Pipedrive

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds significant context: default free model, BYO key for Anthropic with direct billing, and exact return format per model plus combined view. No contradiction.

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, then defaults, then return structure, then use cases. Every sentence is essential and well-structured. 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 4 parameters, no output schema, and presence of sibling tools, the description covers all inputs, defaults, usage patterns, and output structure. Annotations are rich. The description is fully adequate 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%, but the description adds meaning beyond the schema by clarifying default model behavior ('omit for just workers-ai'), the conditional nature of _apiKey ('only needed if anthropic in models'), and practical usage of context parameter. Slightly better than baseline.

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 ('probe'), the resource ('one or more LLMs'), and the output ('score visibility 0-100 per model'), distinguishing it from siblings like scan_competitor_ai_presence by specifying per-model returns and default model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and provides guidance on default model and optional API key, but does not mention when not to use or differentiate from nearby 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 are provided, so description carries full burden. Describes high-level behavior (picks tool, fills arguments, returns result) but lacks details on potential limitations, such as what happens if no data source matches, error handling, or rate limits. 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?

Description is concise (3 sentences) and front-loaded with the core purpose. Examples add useful context but could be considered slightly redundant for a simple tool. No wasted words, though could be trimmed without losing meaning.

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 a single parameter and no output schema, the description is sufficiently complete for the agent to understand its purpose and how to use it. Provides enough context about delegation behavior and examples. Minor gap: does not explain what 'best available data source' means or if there are any prerequisites.

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 (question) with 100% schema coverage. Schema already describes 'Your question or request in natural language'. Description adds value by explaining that the question should be in plain English and provides examples, which clarifies the expected format beyond the schema 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?

Description clearly states verb+resource: 'Ask a question... and get an answer from the best available data source.' It explicitly distinguishes from siblings by highlighting that Pipeworx handles tool selection and argument filling, so the agent does not need to browse other tools or schemas.

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 clear context for when to use: when you have a plain English question and want the system to pick the right tool. Does not explicitly state when not to use or name alternative tools, but examples and description imply it handles broad queries that would otherwise require navigating multiple 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?

The description fully discloses internal behavior: market resolution, bet classification, dynamic fan-out to appropriate data packs, and output structure. It aligns with annotations (readOnlyHint, openWorldHint, destructiveHint) and adds context beyond them, such as the fan-out logic and comparison output.

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 (4 sentences) with each sentence serving a purpose. It front-loads the core action and then adds details. Slightly longer due to complexity, 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?

Despite lacking an output schema, the description thoroughly explains the return value (evidence packet + market-vs-model comparison) and internal processing. It covers all necessary aspects for an agent to correctly invoke the tool.

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

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

The description enhances the input schema by explaining the acceptable formats for 'market' (slug, URL, question text) and detailing the 'depth' enum values (quick vs thorough). Schema coverage is 100%, but the description adds valuable context beyond 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 researches Polymarket bets by pulling Pipeworx data, with specific input formats and output details. It distinguishes itself from siblings by being the core demo product for bet-specific context, avoiding the need for agents to discover data packs manually.

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 use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and implies it's superior to general pack discovery. However, it does not explicitly state when to avoid using it or mention alternative tools for non-Polymarket queries.

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 fully disclose behavior. It mentions data sources (SEC EDGAR, FDA) but does not state whether the tool is read-only or has any side effects. This is a significant gap for a tool that likely performs network requests.

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 with no wasted words. The action ('Compare 2-5 entities side by side') is front-loaded, and the description efficiently covers purpose, data sources, and efficiency benefit.

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

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

The description is complete for a comparison tool, covering input, output (paired data + URIs), and efficiency. However, no output schema exists, and the return structure is only vaguely described, which could be improved with explicit formatting details.

