Bamboohr

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds value by detailing how it probes LLMs, the default model, the BYO key mechanism, and the response structure (per-model {score, confidence, signals, raw_response} + combined view). No contradictions.

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 extremely concise with only two sentences, yet it conveys all essential information. It is front-loaded with the main action and outcome, making it immediately clear.

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 moderate complexity (4 parameters, no output schema), the description fully explains what the tool does, what it returns, and how to configure it. No missing information for effective selection and 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?

Input schema has 100% coverage with good descriptions. The description enhances understanding by explaining the default model behavior, the necessity of `_apiKey` for Anthropic, and providing examples for the `entity` parameter (e.g., 'Pipeworx', 'Acme Corp pricing').

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 'Probe' and identifies the resource 'one or more LLMs' with a clear outcome 'score visibility (0-100) per model'. It distinguishes itself from sibling tools which are domain-specific, making its general-purpose nature evident.

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 explicit use cases like 'AI-marketing audits, pre-launch brand checks, competitive monitoring' and explains when to pass `_apiKey`. It does not explicitly state when not to use it or mention alternatives, but sibling tools are sufficiently different to avoid confusion.

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 explains the tool's behavior: it picks the right tool, fills arguments, and returns the result. It does not contradict any annotations (none provided). It provides enough transparency for an agent to understand the delegated nature of this tool, though it could mention any limitations (e.g., what data sources are available).

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 concise and front-loaded with the core purpose. It includes examples to clarify usage. It could be slightly more structured (e.g., separate the examples) but is effective and not verbose.

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 (one required parameter, no output schema), the description is complete enough. It explains the core functionality and provides examples. It does not need to detail return values as the response is dynamic. The tool's delegated nature is well communicated.

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 input schema has one parameter 'question' with a description. The description adds value by explaining that the question should be in natural language and that the tool handles the rest. With 100% schema coverage, the baseline is 3, and the description does not add new parameter-level details beyond the schema.

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 purpose: answering plain English questions by automatically selecting the best data source and filling arguments. It provides concrete examples that illustrate the scope, making it easy to distinguish from sibling tools like 'bamboohr_get_employee' which are specific to BambooHR.

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 to 'just describe what you need' and gives examples, implying when to use it: for any question where the user wants the system to choose the tool. However, it does not explicitly say when not to use it or mention alternatives, though the sibling tools are clearly different.

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 indicates it returns 'basic info for all employees', which implies a read-only operation and broad scope. However, with no annotations present, the description carries the full burden. It does not disclose details like pagination, response format, or whether the operation might be slow for large directories. The description is adequate but not comprehensive.

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 conveys the core purpose efficiently. It is front-loaded with the verb and resource. No unnecessary words. However, it could benefit from a brief usage note.

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 (2 simple params, no output schema, no annotations), the description is mostly sufficient. It states the tool returns a directory of all employees with basic info, which is reasonable. However, without any output schema, a brief note on what 'basic info' includes would improve completeness.

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%, so the parameters are fully described in the schema. The description does not add any additional meaning beyond what the schema provides (e.g., no mention of required credentials or how to obtain them). Baseline score 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 states the tool retrieves an employee directory from BambooHR with basic info for all employees, specifying both the source system and the scope (all employees). It uses a specific verb ('Get') and resource ('employee directory'), distinguishing it from siblings like bamboohr_get_employee which targets a single employee.

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 does not provide any guidance on when to use this tool versus alternatives. It fails to mention when to prefer this over bamboohr_list_employees or bamboohr_get_employee. No usage context or exclusions are given.

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 empty, so the description carries the full burden. It indicates this is a read operation (get details) and that fields can be specified, which is basic. However, it does not disclose potential side effects (none expected), rate limits, or authentication requirements beyond the schema's required parameters.

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 very short at one sentence, which is efficient. It conveys the core purpose immediately. It could be slightly improved by front-loading the most critical info, but it's already concise.

