Opencellid

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 automatically picks the right source and returns the result. However, it does not mention error behavior (e.g., if the question cannot be answered), auth requirements, rate limits, or latency. The description is adequate for a query tool but lacks deeper behavioral 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 concise with two sentences and a few examples. It starts with the core purpose, then provides usage guidance, and ends with examples. No unnecessary words; every sentence adds value.

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 (routing across 300+ sources) and lack of output schema, the description covers the main functionality well. It explains the automatic routing and provides examples. However, it does not describe the return format (e.g., text, JSON) or what happens on failure. Slightly incomplete but sufficient for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'question' is fully described in the schema with description 'Your question or request in natural language'. Schema coverage is 100%, so the baseline is 3. The description adds examples of usage but does not add new semantic meaning beyond the schema. Thus, score remains at 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?

The description clearly states it answers natural-language questions by automatically selecting the right data source. It provides a specific verb ('answer') and resource ('natural-language question'), and differentiates from siblings by mentioning routing across 300+ sources, while sibling tools are more specific (e.g., get_cell, compare_entities). Examples further clarify the purpose.

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 'Use when a user asks...' and provides concrete examples of queries. It implies that if you know the specific tool, you could use that instead, but does not explicitly state alternatives or when not to use it. This provides clear context for usage, though exclusions are missing.

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 burden. It discloses the capping behavior of OpenCellID and suggests narrowing the bbox, but does not mention authentication, rate limits, or error handling. The disclosed trait is helpful for effective use.

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 concise sentences with no wasted words. The key information is front-loaded: what the tool does, coordinate format, and important limitation.

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 bounding box query with optional filters and server cap, the description covers core behavior and a key limitation. No output schema, but return type (list of towers) is implicit. Could mention what fields are returned, but adequate overall.

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 context by repeating the bounding box format and mentioning the limit default and server cap, but does not add beyond schema for mcc and mnc. Value is marginal.

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 lists cell towers inside a bounding box, with a specific verb 'List', resource 'cell towers', and constraint 'inside a bounding box'. It distinguishes from sibling tools like 'get_cell' which likely retrieves a single cell.

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 the bounding box format and warns about result caps from OpenCellID, advising to narrow the bounding box for more detail. However, it does not explicitly state when not to use this tool or compare it to alternatives like 'get_cell'.

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 carries full burden. Discloses data sources, return includes paired data and URIs, and replaces multiple calls. Lacks explicit mutability or authorization details, but covers main behaviors.

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 with front-loaded purpose, usage triggers, type details, and return info. No waste, each sentence adds value.

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?

Covers purpose, usage, data sources, and return broadly. No output schema, but description specifies data fields. Could include more on exact return format, but sufficient for agent decision-making.

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 substantial meaning: explains what each type returns (revenue, net income for company; adverse events, approvals for drug), gives examples, and enriches 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 the tool compares 2-5 companies or drugs side-by-side, with specific verbs and resource types. It distinguishes from sibling tools like entity_profile by noting it replaces 8-15 sequential 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?

Provides explicit examples of when to use (user says 'compare X and Y', 'X vs Y', etc.). Does not explicitly state when not to use or mention alternatives, though sibling names are available.

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. Description implies read-only behavior (browse, search, look up) but does not explicitly confirm that no state changes occur or describe any side effects like logging or rate limits.

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?

Description is front-loaded with purpose and action, followed by examples. Slightly verbose but efficient; every sentence serves a 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?

Given two parameters, no output schema, and a simple return format, the description fully covers what the tool does, when to use it (first call), and how to use the parameters. No missing 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?

Schema covers both parameters with descriptions. Description adds value by clarifying return format (top-N with names+descriptions) and providing example queries, which helps agents craft effective queries.

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 finds tools by describing data/task, lists many example domains, and notes it returns top-N relevant tools. This clearly distinguishes it from sibling tools, which are specific domain 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?

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set.' Provides usage examples but does not explicitly state when not to use it.

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 fully disclose behavioral traits. It lists returned data types (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. However, it does not explicitly state read-only nature or any side effects, though implied.

