Gong

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds value by noting the default free model and the BYO key for Anthropic, implying cost implications. It also mentions the return structure (per-model score, confidence, signals, raw_response + combined view), which is beyond what annotations provide.

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

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

The description is concise and front-loaded: two sentences (three if counting the returns line) that efficiently convey purpose, usage, and key details. Every sentence earns its place with 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?

Given the tool's complexity (4 parameters, no output schema) and rich annotations, the description covers the essential aspects: what it does, default vs paid models, return structure. It does not cover edge cases or error handling, but it is sufficient for an LLM-probing 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 description coverage is 100%, so baseline is 3. The description reinforces schema details (e.g., default model, API key usage, context disambiguation) but does not add substantial new meaning beyond what the schema already provides. Slight added value in explaining the _apiKey and models parameters.

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: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies the verb 'probe', the resource 'LLMs', and the outcome 'score visibility'. This distinguishes it from sibling tools like 'compare_entities' or 'scan_competitor_ai_presence'.

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 when to use the tool: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also clarifies the default model and the need for an API key for Anthropic. However, it does not explicitly mention when not to use it or provide direct alternatives, though usage context is well implied.

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 clearly explains the tool's behavior: it selects the best data source, fills arguments, and returns results. It also mentions it works in plain English and requires no schema knowledge. Since no annotations are provided, the description carries the full burden, and it does so well by disclosing the automated delegation behavior. However, it does not mention limitations or error cases.

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) and front-loaded with the core purpose. It provides examples, which is helpful. However, the phrase 'No need to browse tools or learn schemas — just describe what you need' is somewhat redundant with the first sentence, but still adds clarity.

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 only one parameter, no output schema, and no annotations, the description is fairly complete. It explains the tool's automated nature and provides usage guidance. It does not mention the return format or error handling, but given the tool's simplicity, 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?

The input schema has 100% coverage with a single parameter 'question' described as 'Your question or request in natural language'. The description adds meaning by explaining that the question should be in plain English and providing examples, but does not add much beyond what the schema already conveys. 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: answering natural language questions by automatically selecting and invoking the best data source. It uses a specific verb ('Ask') and resource ('answer from best available data source'), and distinguishes itself from sibling tools by emphasizing its ability to handle arbitrary questions without needing to browse tools or learn 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?

The description explicitly says to use this tool when you want to 'ask a question in plain English' and notes that it picks the right tool automatically, implying it should be used when the agent doesn't know which specific tool to call. It also provides concrete examples of appropriate questions, but does not explicitly state when not to use it or suggest alternatives.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false, and the description adds that the tool resolves, classifies, fans out to packs, and compares probabilities. It mentions the depth parameter's effect (quick vs thorough). This provides behavioral context beyond the annotations.

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

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

The description is a single concise paragraph that front-loads the action, then covers inputs, process, and use cases. Every sentence adds essential information without redundancy.

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

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

Despite no output schema, the description fully explains what the tool returns (evidence packet + comparison), how it works (classification, fan-out), and when to use it. No gaps for the intended use case.

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 enriches parameter meaning: market can be slug, URL, or question text; depth is clarified as number of evidence sources (quick=2-3, thorough=full). This adds value beyond the enum and type 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. It specifies inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It is distinct from siblings like ask_pipeworx and compare_entities by focusing on bet-specific orchestration.

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?". It implies this tool is for Polymarket betting contexts but does not explicitly mention when not to use or list sibling alternatives for exclusion.

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 bears the full burden of behavioral disclosure. It explains the output format (paired data + pipeworx:// URIs) and data fields per type, but does not cover potential errors, authentication needs, or rate limits, leaving some behavioral aspects implicit.

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

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

The description is two sentences long, front-loaded with the primary purpose. Every sentence contributes essential information: first sentence defines the action, second sentence details variants and benefits. 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?

Given the tool has no output schema or annotations, the description adequately covers the input parameters and output specifics. It explains what data is returned for each entity type and mentions resource URIs. While error handling and edge cases are omitted, the description is sufficient for typical 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 well-described parameters. The description adds value beyond the schema by explaining the context for each parameter (e.g., for type='company' vs 'drug') and the nature of the return data, enriching the semantic understanding.