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 is simple (get employee details), the description is adequate but minimal. There is no output schema, so the agent might wonder about the return format. However, the description together with the schema provides enough to use the tool correctly. Lacks details like error handling or behavior if ID is invalid.

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 the schema already documents all parameters clearly. The description adds no additional meaning beyond stating to 'specify which fields to retrieve', which is already clear from the schema. Baseline 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 states the verb 'Get' and the resource 'details for a specific employee by ID', and distinguishes it from sibling tools like 'bamboohr_list_employees' and 'bamboohr_get_directory'. It specifies the action and the scope (by ID) with no ambiguity.

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. It does not mention that to get a list of employees first you would use 'bamboohr_list_employees', nor does it specify any prerequisites (e.g., knowing the employee ID).

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 does not disclose any behavioral traits. Annotations are empty, so the description carries the full burden. It does not mention read-only nature, pagination, or data format. Since annotations are missing, the description should compensate but fails to.

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 concise sentence, front-loading the purpose. It is appropriately short, though it could include more detail without being verbose.

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 lack of annotations and output schema, the description is insufficient. The tool requires authentication parameters but the description does not mention authentication steps or context. The tool's complexity is low, but completeness is lacking for a file list retrieval.

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% and parameters are described with basic descriptions (Employee ID, API key, subdomain). However, the description does not add any additional meaning beyond what the schema provides, missing the chance to explain how to obtain the employee ID or any constraints.

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 retrieves a list of files for an employee, but does not specify what information about the files is returned (e.g., file names, IDs). It distinguishes itself from sibling tools by focusing on employee files, but lacks detail about the resource type.

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 other BambooHR tools, such as when to use this vs. bamboohr_get_employee. There are no prerequisites or context for 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?

Annotations are empty, so the description carries full burden. It does not disclose any behavioral traits such as whether it returns all fields, pagination, or rate limits. Simply states it returns a directory, which is vague.

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?

Very short, but could be more informative. The second sentence is somewhat redundant with the first. Could benefit from additional context without being verbose.

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 simplicity of the tool (no output schema, no nested objects), the description is minimally adequate but lacks details on what 'directory' includes (e.g., fields returned). With sibling tools that may overlap, more clarity 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 coverage is 100%, so parameters are well-documented in the schema. The description adds no additional semantics beyond the schema.

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 states it lists employees from BambooHR and returns a directory, which is clear. However, it doesn't distinguish this from sibling tool bamboohr_get_directory, which likely also returns employee directory info.

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 on when to use this tool versus alternatives like bamboohr_get_directory or bamboohr_get_employee. The description lacks any context on usage scenarios.

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 empty, so description carries full burden. It does not disclose behavioral traits such as whether the tool is read-only, pagination, sorting, or authentication requirements beyond what schema provides.

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?

Single sentence, no fluff. Could be slightly more informative without adding length.

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 4 required params, no output schema, and empty annotations, the description is minimal. It covers the core action but lacks details on response format, error handling, or use cases.

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%, so baseline is 3. Description adds no additional meaning to parameters; it only mentions date range but not formats or constraints beyond the schema.

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?

Description states the verb 'list' and resource 'time-off requests' with a date range constraint. It clearly distinguishes from siblings like bamboohr_get_employee, which retrieves a single employee.

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 on when to use this tool vs alternatives like bamboohr_list_employees or other time-off-related tools. No mention of prerequisites or exclusions.

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 (readOnlyHint=true, openWorldHint=true, destructiveHint=false) indicate safety and external data access. The description adds detailed behavioral context: market resolution, bet classification, fan-out to specific data packs, and return of evidence packet with comparison. No contradictions 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?

The description is three sentences, front-loading the main action (research a Polymarket bet), then explaining how it works, and finally giving use cases. Every sentence adds value; no wasted words.

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 two parameters, no output schema, and annotations covering safety, the description is complete. It explains the process, input formats, depth options, and output contents. All essential aspects are covered.

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 descriptions for both 'market' and 'depth'. The description reinforces input types but does not add new semantics beyond the schema. Baseline score 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 states the tool's purpose: researching a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet with comparison). It distinguishes itself from siblings by focusing specifically on Polymarket bets with automatic data pack fan-out.