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-loaded with purpose and usage. Each sentence adds distinct value: purpose, examples, return data, parameter guidance. 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?

No output schema, so description must and does explain return values comprehensively. Lists all returned data categories and mentions citation format. For a tool with two simple parameters, the description is complete and actionable.

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 covers both parameters fully (100% coverage). Description adds extra value with examples ('AAPL', '0000320193') and notes type enum only supports 'company' with future plans. Baseline is 3 due to high coverage; marginal improvement earns a 4.

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 'Get everything about a company in one call', specifying the verb 'get' and the resource 'everything about a company'. It distinguishes from siblings by noting it replaces multiple pack tool calls, providing concrete usage examples.

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?

Explicit usage context: 'Use when a user asks...' with example queries. Also states when not to use: 'Names not supported — use resolve_entity first if you only have a name.' Provides 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 the description carries full burden. It indicates a destructive action but does not disclose what happens if the key does not exist, whether deletion is permanent, or any permission requirements. Some additional behavioral context is missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences, each purposeful: action, usage guidelines, and context with siblings. No extraneous text, well front-loaded.

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 one parameter and no output schema, the description covers the core purpose, usage context, and tool relationships. Minor omission of error handling or return value, but overall adequate.

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 a single required 'key' parameter described as 'Memory key to delete.' The description adds 'previously stored' but does not significantly enhance understanding beyond the schema definition.

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 explicitly states the action: 'Delete a previously stored memory by key.' It uses a specific verb and resource, and distinguishes itself from siblings like 'remember' and 'recall' by being the deletion counterpart.

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 scenarios for use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also suggests pairing with related tools, giving clear guidance on when to use versus 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, but the description does not disclose behavioral traits like rate limits, data freshness, or whether it is read-only. It implies a query but lacks explicit safety information.

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 concise sentences, front-loads the main action, no filler. Could possibly add more structure (e.g., bullet points for parameters) but still 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?

No output schema, but description lists return fields. However, lacks detail on return format (e.g., how lat/lon are represented). Adequate for a simple lookup but not fully 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?

Schema coverage is 100%, but description adds context: explains MCC/MNC with examples, defines LAC and cell_id in context, and mentions radio type as optional disambiguation. Adds value 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?

Clearly states the action ('Geolocate a cell tower') and lists the required identifiers (MCC, MNC, LAC, cell_id) and output fields (lat/lon, range, samples, radio type). Distinguishes from siblings like 'cells_in_area' by focusing on a single cell.

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 guidance on when to use this tool versus alternatives (e.g., 'cells_in_area'). Does not mention prerequisites or limitations.

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?

Given no annotations, the description discloses key behaviors: rate-limited to 5 per identifier per day, free, does not count against tool-call quota, and that the team reads digests daily. It does not mention what happens after submission (e.g., confirmation), but for a feedback tool this is acceptable.

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 well-structured paragraph. It front-loads the core purpose, then provides usage conditions, format guidelines, and limitations. Every sentence serves a purpose, with no filler. Length is appropriate for the complexity.

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 and moderate complexity, the description covers all essential aspects: purpose, when to use, parameter semantics, rate limits, and team response. It could optionally mention a confirmation message, but the current content is sufficient for an agent to invoke 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the enum options for 'type' in context of when to use each, and provides guidance on what to include in 'message' (be specific, 1-2 sentences, 2000 chars max). It also clarifies that 'context' is optional and what fields it contains.

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 explicitly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It lists specific use cases (bug, feature, data_gap, praise) and is distinct from all siblings, which are query or analysis 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 when-to-use guidance for each feedback type, instructs users to describe issues in terms of Pipeworx tools/packs, and explicitly advises against pasting end-user prompts. It also mentions rate limits and that the tool is free and quota-free.

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, so description carries burden. Discloses it's a read (retrieve/list), scoped to identifier, and can be used without re-deriving. 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?

Five sentences, front-loaded with purpose. Each sentence adds value—usage, scope, pairing. Efficient with no fluff.

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?