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

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

The description clearly states the tool's function: comparing 2–5 entities side by side. It specifies the two entity types (company and drug) and the data fields returned for each, making the purpose distinct and unambiguous.

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

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

The description indicates when to use this tool (for side-by-side comparison) and mentions it replaces 8–15 sequential calls, implying efficiency gains. It does not explicitly list alternatives among sibling tools but provides clear context for 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 provided, the description fully carries the behavioral disclosure burden. It states that the tool searches by natural language description and returns the most relevant tools. However, it does not mention any side effects, rate limits, or whether it reads or writes data. A score of 4 is appropriate because it clearly describes the core behavior without going into finer details that are not critical for a search tool.

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

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

The description is very concise: two sentences that front-load the purpose and usage guideline. Every sentence is necessary and earns its place. No redundant or filler content.

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 has 2 simple parameters, no output schema, no nested objects, and is a search/discovery tool, the description is complete enough. It explains what the tool does, when to use it, and how to use the parameters. It could optionally mention that the output includes tool names and descriptions, but that is implied. A 4 is appropriate as it covers all necessary aspects without being overbearing.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the query parameter as 'Natural language description of what you want to do' with examples, which goes beyond the schema's minimal description. The limit parameter is also well-explained with defaults and max. This extra context justifies 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 the verb 'Search' and the resource 'Pipeworx tool catalog', explaining that it returns relevant tools with names and descriptions. It distinguishes itself from siblings by positioning itself as the discovery tool to call first when 500+ tools are available, which is not claimed by any sibling.

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 the agent when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' It provides clear context and an alternative strategy (call first), without needing to list exclusions.

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

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

No annotations provided, but the description details what data is returned and that federal contracts are excluded for performance reasons. Does not mention auth or rate limits, but the behavioral traits are well disclosed.

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

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

Two sentences with key information front-loaded. The first sentence is dense but effective. No redundant words.

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

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

Given no output schema, the description covers what the tool returns (data types and citation URIs) and notes an important exclusion. Sufficient for an agent to understand the tool's capabilities and limitations.

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%. The description adds context: explains the 'type' enum limitation, that 'value' accepts ticker or CIK, and instructs to use resolve_entity for names. Goes beyond schema info.

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 returns a full entity profile across multiple Pipeworx packs, enumerates data sources for 'company' type, and distinguishes itself from siblings like resolve_entity and usa_recipient_profile.

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?

Mentions it replaces 10-15 calls and explicitly says not to use for federal contracts (use usa_recipient_profile). Also advises using resolve_entity if only have a name. Could be more explicit about alternatives, but 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, so the description carries full burden. It does not disclose whether deletion is irreversible, requires authentication, or affects other memories.

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

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

Single sentence, no fluff, but could be more informative within the same length.

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

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

Simple tool with one param, but lacks any behavioral or usage context. With no annotations and no output schema, more detail 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% and the description adds no extra meaning beyond the schema's description of 'key' as 'Memory key to delete'.

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 'Delete a stored memory by key' clearly states the action (delete) and the resource (stored memory). It distinguishes itself from 'remember' (store) and 'recall' (retrieve) among siblings.

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

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

No guidance on when to use this tool versus alternatives like 'forget' vs 'remember' or 'recall'. No mention of prerequisites or consequences.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds process details: fetches page, extracts title/description/key links, emits standard markdown. This goes beyond annotations and provides full transparency.

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

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

The description is a single paragraph of three sentences, front-loaded with purpose. Every sentence adds value, and there is no waste.

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

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

Given the tool's simplicity (2 params, no output schema) and informative annotations, the description is fully complete. It covers what the tool does, how it works, and practical use cases.

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

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

Schema coverage is 100% with clear descriptions for both parameters (url and max_links). The description reiterates the URL purpose and mentions the default/max for max_links, but adds no significant new meaning 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 it generates an llms.txt file for any URL, specifying the action (generate), the resource (llms.txt file), and the purpose (for AI crawlers). It distinguishes itself from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing on producing a standard file format.

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 three use cases: indexing a client's site, drafting for own project, or auditing competitor AI visibility. While it doesn't mention when not to use or alternatives, the given scenarios are clear and helpful for an agent to decide.