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, but the description adds meaning by detailing what data is returned per type (e.g., revenue for companies, trial counts for drugs). This enriches the schema's enum and array 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 entities side by side, with explicit differentiation between 'company' and 'drug' types. It distinguishes itself from sibling tools (e.g., pipedrive tools, memory tools) by its unique comparison functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states it replaces 8-15 sequential agent calls, indicating efficiency gains. However, it does not specify when not to use it (e.g., for single entity lookup) or provide explicit 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 states that the tool searches and returns the most relevant tools with names and descriptions, which is transparent. However, it does not disclose if there are any side effects 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?

Three sentences, each serving a purpose: what the tool does, what it returns, and when to use it. 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?

Given the tool's simplicity (2 params, no output schema), the description covers the necessary information: purpose, usage context, and parameter hints. The context signals indicate 100% schema coverage, so the description complements it well.

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 the description adds value by explaining the query parameter as a 'natural language description' with examples. It also mentions defaults and max for the limit parameter. However, the description does not elaborate beyond what the schema says.

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: searching a tool catalog by describing what you need, and returning relevant tools with names and descriptions. It distinguishes itself from sibling tools by being a meta-tool for discovering other 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 to call this FIRST when there are 500+ tools available to find the right ones, providing clear when-to-use guidance. Although it does not specify when not to use it, the context of having many tools makes this appropriate.

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 discloses it returns citation URIs, consolidates multiple calls, and lists included data types. Sufficiently transparent about behavior, though missing explicit readOnly/destructive hint.

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?

Concise three-sentence description with front-loaded purpose. Every sentence adds essential information without 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?

Given no output schema and moderate complexity, the description adequately covers return format (URIs), included data, and alternatives. Could mention pagination or rate limits, but not necessary for this scope.

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 the description adds meaning: clarifies type only supports 'company', value can be ticker or CIK, and warns that names are unsupported (use resolve_entity). 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?

The description clearly states it retrieves a full entity profile across multiple Pipeworx packs, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes from sibling usa_recipient_profile for federal contracts.

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 guidance: when to use (comprehensive entity profile), when not to use (federal contracts → usa_recipient_profile; name only → resolve_entity first). Provides clear context for alternative tool 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?

No annotations provided, so description must carry full burden. It states it deletes but doesn't disclose if deletion is permanent, reversible, or if confirmation is needed. Lacks behavioral details.

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 wasted words. Front-loaded with action and resource.

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 low complexity (1 required param) but no output schema or annotations, the description is complete for the action but lacks safety and side-effect context expected for a deletion 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% with a clear description for 'key'. Description adds no extra meaning beyond the 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?

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

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; no mention of prerequisites or conditions. The description is minimal and does not help the agent 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds behavioral context (fetches page, extracts title/description/key links, emits markdown) without contradicting annotations. It could mention error handling but remains adequate.

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 core action and output format. Every sentence adds value, with 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 the two-parameter input and absence of output schema, the description covers the essential behavior (fetch, extract, emit) and use cases. It does not address error scenarios or URL validation, but the tool is straightforward enough that this is 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 parameters are fully documented in the schema. The description restates the url and max_links purpose without adding significant new meaning beyond what the schema already provides.

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: generate a production-ready llms.txt file for any URL, specifying the output format and target audience. No sibling tool serves this function, so differentiation 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 lists use cases (getting a client's site indexed, drafting for own project, auditing competitors), providing clear context. It does not include explicit exclusions or alternatives, but the task is well-defined and distinct from 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?

No annotations provided, so description must cover behavioral traits. It does not mention that the tool requires a valid API key, the format of the response, or any potential errors (e.g., deal not found). Simply states 'Get a single deal by ID' without additional 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?

Single sentence, no wasted words. Front-loaded with action and resource. Could include a brief note about response or prerequisites without losing conciseness.

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

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

Tool has two required parameters (API key and deal ID) and no output schema. Description doesn't mention that an API key is needed or what the response contains. For a simple retrieval tool, more context about authentication and return format would be helpful.

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 schema already documents both parameters. Description adds no extra meaning beyond what's in 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?

Description clearly states the action (Get) and resource (single deal by ID) from a specific source (Pipedrive). Distinguishes from sibling tools like pipedrive_list_deals and pipedrive_search which handle multiple deals or search functionality.