Complete for a simple tool with 1 optional param and no output schema. Covers behavior, scope, and sibling relationships. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter 'key', fully described in schema. Description adds real-world examples (ticker, address, notes) beyond schema, providing extra semantic context.

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 retrieves a saved value or lists keys (omit key). Distinguishes from siblings 'remember' and 'forget' by referencing them explicitly.

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 concrete examples like ticker, address, notes. Mentions scoping to identifier and pairing with remember/forget. Lacks explicit when-not, but context is sufficient.

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, but the description fully discloses behavior: fanning out to SEC EDGAR, GDELT, and USPTO in parallel, accepting ISO dates or relative shorthand, and returning structured changes with citation URIs.

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 well-structured, with the main purpose front-loaded and each sentence adding value. No unnecessary 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?

Given the tool has no output schema, the description adequately describes the return format. Parameter descriptions are complete. However, it lacks mention of error handling or limitations, making it slightly less 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 input schema has 100% coverage and already describes each parameter well. The description adds minor context (e.g., relative shorthand examples for 'since') but does not significantly augment 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 clearly states the tool's purpose: retrieving recent changes for a company. It provides multiple example queries and distinguishes 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 explicitly tells when to use the tool with example user queries. It does not mention when not to use or alternative tools, but the guidance is sufficient for 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, description fully discloses storage as key-value pair, scoping by identifier, persistence differences (authenticated vs anonymous, 24h retention). 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?

Four sentences, front-loaded with purpose, each sentence adds essential info. No redundancy or fluff.

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?

Covers input, behavior, persistence, and sibling relationships. Lacks explicit return value description, but for a simple store operation, it is sufficient for correct usage.

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 param descriptions. Description adds context on key naming conventions and that value can be any text, adding value 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?

Description clearly states 'Save data the agent will need to reuse later' and provides specific examples. Differentiates from siblings recall and forget by explicitly pairing with them.

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 says 'Use when you discover something worth carrying forward' and lists example scenarios. Also guides pairing with recall and forget for retrieval and deletion.

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 must convey behavior. It states the tool returns IDs and citation URIs, implying a read-only operation. However, it does not mention error behavior (e.g., if the entity is not found) or any side effects, leaving some uncertainty.

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 at ~5 sentences, front-loads the purpose, provides concrete examples, and each sentence contributes meaning. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 required params, no output schema), the description covers inputs, outputs, and usage order. It does not specify what happens on failure, but that is acceptable for a lookup 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%, so baseline is 3. The description adds value by explaining that 'type' maps to company or drug and giving illustrative examples for 'value' (ticker, CIK, name). This goes beyond the schema's enum description.

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 vague names into canonical identifiers (CIK, ticker, RxCUI, LEI). Examples like 'Apple' and 'Ozempic' clarify the scope. The sibling list suggests no other tool duplicates this function.

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 advises using this tool before others needing official identifiers, and notes it replaces 2–3 separate lookup calls. However, it does not explicitly exclude cases where the identifier is already known.

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 responsibility. It discloses that the tool returns a verdict (confirmed, approximately_correct, refuted, inconclusive, unsupported), extracted structured form, actual value with citation, and percent delta. It also mentions data sources (SEC EDGAR + XBRL) and efficiency benefits. Missing details on authorization or rate limits, but overall transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with action verbs, and every sentence adds value: purpose/usage, domain limitation, and output/efficiency. No unnecessary verbiage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input (one parameter) and no output schema, the description covers purpose, usage, expected output format, domain constraints, and efficiency. It is complete for the tool's complexity, though details on edge cases or error handling are absent.

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 one well-described parameter. The description adds natural-language context and examples, but does not significantly extend beyond 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 tool's purpose: fact-check, verify, validate, or confirm/refute a factual claim against authoritative sources. It specifies the domain (company-financial claims for US public companies) and distinguishes itself from siblings by replacing multiple sequential 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 examples and says 'Use when an agent needs to check whether something a user said is true.' However, it does not explicitly state when not to use it or mention alternatives beyond implying it replaces other tools. The sibling list is available but not referenced in guidelines.

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