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

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

Annotations are empty, so the description carries full burden for behavioral traits. It discloses that the tool returns participants, duration, and metadata, which implies a read-only operation. However, it does not mention any potential side effects, rate limits, or authorization requirements. Given the tool is a simple get-by-id, the description is adequate but not rich.

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

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

The description is a single sentence with no wasted words. It front-loads the purpose and key output. Could be slightly improved by adding a brief note on when to use it, but remains 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?

Given the tool is a simple single-parameter get-by-id operation with no output schema, the description is complete enough. It names the primary output fields (participants, duration, metadata). No significant gaps for its complexity level.

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 the only parameter callId already described as 'The Gong call ID'. The description adds no additional semantic meaning beyond the schema. Baseline 3 is appropriate since schema does the full job.

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

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

The description clearly states the verb 'Get details' and the resource 'specific Gong call by its ID', and lists the key information returned (participants, duration, metadata). It is distinct from sibling tools like gong_list_calls (which lists calls) and gong_get_transcript (which gets transcript), making it unambiguous.

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

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

The description implies when to use this tool (when you have a specific call ID and need call details), but does not explicitly state when not to use it or mention alternatives. It provides basic usage guidance but lacks exclusion criteria or comparison to sibling tools.

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

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

With no annotations, description partially covers behavior: indicates it's a read operation ('get') and returns a 'full conversation transcript'. However, does not disclose potential length, format, or any restrictions (e.g., access permissions, call type).

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

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

Two sentences, direct and to the point. No unnecessary words. Could combine into one sentence without loss.

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 required parameter and no output schema, the description adequately explains input and output. However, lacks details about the transcript format or any limitations, which would be helpful for a tool returning a potentially large text.

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 100% of parameters with description for callId. Description adds no further meaning beyond the schema; both state 'transcript' and 'call ID'. Baseline 3 is appropriate.

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

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

Clearly states it gets a transcript for a Gong call, specifying the resource ('transcript') and action ('get'). Distinguishes from siblings like 'gong_get_call' which returns call details, not transcript. Could explicitly differentiate from gong_list_calls which lists 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?

No guidance on when to use this tool versus alternatives like gong_get_call. No mention of prerequisites (e.g., call must exist) or context (e.g., only for completed calls).

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

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

No annotations are provided, so the description carries the burden. It mentions pagination (cursor-based) but does not disclose behavior like ordering, rate limits, or what happens when no filters are applied (likely returns all calls).

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

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

The description is two sentences, efficient and front-loaded with the core purpose. No wasted words.

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

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

For a list tool with 3 optional parameters and no output schema, the description covers the basics but lacks details on default behavior (e.g., max results) or any caveats about call availability.

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 context by mentioning 'date range' and 'cursor-based pagination', which aligns with parameters. However, the description does not add much meaning beyond the schema's descriptions; the 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 verb 'list' and resource 'recorded calls from Gong', with optional filtering and cursor-based pagination. It distinguishes from sibling tools like gong_search_calls and gong_get_call by focusing on listing with optional date filters.

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 mentions optional date range and pagination, providing clear context for when to use filters. However, it does not explicitly state when not to use this tool or mention alternatives like gong_search_calls for more complex 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?

With no annotations, the description carries full burden. It correctly indicates a read-only list operation. No behavioral traits beyond 'list all users' are disclosed, but that is sufficient for a simple list tool.

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

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

A single, short sentence that is front-loaded and contains no unnecessary words. Perfectly 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?

For a simple list tool with no parameters, no output schema, and no nested objects, the description is complete enough. It explains what it does and what resource it returns.

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

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

The schema has no parameters, so description coverage is 100%. The description adds no parameter info because none exist; this is appropriate.

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

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

The description clearly states the verb ('List') and resource ('all users'), and the scope ('in the Gong workspace'). It distinguishes from siblings like gong_list_calls which list calls, not users.