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. Siblings like pipedrive_list_deals and pipedrive_search exist, but description doesn't mention them or indicate that this tool is for fetching a specific deal by 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?

No annotations are provided, so the description must disclose all behavioral traits. It correctly states it is a read operation (get) and returns a single person. It does not mention what happens if the ID is invalid or missing, or any authorization details beyond the API key parameter. This is adequate but minimal.

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 that front-loads the core action and resource. 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 the tool's simplicity (2 required parameters, no output schema, no nested objects), the description is reasonably complete. It identifies the tool's purpose and key input. However, it does not describe the return value or error conditions, which would be helpful for 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 description coverage is 100% (both 'id' and '_apiKey' have descriptions). The description adds no additional meaning beyond what the schema already provides. The baseline of 3 is appropriate since the schema does the heavy lifting.

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 clear verb-resource pair: 'Get a single person (contact) by ID from Pipedrive.' It specifies the resource (person/contact), the action (get), and the source (Pipedrive). This distinguishes it from sibling tools like pipedrive_list_persons and pipedrive_search, though it could be slightly more explicit about being a single-item retrieval.

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 is given. However, the description implies it is for retrieving a specific person when the ID is known. Given sibling tools like pipedrive_list_persons (for listing) and pipedrive_search (for searching), a savvy agent might infer usage, but there is no direct comparison.

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 that this is a read-only operation, pagination behavior (beyond schema), rate limits, or any side effects.

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

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

The description is a single concise sentence. It is front-loaded and free of extraneous information.

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

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

The tool has 4 parameters, no output schema, and no annotations. The description fails to explain return format, sorting, or any limitations (e.g., max results beyond schema).

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

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

Schema coverage is 100% with detailed descriptions for each parameter (limit, start, status, _apiKey). The description adds no extra meaning beyond the 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 clearly states the action ('list') and the resource ('deals from Pipedrive CRM'). It distinguishes from siblings like 'pipedrive_get_deal' (single deal) and 'pipedrive_search' (search across 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?

No guidance on when to use this tool versus alternatives like 'pipedrive_search' or 'pipedrive_get_deal'. No mention of prerequisites or context.

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

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

No annotations are provided, so the description carries full burden. It correctly implies a read-only operation ('List'), which aligns with expected behavior. However, it does not mention any rate limits, pagination behavior beyond what's in the schema, or any side effects. Since there are no annotations to contradict, a mid score is appropriate.

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?

One short sentence, no waste. It is front-loaded with the verb. Could be slightly improved by adding a brief usage note, but it is appropriately 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 that the tool is a simple list operation with a schema covering all parameters and no output schema, the description is adequate. It tells the agent what it does. However, it could mention that results are paginated or that it returns all persons by default, which is partially covered by the schema but not explicitly stated in the description.

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 baseline is 3. The description adds no parameter information beyond the schema (e.g., no mention of optional filters or sorting). It simply restates the resource type, which is already in the name.

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 'List' and resource 'persons (contacts) from Pipedrive', distinguishing it from sibling tools like pipedrive_get_person (get one person) and pipedrive_list_deals (list deals). It is specific and leaves 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?

No guidance on when to use this tool versus alternatives. For example, it does not explain when to use list_persons vs search (sibling) for finding persons. It also lacks context about typical use cases or prerequisites beyond the API key.

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 must disclose behavioral traits. It does not mention that the tool requires an API key, or that it searches all item types by default. The description is minimal and omits important context like pagination or result format.

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 a single sentence, front-loading the core purpose. However, it could include more detail without becoming 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 complexity (4 params, no output schema, no annotations), the description is insufficient. It lacks guidance on result structure, pagination (limit param), or how item_types affects behavior. The description barely adds value over the tool name.

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. However, the description adds no value beyond the schema—it does not explain how parameters like 'term' or 'item_types' interact. The description is generic and does not enhance understanding of parameter usage.