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 explicit usage scenarios: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It does not mention when not to use or alternatives, but the examples are clear and practical.

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 provided, so description must be self-contained. It discloses the data sources (SEC EDGAR, FDA) and that data is returned as paired data plus URIs, but does not mention read-only nature, rate limits, or possible delays. Adequate but not rich.

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 succinct sentences: first sets purpose, second details types, third mentions output and efficiency. No wasted words, information density is high.

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 no output schema, description provides minimal detail on return format ('paired data + URIs'). Agent can infer structure but might need more specifics on how results are organized. Adequate for a straightforward comparison 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?

Schema coverage is 100% (both parameters described in schema), baseline 3. Description adds value by explaining how type determines the data returned and giving examples for values, enhancing understanding beyond schema alone.

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 'Compare' and specifies the entities (companies or drugs) and the exact data fields returned for each type. It differentiates from sibling tools which are focused on HR, memory, or single entity lookups.

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 the tool is for side-by-side comparison and mentions efficiency gains, but does not explicitly state when to use versus alternatives or provide exclusions. Guidance is present but could be more explicit.

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 mentions it 'Returns the most relevant tools with names and descriptions,' giving a clear expectation of what the output contains. With no annotations provided, this is valuable behavioral context. It could further explain if the search is semantic or keyword-based, but the current disclosure is strong.

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 long and front-loaded with the core action. It is concise but could be slightly more structured; however, every sentence adds value without 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 (2 parameters, no output schema, no nested objects), the description is complete. It covers the search intent, usage context, and expected results. The sibling list and context signals further support that no additional information 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 coverage is 100%, so the baseline is 3. The description adds context for the query parameter by providing example natural language queries (e.g., 'analyze housing market trends'), which helps the agent understand the expected input format. It also mentions the limit parameter's default and max values, supplementing the schema.

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+resource combination: 'Search the Pipeworx tool catalog' and clearly states its purpose to find tools by describing needs. It distinguishes itself from siblings like 'ask_pipeworx' by specifying that it searches for tools, not answers questions.

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 FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance and prioritization context, which is especially valuable given the large number of 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?

With no annotations, the description carries full transparency burden. It discloses the returned data sections and URI format, and notes it replaces 10-15 calls, but omits details like latency, rate limits, or read-only status.

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?

Four sentences front-loading the main purpose and key details. Efficient and no wasted words, though a slightly more structured format could improve scannability.

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 2 parameters, no output schema, and no annotations, the description covers output composition, parameter constraints, and usage alternatives. Minor gap: expected output format beyond URIs is implied but not detailed.

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% (baseline 3). The description adds significant context: type only supports 'company' with future plans, and value explains ticker/CIK formats and directs to resolve_entity for names, far exceeding the bare schema.

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 full profile of an entity, lists specific data sources (SEC filings, XBRL, patents, news, LEI), and explicitly contrasts with sequential calls, making the purpose unambiguous.

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 provides when to use (comprehensive entity profile) and when not to (for federal contracts, use usa_recipient_profile; for names, use resolve_entity first), offering clear 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?

No annotations provided, so description must cover behavioral traits. It states deletion by key but lacks details on persistence, irreversibility, or side effects. Adequate for a simple delete operation.

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?

Single sentence, perfectly concise, front-loaded with action and object. No unnecessary words.

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 is a simple single-parameter delete with no output schema or nested objects, description is mostly complete. Could mention confirmation or error behavior, but not critical.

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% (key described in both schema and description). Description adds no extra meaning beyond schema, so baseline 3.

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?

Description uses specific verb 'Delete' and resource 'stored memory by key', clearly distinguishing from siblings like 'recall' (retrieve) and 'remember' (store).

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 explicit when-to-use or when-not-to-use guidance. Sibling tools like 'recall' and 'remember' imply complementary operations, but description doesn't guide selection.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral context: fetches page, extracts title/description/links, outputs markdown. No contradictions.