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

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

The description implies usage for listing users, but does not specify when to use this tool versus alternatives like gong_search_calls. No exclusions or context on 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 carries full burden. It states that the tool returns matching calls but does not disclose behavioral details such as whether the search is case-sensitive, matches partial words, or the scope (e.g., date range, user). No mention of rate limits, authentication, or 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 two sentences, front-loading the main action. No unnecessary words. Could be slightly more detailed 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 low complexity (1 parameter, no output schema), the description is minimally adequate. It tells the agent what the tool does and what parameter is needed. However, it lacks context on result format, pagination, or limitations, which may be relevant for effective 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 the parameter 'keyword' having a description: 'Keyword to search for in calls'. The tool description adds no further meaning beyond what the schema already provides. Thus baseline score of 3 is appropriate.

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

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

The description uses a clear verb-resource pair: 'Search Gong calls by keyword'. It specifies the action (search) and resource (calls), and adds the result detail 'Returns calls that match the search text'. However, it does not differentiate from sibling tools like gong_list_calls, which might also return calls but without keyword filtering.

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

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

No guidance on when to use this tool vs. alternatives like gong_list_calls (which might list all calls without search) or gong_get_call (retrieve a specific call). No when-not-to-use or prerequisites are 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?

No annotations are provided, so the description carries the full burden. It discloses rate limiting (5 per identifier per day) and content guidelines (no verbatim prompts). For a simple feedback tool, this is sufficient; it could mention if any confirmation is given, but it's not critical.

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: four sentences that cover purpose, usage guidelines, rate limiting, and content policy. Every sentence earns its place 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?

Given the tool's simplicity (3 parameters, no output schema), the description fully covers what the agent needs to know: the purpose, when to use, what to include, and rate limits. 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 coverage is 100% with descriptions for each parameter. The description adds significant value beyond the schema by instructing to describe what was tried in terms of Pipeworx tools/data and not to include user prompts. This directly helps the agent craft appropriate messages.

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 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, praise), distinguishing it from sibling tools like ask_pipeworx which is for questions. It uses a specific verb and resource.

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

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

The description gives explicit guidance on when to use (for feedback) and what to include (describe what you tried in terms of Pipeworx tools/data, do not include user prompt). It also mentions rate limiting. However, it does not explicitly state when not to use or provide alternatives, which would push it to 5.

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. The description adds caching details and data source, but no additional behavioral traits beyond what annotations provide.

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

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

Three sentences front-loading key purpose and usage. Every sentence adds value without redundancy.

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

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

Given one parameter, no output schema, and rich annotations, the description thoroughly covers the tool's purpose, use cases, and caching behavior.

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 parameter. The description adds value by explaining the trade-off between short and long windows, which is not in 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?

The description clearly states the tool returns top tools, top packs, and call volume over recent windows. It distinguishes itself from siblings like ask_pipeworx or discover_tools by focusing on trending data.

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 (hot data discovery, confirmation of canonical choice, alignment checking). No explicit alternatives are mentioned, but the context from sibling tool names suggests differentiation.

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

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

Annotations already declare readOnlyHint and non-destructive. The description adds value by detailing the internal process: walking child markets, extracting dates/thresholds, sorting, and reporting violations. This complements the annotations without 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?

The description is a single paragraph that efficiently conveys purpose, mechanism, and output. It avoids unnecessary repetition but could be slightly more streamlined.

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, the description explains the return format ({market_a, market_b, gap_pp, suggested_trade}) and provides sufficient context for an AI to understand behavior and 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?

The single parameter 'event' has 100% schema coverage with its own description. The tool description repeats this info ('Pass a Polymarket event slug or URL'), adding no new semantic value 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 in Polymarket events via monotonicity violations. It specifies the action (find arbitrage), resource (Polymarket event), and mechanism (checking price ordering), distinguishing it from siblings like polymarket_edges.

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 when to use: for events with multiple 'by [date]' or 'by [threshold]' markets, and describes the logic. It does not explicitly exclude other scenarios or name alternatives, but the context is clear for this specialized tool.

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?

Describes the model (lognormal from FRED + coinpaprika), workflow (scans top markets, groups by asset, fetches price history once, computes probability), and output (ranked by edge, with suggested direction). This adds significant detail beyond annotations which only indicate read-only and open-world.

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?

Every sentence adds value; front-loaded with purpose, then model details and output. Slightly long but appropriate for a complex tool.

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

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

Given no output schema, the description explains return values (top N ranked by edge magnitude with suggested direction). It covers the tool's function and limitations 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?