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 searches across multiple entity types in Pipedrive, using a specific verb ('search') and listing the resources. It distinguishes from siblings like pipedrive_get_deal or pipedrive_list_persons by its broad search capability.

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 use for cross-entity searches but does not explicitly state when to use this tool versus specific get/list tools. No exclusion criteria or alternatives mentioned.

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

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

With no annotations provided, the description carries the full burden of behavioral transparency. It discloses the rate limit (5 messages per identifier per day), states that it is free, and specifies content guidelines (describe in terms of Pipeworx tools, no verbatim prompts). This is adequate for a feedback tool, though it could mention what happens after submission (e.g., acknowledgment).

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: first states the main action, second lists use cases, third adds constraints and guidelines. It is concise, front-loaded, and contains no unnecessary words. Every sentence contributes 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 simplicity (sending feedback) and the lack of output schema, the description covers essential aspects: purpose, when to use, parameter guidelines, and constraints. It does not mention return values or typical response, but for a feedback tool, this is a minor omission. Overall, it is sufficiently complete for an AI agent to use 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 has 100% description coverage, but the description adds meaningful behavioral guidance for the parameters. For the 'type' parameter, it reinforces the enum options. For 'message', it adds advice on specificity and length ('1-2 sentences typical, 2000 chars max'), which goes beyond the schema description. This extra context improves an AI agent's ability to use the tool correctly.

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: sending feedback to the Pipeworx team. It lists specific use cases (bug reports, feature requests, data gaps, praise) and distinguishes it from sibling tools, none of which serve a similar 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 says when to use the tool (for the listed feedback types) and provides important guidelines: describe using Pipeworx tools/data, avoid including the end-user's prompt verbatim, and notes the rate limit of 5 messages per day. It does not explicitly state when not to use it, but the tool is self-contained and no sibling serves as an alternative for feedback.

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 adds significant context beyond the annotations: data is self-aggregating from CF analytics-engine, contains no PII, and is cached for 5 minutes to 1 hour. This complements the readOnlyHint and idempotentHint annotations perfectly.

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 core output, then lists use cases, then additional details. At ~100 words, it's efficient but could trim the use-cases list slightly. Still earns a 4 for effective structure.

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 (top tools, top packs, count) and mentions caching behavior. It covers all necessary context for a simple read-only tool with one parameter.

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

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

The schema covers 100% of parameters (only 'window'), with an enum and description. The description repeats the enum values and adds context about window duration trade-offs, but doesn't provide additional meaning 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 returns 'top tools, top packs, and total call volume' over a specified window, using specific verbs like 'returns' and 'derived'. It uniquely identifies the resource (trending data from Pipeworx) and distinguishes from sibling tools 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?

Three explicit use cases are provided: discovering hot data sources, confirming canonical tool choice, and aligning use case with agent needs. While it doesn't specify when not to use it, the context is clear and actionable.

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 the annotations (readOnlyHint, openWorldHint, destructiveHint), the description adds behavioral details: how monotonicity checking works, the two modes, and cross-event search capabilities. 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 concise yet thorough, with a clear structure separating the two modes. Every sentence contributes meaningfully, with no redundancy.

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

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

Despite lacking an output schema, the description mentions return values (ranked opportunities with reasoning). It covers the tool's complexity (two modes) and usage context adequately.

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 has 100% coverage with descriptions for both parameters. The description adds value by explaining the modes, but does not provide new semantic detail 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 finds arbitrage opportunities on Polymarket by checking monotonicity violations, and distinguishes two modes (event and topic). It is specific and stands out from sibling 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 explicitly explains when to use event mode vs topic mode, including an example of when topic mode is necessary. It provides clear context for each scenario.

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 provides detailed behavioral context: V1, crypto-price bets, lognormal model from FRED + coinpaprika, grouping by asset, computing model probability, and ranking by edge. Annotations already indicate readOnly, openWorld, non-destructive, so description adds extra depth.

