searches and returns a digest with citations, implying a read-only operation, but does not explicitly state safety, rate limits, or other side effects. The 'recent news' and 'citations' details add some transparency beyond 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 a single sentence with no fluff, front-loading the action and purpose. It earns every word, though it omits parameter details (captured elsewhere in scoring).

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, an output schema exists, and the core behavior is stated. However, the description omits the second parameter and provides no usage context relative to siblings. It is adequate but not comprehensive.

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 0%. The description only explains 'topic' via backticks, and does not mention 'n_results' at all. Since there are only two parameters and the description leaves one undocumented, it fails to fully compensate for the lack of schema descriptions.

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 a specific verb ('Search') and resource ('the web for recent news'), and clearly distinguishes this tool from siblings like find_competitors and summarize_url by focusing on news digests with citations.

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?

No guidance is provided on when to use this tool versus alternatives, nor any exclusions. The description merely states what it does without specifying contexts or distinguishing it from similar search-based sibling tools.

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 are absent, so the description must disclose behavioral characteristics. It does not mention whether the tool is read-only, any external dependencies, limitations, or what constitutes 'plausible' competitors.

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 a single sentence that immediately conveys the tool's purpose and includes an example, with no extraneous 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?

For a simple tool with an output schema, the description covers the core action and inputs. It is somewhat thin on behavioral context, but the availability of an output schema reduces the need to explain return values.

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 has no parameter descriptions (0% coverage), but the description naturally explains both parameters: n is the number of competitors and domain is the company's website. The example 'stripe.com' clarifies the domain format.

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 function with a specific verb 'Identify' and resource 'competitors', and uses parameter placeholders n and domain, distinguishing it from the sibling tools which are content-related.

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?

No guidance is provided on when to use this tool vs alternatives or when not to use it. The description only states the action, leaving the agent to infer usage context.

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 must disclose behavioral traits, but it only restates the action. It does not explain whether the original text is preserved, any input length limits, how the output format affects content, or any side effects.

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 entire description is a single, front-loaded sentence with no filler. It conveys the verb, resource, and target output formats efficiently.

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 tool has only two simple parameters and an output schema, so the description covers the core purpose and parameter semantics sufficiently. It loses a point because it lacks any guidance on when to use the tool or what behavioral caveats exist.

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 0%, but the description compensates by clarifying that 'text' should be long-form and by explicitly listing the three valid format values. This adds meaningful context beyond the bare schema definitions.

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 'Repurpose' and identifies the resource as 'long-form text', then enumerates three concrete output formats (twitter_thread, linkedin_post, newsletter). This clearly distinguishes it from sibling tools like summarize_url and find_competitors.

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 implies usage for converting long-form content into a listed format, but it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool extracts clean article text, which is a useful behavioral detail, but it does not mention potential failure modes (e.g., invalid URLs, non-article content, rate limits) or clarify that it is read-only. The description adds some context beyond what a simple 'summarize URL' would, but significant gaps remain.

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 a single sentence that fully captures the core functionality with no redundant words. It is front-loaded with the verb 'Fetch' and conveys the entire pipeline efficiently.

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 and the presence of an output schema (which eliminates the need to describe return values), the description is mostly complete. It covers the main workflow and input semantics adequately. The lack of explicit usage guidelines and failure-specific details prevents a perfect score, but for a straightforward summarization tool it is reasonably 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 schema has no parameter descriptions (0% coverage), so the description must compensate. It does so by mentioning 'URL' and 'N-bullet,' which directly explains the 'url' and 'n_bullets' parameters. The term 'N-bullet' clarifies that n_bullets controls the number of bullets in the summary, adding meaning beyond the raw schema. However, it does not explain constraints or default behavior beyond the schema's default.

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 action: 'Fetch a URL, extract clean article text, and return an N-bullet summary.' It uses specific verbs (Fetch, extract, return) and names the resource (URL, article text, summary). It distinguishes itself from siblings like daily_digest, find_competitors, and repurpose_content by focusing on URL summarization.

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 implies usage context—when you want a summary of a URL's content—but does not explicitly state when to use it versus alternatives, nor does it mention when not to use it (e.g., non-article pages). It lacks exclusions or comparisons to sibling tools, so guidance is only implied.

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