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, front-loaded with main purpose. Ideal for quick agent scanning.

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 no output schema, description explains output format (text blob ready for site-root/llms.txt). Covers process and use cases, fully adequate for the tool's simplicity.

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 clear descriptions. Description does not add extra meaning beyond schema, so baseline 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 uses specific verb 'generate' and resource 'llms.txt file', clearly stating the tool's function. It distinguishes from unrelated sibling tools by specifying AI crawler indexing use case.

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?

Description explicitly lists three use cases: client indexing, own project drafting, competitor auditing. It does not mention when not to use or alternatives, but context is 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?

Discloses rate limit and free usage; no contradictory annotations. Could mention feedback is sent and not stored locally, but adequate for a simple write tool without 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?

Extremely concise: 2 sentences covering purpose, usage rules, and rate limit. Front-loaded with key 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?

Fully complete for a feedback tool: schema covers all params, description provides usage rules. No output schema 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 already covers all parameters with descriptions. Description adds minimal extra (e.g., message length limit), but mostly repeats schema info. Baseline 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?

Clearly states the tool is for sending feedback to Pipeworx team, listing specific use cases (bug reports, feature requests, missing data, praise) and distinguishing it from other tools like ask_pipeworx.

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?

Provides explicit guidance on what to include (describe attempted tools/data) and exclude (end-user prompt verbatim), plus rate limit (5/day). Implicitly contrasted with ask_pipeworx for questions.

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 annotations (readOnly, idempotent), description adds data source (CF analytics-engine), privacy (no PII), caching (5min-1h), and return structure (pack, tool, count). No contradictions.

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?

Single paragraph, front-loaded with main purpose, followed by use cases and extras. Every sentence adds value; no verbosity.

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 no output schema, description sufficiently outlines return structure (top tools, packs, total call volume) and shape. Caching and data source details complete the picture.

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?

Parameter 'window' fully described in both schema and description. Description adds context about trade-off between short and long windows. Schema coverage is 100%, but description enriches semantics.

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?

Description clearly states it returns trending tools, packs, and call volume over a window. Differentiates from siblings like ask_pipeworx by focusing on aggregate trends.

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?

Lists three explicit use cases for when to use, but lacks guidance on when not to use or alternatives. However, context is strong enough to guide agent.

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 a read-only operation (readOnlyHint=true, destructiveHint=false). The description adds significant behavioral detail: 'walks the child markets, extracts dates / thresholds from each question, sorts them, and reports any pair where the rule is violated.' It also specifies the return format (list of {market_a, market_b, gap_pp, suggested_trade}). No contradictions 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?

The description is relatively long but well-structured: it starts with the goal, explains the concept with an example, and then describes the process and output. Every sentence contributes to understanding. Could be slightly more concise, but it is not wasteful.

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 complexity of arbitrage detection and the lack of an output schema, the description is complete: it explains the monotonicity violation logic, the steps the tool performs, and the exact return format. No additional information is needed for an agent to use the tool correctly.

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 input schema already describes the 'event' parameter as a slug or URL. The description adds value by explaining how the parameter is used ('Pass a Polymarket event slug or URL; the tool walks the child markets...') and gives an example slug ('when-will-bitcoin-hit-150k'). This provides usable context beyond the schema.

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 purpose: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It explains the rationale with an example ('BTC hit $150k by May 31' vs 'Jun 30'), and distinguishes itself from the sibling 'polymarket_edges' by focusing on arbitrage detection.

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 implicitly defines when to use this tool: when the user has a Polymarket event with multiple date- or threshold-based child markets. It does not explicitly list alternatives or exclusions, but the context is clear enough for an AI agent to decide.

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 readOnly and openWorld, and the description adds context about the computation (scans top markets, groups by asset, fetches price history once, computes model probability). It discloses reliance on external data sources (FRED, coinpaprika) and the output structure (edge magnitude, trade direction). No contradictions 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?