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 well-structured and front-loaded with the main purpose. It includes necessary details without being overly verbose, though it could be slightly more 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 no output schema, the description clearly states the return value (top N ranked with suggested trade direction). It covers the overall workflow, model sources, and usage context, making it complete for the 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?

Schema coverage is 100% with adequate parameter descriptions. The tool description reinforces defaults (limit=10, window=1wk, min_edge_pp=0.5) but adds no significant meaning beyond the schema, thus 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?

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, distinguishing it from siblings like polymarket_arbitrage or bet_research.

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, indicating when to use it. However, it does not mention when not to use or compare to alternatives, leaving some ambiguity.

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, openWorldHint, idempotentHint, and destructiveHint=false. Description adds value by explaining the pricing difference (2-25pp spread) and the two modes, and outlines return fields (prices, spread). No contradiction 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?

Description is structured with a clear opening sentence, an explanatory sentence about the arb signal, and a detailed sentence on the two modes. Every sentence adds value; no redundancy. Slightly long but well-organized and 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?

Given the complexity (3 parameters, two modes, return with multiple fields) and no output schema, the description adequately covers purpose, modes, parameters with examples, and return structure (prices and spread). Could elaborate on 'leg' interpretation but is sufficient for agent use.

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 each parameter described. Description adds meaning beyond schema by listing the topic values with examples, explaining that explicit tickers override topic, and clarifying the return format (leg prices and spread).

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 identifies the tool as a cross-venue spread calculator between Kalshi and Polymarket, uses specific verbs ('cross-venue spread'), and names the resources and the arb signal. It distinctly differentiates from siblings like polymarket_arbitrage by explicitly naming both venues and the spread concept.

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 outlines two usage modes (topic vs explicit tickers) with examples, indicating when to use each. It implies context for usage (arbitrage signal) but does not explicitly state when not to use this tool versus alternatives like polymarket_arbitrage. Strong guidance but lacks exclusion criteria.

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

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

With no annotations provided, the description must carry the burden. It discloses that the tool retrieves stored memories and can list all if key is omitted. However, it does not mention any side effects (none expected for retrieval), permissions, or whether the data persists across sessions (it says 'previous sessions'). The description is straightforward but could benefit from mentioning that it is a read-only 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?

The description is a single sentence with no filler. It is front-loaded with the core action and includes the optional behavior in the same sentence. Every word is necessary.

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 (1 optional param, no output schema, no nested objects), the description is adequate. It covers the main use cases and clarifies the key omission behavior. No significant gaps remain.

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 provides 100% coverage with a description for the only parameter 'key'. The description adds context by explaining that omitting the key lists all memories. Since schema coverage is high, a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to retrieve a memory by key or list all memories. It specifies the action ('retrieve', 'list'), the resource ('memory'), and the optional behavior. This distinguishes it from sibling tools like 'remember' (which presumably stores memories) and 'forget' (which deletes).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also implies when to omit the key (to list all). However, it does not explicitly mention when not to use it or provide 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?

No annotations provided, so description carries full burden. It discloses parallel fan-out behavior, date format options, and return structure (structured changes, total_changes, URIs). It does not explicitly state non-destructiveness, but the query nature is evident. Slight gap in mentioning lack of side effects.

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

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

Description is concise: four sentences front-loaded with purpose, then details on behavior, date formats, and return. No redundancy or filler; every sentence contributes 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?

No output schema, but description explains return values (structured changes, count, URIs). Covers key behaviors: multiple sources, date parsing, and typical usage. Lacks mention of pagination or rate limits, but overall sufficient for a complex tool.

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

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

Input schema coverage is 100%, baseline 3. Description adds significant context: explains 'since' accepts ISO and relative formats with examples, 'value' can be ticker or CIK, and fan-out behavior for 'type'. This goes beyond the basic 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?

Description clearly states the tool retrieves recent changes for an entity since a given time, lists specific data sources (SEC EDGAR, GDELT, USPTO) for type 'company', and gives example use cases. This specificity distinguishes it from siblings like 'entity_profile' or '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?

