MESSORA

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds important behavioral details beyond those: terminal states (SUCCESS, FAILURE, REVOKED) versus non-terminal states (PENDING/STARTED), and the need to poll again. This is valuable, non-redundant 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?

The description is two concise sentences. It front-loads the core purpose, then adds the most critical behavioral note (polling and terminal states) without any filler or 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?

With a simple single-parameter schema and an output schema present, the description covers the needed operational semantics: what the tool does, what states exist, and that polling is required. The annotations cover safety, so no further behavioral disclosure is needed.

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%, and the description does not explain the job_id parameter, such as where to obtain it or its expected format. Although the parameter name is self-explanatory and only one exists, the description fails to compensate for the absence of schema descriptions, so the agent must infer the meaning.

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 ('Get') and clearly identifies the resource ('status and result of a crawl or search job'). It distinguishes from siblings like start_crawl and start_search by focusing on retrieving an existing job, and even names the poll-able states.

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 clear usage context: it is for retrieving job status and indicates that PENDING/STARTED states must be polled again, implying repeated calls. It does not explicitly state when not to use this tool or name alternatives, but the sibling list and phrasing make the intended use obvious.

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 already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds specificity about the returned data (plan, credits, balance) but discloses no additional behavioral traits such as authentication, rate limits, or side effects. Given the strong annotations, the description provides marginal extra value.

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, clear sentence that front-loads the action and concisely conveys the purpose. There is no redundant information or filler, making it highly efficient.

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 tool with no parameters and an output schema present, the description is complete. It specifies exactly what information is returned (plan, credits used, remaining balance) and does not need to explain output structure since that is covered by the 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?

The tool has 0 parameters, so the schema trivially covers 100% with nothing to document. Per the baseline rule for 0 params, a score of 4 is appropriate; the description does not need to add parameter semantics since there are none.

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 ('Get') and resource ('the plan, credits used and remaining balance of the MESSORA account'). It distinguishes itself from sibling tools such as get_job, scrape_url, start_crawl, and start_search, which all 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?

The description provides no guidance on when to use this tool versus alternatives, no exclusions, and no context on when it is appropriate. It simply states what it does without indicating when an agent should choose it over other options.

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 discloses credit consumption only on success, which is non-obvious and useful. It also clarifies synchronous return behavior. Annotations indicate readOnly=false and idempotent=false, and the description adds specific context about side effects, enhancing transparency.

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 long, front-loaded with the core purpose, and every sentence provides meaningful information without redundancy. It achieves high clarity in a compact form.

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 16 parameters and output schema, the description provides a high-level overview but omits important behaviors such as output formats, error handling, and most parameter options. The output schema fills some gaps, but the description alone is incomplete for a complex 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?

With 16 parameters and 0% schema coverage, the description only explains max_pages, leaving many critical parameters (e.g., formats, render_js, tags, fact_check) without semantics. This is a significant gap for effective tool invocation.

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 extracts content from a URL and is synchronous, returning content directly with no job_id. This distinguishes it from sibling tools like start_crawl, which likely operate asynchronously, and from get_job/get_usage which are not extraction tools.

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 clear context by highlighting synchronous behavior and direct content return, implying usage for immediate extraction needs. It does not explicitly name alternatives but the contrast with start_crawl's job_id is implied, giving reasonable guidance.

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 indicate the operation is not read-only and not idempotent. The description adds valuable context about the asynchronous nature and the job lifecycle states (PENDING/STARTED transitioning to terminal states), which is beyond the annotations. It does not discuss side effects or rate limits, but the added state-machine detail 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 a single, information-dense sentence that wastes no words. It conveys the core action, the asynchronous contract, and the next step (poll get_job) efficiently, earning top marks for conciseness.

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?

Despite the concise phrasing, the description omits any elaboration on crawl configuration or parameter behaviors, which is significant for a tool with 16 parameters and no schema descriptions. The presence of get_job as a sibling and an output schema helps, but the description alone does not provide enough context to safely configure non-default options like pdf_ocr or max_depth.

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%, yet the description provides zero explanation of the 16 parameters. It does not even mention that 'url' is the target to crawl or what 'max_pages' controls, leaving agents to guess from names and types alone. With no compensation in the description, this is a critical gap.

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 begins with 'Start an asynchronous crawl', a specific verb+resource that clearly identifies the action and distinguishes it from siblings like start_search, scrape_url, or get_job. It also states the return value (job_id), leaving no ambiguity about what the tool does.

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 instructs the agent to poll get_job until the job reaches a terminal state (SUCCESS, FAILURE, or REVOKED), which is direct workflow guidance. It does not, however, offer explicit exclusions or when to use alternatives like scrape_url for single-page fetches, so it does not meet the full 5-level bar.

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 annotations already indicating non-read-only, open-world, and non-idempotent behavior, the description adds the crucial detail that the search is asynchronous and returns a job_id that must be polled via get_job. This is a behavioral trait not captured by the annotations, enhancing the agent's understanding of the workflow. It does not mention rate limits or auth, but the async pattern is well disclosed.

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 only two sentences long, front-loaded with the core action and output, and immediately directs the agent to the next step (poll get_job). Every word contributes value, with no redundancy or unnecessary detail.

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?

While the output schema likely covers return values, the tool has 10 parameters and the description only addresses the start-and-poll flow. Key parameter semantics are missing, as noted above. The description is minimally viable but leaves significant gaps for an agent to correctly configure the search options.

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%, so the description must compensate for explaining parameters, but it does not. The description only mentions the query implicitly and the returned job_id. Parameters like query_fanout, use_cache, and include_domains are left entirely to their titles, which may be ambiguous. The description adds no parameter-level guidance, making this a weak point.

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 that this tool starts an asynchronous premium web search and returns a job_id. The action is specific ('Start') and the resource is well-defined ('premium web search'), distinguishing it from siblings like get_job, start_crawl, and scrape_url.

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 clear workflow guidance: start the search, then poll get_job until a terminal state. This tells the agent exactly what to do after invoking the tool. However, it does not explicitly compare with alternatives like start_crawl or state when not to use this tool, so it stops short of a full 5.

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