The description is front-loaded with the main purpose and efficiently explains the V1 scope, process, output, and intended use. While slightly verbose, every sentence contributes necessary context without 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 complexity (multiple data sources, model computation), the description adequately explains the approach and output. No output schema exists, but the return values (top N with edge and direction) are described. Some limitations (e.g., model assumptions, data freshness) are not mentioned, but overall sufficient.

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?

All parameters are described in the schema (100% coverage), so the description adds limited new semantic value beyond stating defaults and the overall process. The description reinforces the parameters' roles but does not provide deeper 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 clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price, specifically for crypto-price bets using a lognormal model. It differentiates itself from siblings by specifying its unique methodology (lognormal model, FRED, coinpaprika) and its purpose of discovering betting opportunities.

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 targets the 'what should I bet on today' question, giving a clear use case. However, it does not directly compare to sibling tools like polymarket_arbitrage or bet_research, missing an opportunity to guide when to use this tool over 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, and the description adds context about typical spread ranges (2-25pp) and return format (probabilities and spread). It does not contradict annotations and provides useful behavioral insight beyond the structured fields.

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 moderately lengthy but well-structured: purpose, context, modes, return format. Every sentence adds value and there is no redundancy. It could be slightly tightened but remains effective.

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 no output schema, the description adequately explains return values (leg-by-leg prices, spread). It covers the two main usage scenarios and provides necessary context for a read-only, idempotent tool with optional parameters. Minor gaps in edge cases are acceptable.

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%, so baseline is 3. The description adds significant value by explaining the two modes in detail, listing exact topic shortcuts, and clarifying that explicit parameters override topic-mapped sides. This goes well beyond the schema's parameter 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 the tool computes a cross-venue spread between Kalshi and Polymarket, with specific verb 'cross-venue spread' and resource mentioned. It distinguishes from sibling tools like polymarket_arbitrage by explicitly focusing on two-venue comparison.

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 explains two modes ('topic' for pre-mapped shortcuts, explicit for custom pairings) and provides context on when each is appropriate. It does not explicitly state when not to use or mention alternative tools, but the guidance is clear enough for effective selection.

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, the description clarifies behavior: key optional, listing all if omitted. Indicates memory persistence across sessions. No contradictions.

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 clear sentences, front-loaded with primary action, then listing behavior and usage context. No wasted words.

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 1 optional param, no output schema, and simple behavior, description is nearly complete. Could mention return format (string?) but not critical.

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 100% but description adds meaning: 'key to retrieve (omit to list all keys)' explains optionality and listing behavior beyond schema.

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 retrieves a memory by key or lists all memories. The verb 'Retrieve' and resource 'stored memory' are specific, and the description distinguishes from 'remember' and 'forget' siblings.

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 use: 'Retrieve context you saved earlier'. Does not exclude scenarios, but no explicit mention of when not to use or alternatives beyond implicit differentiation.

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, the description carries the full burden and explains that the tool fans out to multiple sources in parallel, returns structured changes, a count, and pipeworx:// URIs. It also clarifies the 'since' parameter format. While it doesn't mention rate limits or auth needs, the behavioral description is thorough for a read-only monitoring 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?

The description is one concise paragraph that logically flows: purpose, fan-out explanation, parameter details, return structure, and use cases. No redundant or unnecessary words.

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 no output schema, the description explains return values (structured changes, count, URIs) and parameter formats. It could be improved by mentioning potential limitations like result truncation or pagination, but it is largely complete for typical monitoring tasks.

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%, and the description adds value by explaining that 'since' accepts relative formats like '7d', that 'value' can be a ticker or CIK, and that 'type' is currently limited to 'company.' This goes beyond the schema comments.

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?

Description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the entity type (company) and the data sources (SEC EDGAR, GDELT, USPTO), making it distinct from siblings like 'entity_profile' and 'compare_entities.'

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 includes explicit use cases: 'brief me on what happened with X' or 'change-monitoring workflows.' It also explains the 'since' parameter formats. However, it does not provide guidance on when not to use the tool or compare it to alternatives like 'entity_profile.'