Description explicitly recommends use for 'brief me on what happened with X' and change-monitoring workflows. While it doesn't list when not to use or alternative tools, the context is clear enough for an AI agent to select appropriately 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?

No annotations are provided, so the description must disclose behavior. It does so by mentioning persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'). This adds value beyond the input schema.

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

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

Three sentences, each adding value: what it does, when to use it, and a behavioral note. 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 the simple tool (store a key-value pair), the description is complete. It covers purpose, use cases, and behavioral notes (persistence). No output schema exists, but the tool is straightforward.

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 no further parameter details. Baseline of 3 is appropriate since the schema already documents the parameters clearly.

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 ('Store') and resource ('key-value pair in your session memory'), clearly distinguishing it from sibling tools like 'recall' (which retrieves) and 'forget' (which 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 says 'Use this to save intermediate findings, user preferences, or context across tool calls', providing clear use cases. It does not explicitly mention when not to use it, but the purpose is distinct from 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?

No annotations are provided, so the description must carry the burden. It describes the return values (ticker, CIK, name, URIs) but does not disclose any behavioral traits such as idempotency, rate limits, or side effects. While it implies a read-like operation, it lacks explicit safety guarantees.

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, using two sentences to convey the core purpose, supported types, input formats, and output. Every word contributes meaning, with no redundancy or filler.

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 two parameters and no output schema, the description adequately covers inputs, outputs, and usage context. It could be enhanced by mentioning error handling or required permissions, but the current level is sufficient for correct 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?

The input schema covers both parameters with descriptions. The description adds value by providing concrete examples (AAPL, Apple, 0000320193) and clarifying that the 'value' parameter accepts multiple identifier formats, which enhances understanding beyond the schema's generic string 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's purpose: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' It specifies the supported entity type (company) and the inputs (ticker, CIK, name), distinguishing it from siblings by noting it replaces multiple lookup 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 clear usage context: single call, supports company type, accepts various identifiers. It implies when to use (for company resolution) and mentions efficiency (replaces 2-3 calls), but does not explicitly state when not to use or compare with alternatives beyond the replacement note.

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 readOnly, openWorld, idempotent, and non-destructive. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns score, confidence, signal density. 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 four sentences, front-loaded with the main purpose, followed by mechanism, use case, and output. 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?

The description fully explains the tool's purpose, when to use, input, output format, and example. No missing information given the absence of an output schema.

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

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

Since schema coverage is 100% with good descriptions, baseline is 3. The description adds minimal extra meaning beyond summarizing the parameters' roles and the probing mechanism.

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 compares AI visibility across multiple entities side-by-side, ranks them, and identifies most/least recognized. It distinguishes itself from the single-entity sibling 'ai_visibility_check' by focusing on multi-entity 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 provides clear usage context: competitive AI-marketing audits with an example scenario. It implies not to use for single entity checks (use ai_visibility_check), but does not explicitly list alternatives or 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.

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

With no annotations, the description bears full responsibility. It discloses the data source (SEC EDGAR + XBRL), output verdicts, citation format, and efficiency gains. It does not mention authentication, rate limits, or destructive actions, but overall provides solid 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 three sentences, front-loaded with the core purpose, and each sentence adds value: purpose, domain, and return details. No superfluous 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 a single parameter, no output schema, and no annotations, the description covers purpose, domain, return values, and value proposition. It lacks explanation of verdict categories or error handling, but is largely complete for a v1 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%, but the description adds significant meaning by detailing permissible claim types and giving concrete examples, helping the agent understand valid inputs beyond the schema's generic 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 verb 'fact-check' and the resource 'claim against authoritative sources', specifies the domain (company-financial claims for US public companies), and distinguishes itself from siblings by emphasizing it replaces multiple agent calls. This provides a precise and differentiated 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 limits use to company-financial claims (revenue, net income, cash) for public US companies, providing clear context. However, it does not offer exclusions or compare to sibling tools, leaving some ambiguity for non-financial claims.

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