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 provided, so description must carry the burden. It discloses memory persistence duration (24 hours for anonymous) and authentication benefits, but doesn't mention potential size limits, overwrite behavior, or whether keys are case-sensitive. Adequate but not comprehensive.

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 with no wasted words. The first sentence defines the action and resource, the second provides usage context and persistence details. Front-loaded with core purpose.

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 key-value store with 2 parameters and no output schema, the description covers purpose, usage guidelines, and key behavioral traits (persistence). Lacks mention of overwrite behavior or value size limits, but overall complete for the tool's complexity.

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% with examples for both key and value, so the description adds little beyond restating the purpose. Baseline 3 is appropriate as schema already documents parameters well.

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 stores a key-value pair in session memory, with specific verbs and resources. It differentiates from siblings like 'recall' (retrieves) and 'forget' (removes).

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 explicit guidance on when to use (save intermediate findings, user preferences, context across calls) and notes persistence differences for authenticated vs anonymous users, helping the agent decide contextually.

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 fully disclose behavioral traits. It describes a read-only resolution and discloses return fields, but does not explicitly state that it is safe, idempotent, or free of side effects. The information given is adequate but not exhaustive.

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 very concise at three sentences, front-loading the purpose, then detailing the supported type and inputs, and ending with a benefit statement. Every sentence adds value without 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, the description sufficiently covers input formats, return values, and its advantage over multiple calls. It does not discuss error handling or edge cases, but for a v1 tool with this complexity, it provides adequate 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 input schema already covers both parameters with descriptions, including examples for 'value'. The main description adds no new meaning beyond the schema, so it meets the baseline for high schema 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 the tool resolves entities to canonical IDs, specifies the accepted input formats (ticker, CIK, name), and explains the return value. It distinguishes itself from sibling tools by focusing on entity resolution and mentioning it reduces multiple calls.

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 context on when to use the tool: for entity resolution to get canonical IDs efficiently. It mentions it replaces 2-3 lookup calls, implying it should be preferred over multiple separate calls, but does not explicitly mention when to avoid it or discuss alternatives among siblings.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds that it probes each entity, ranks, and surfaces most/least recognized, which is additional behavioral context beyond 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, front-loaded with main purpose. Every sentence adds value: purpose, mechanism, and example use case. No waste.

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 no output schema, description explains return structure (ranked list with score, confidence, signal density). Annotations cover behavioral traits. Complete for a comparison tool with this complexity.

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%, but description adds valuable context: first entity treated as 'subject', models omission defaults to workers-ai, _apiKey only needed if anthropic, context disambiguates names. This adds meaning beyond schema.

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 compares AI visibility across multiple entities side-by-side, using ai_visibility_check to probe each. It distinguishes from sibling tool 'ai_visibility_check' (which likely does single entity) and 'compare_entities' (more general). The specific verb+resource+scope is clear.

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 'Useful for competitive AI-marketing audits' with an example question. It implies usage context but does not explicitly state when not to use it or mention alternatives beyond the implied single-entity sibling.

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 provided, so description must reveal behavioral traits. It describes return format (verdict types, citation) and scope limitation. However, does not mention auth requirements, rate limits, or error handling for unsupported claims.

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, no wasted words. First sentence states core purpose, second lists capabilities and value. Front-loaded with essential info.

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?

No output schema, so description must explain return values, which it does (verdict types, citation format). Covers domain, sources, and substitution for sequential calls. Complete for a single-param 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?

Only one parameter 'claim' with 100% schema coverage. Description adds natural-language examples and context, but schema already describes it adequately. Baseline 3 applies.

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?

Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifies domain (company-financial for US public companies) and sources (SEC EDGAR + XBRL). It differentiates from siblings like compare_entities or entity_profile which serve 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 states it replaces 4–6 sequential agent calls, indicating when to use it. Also implicitly limits usage to company-financial claims via 'v1 supports company-financial claims', but does not fully enumerate when not to use.

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