Schema coverage is 100% (all parameters described). The description reinforces parameter meaning (e.g., limit is top N, window is volume window) but does not add substantial new semantics beyond the 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 scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, with specific verb+resource. It distinguishes from sibling 'polymarket_arbitrage' by focusing on edge detection rather than arbitrage.

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

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

Explicitly states it's built for 'what should I bet on today' and helps discover opportunities without manual browsing. Does not explicitly exclude cases or name alternatives, but the use case is clear.

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

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

The description discloses behavioral traits beyond annotations: it explains the real-time nature of the spread, the two operational modes, how parameters override topic mapping, and the output format (raw probabilities and spread in percentage points). Annotations already indicate read-only and idempotent, but the description adds critical behavioral context for invocation.

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 efficiently structured: front-loaded purpose, then modes with clear labels, parameter details, and output summary. Every sentence provides unique information without redundancy. It is detailed yet concise.

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

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

Given the tool's complexity (cross-venue, multiple modes, no output schema), the description is remarkably complete. It covers the arbitrage rationale, mode selection, parameter behavior, and output structure. The lack of an output schema is fully compensated by the clear description of return values.

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

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

All three parameters are fully described in the schema (100% coverage). The description adds significant value by explaining the interaction between topic and explicit parameters, including override behavior and concrete examples (e.g., 'KXFED-26OCT', 'fed-decision-in-june-825'). 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?

The description clearly states the tool's purpose as a cross-venue spread calculator between Kalshi and Polymarket for the same resolving question. It uses specific verbs like 'cross-venue spread' and identifies the resource (two prediction markets), distinguishing it from all sibling tools (e.g., polymarket_arbitrage, polymarket_edges).

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 two modes (topic vs explicit) and when to use each, and it mentions the arbitrage signal context. However, it does not explicitly state when NOT to use this tool or provide direct comparisons to siblings, though the context is clear enough.

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 clearly states the tool is for retrieval (non-destructive), and mentions persistence across sessions. It does not describe error behavior (e.g., missing key), but that's minor for a retrieval tool.

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

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

Two sentences, front-loaded with purpose and immediate usage instruction. Every word adds value; 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 no output schema, description explains return value behavior (single memory vs list). Parameter coverage is complete. No output schema needed for this simple retrieval. Could mention case sensitivity or format of key, but not critical.

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

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

Schema coverage is 100% with a single optional parameter 'key', and the description adds that omitting it lists all keys. This aligns with the schema description; no additional semantics needed beyond what schema 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 uses specific verbs ('Retrieve', 'list') and resource ('memory by key'), clearly distinguishing the operation from 'remember' (store) and 'forget' (delete). It also covers the variant of omitting the key to list all.

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

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

Explicitly states when to use (retrieve context saved earlier) and the optional omission of key to list all. However, no alternative tool is named, though siblings like 'remember' and 'forget' are implied.

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 fully details behavior: fans out to multiple sources in parallel, accepts ISO and relative dates, returns structured changes with URIs. 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, each with clear purpose. Front-loaded with main action. 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?

Covers input format, multiple data sources, output structure (structured changes, count, URIs). Complete for a read tool with no 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?

Schema has 100% coverage, but description adds meaning: explains relative date formats, ticker vs CIK, and that type is limited to 'company'. Baseline 3 plus extra 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?

The description clearly states 'What's new about an entity since a given point in time' and elaborates on the fan-out behavior for company type, distinguishing it from sibling tools 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?

Explicitly suggests use cases: 'Use for "brief me on what happened with X" or change-monitoring workflows.' Does not mention when not to use, but context is clear.

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

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

With no annotations, the description carries the full burden. It discloses persistence differences ('authenticated users get persistent memory; anonymous sessions last 24 hours'), which is crucial behavioral context. No contradictions with annotations since none exist.

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 three sentences: purpose, usage examples, and persistence behavior. Each sentence adds value 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?

Given the simplicity (2 simple params, no output schema, no nested objects), the description covers the key aspects: what it does, how to use it, and persistence behavior. It lacks mention of any size limits or overwrite behavior, but is largely complete for a basic memory store 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 input schema covers both parameters with descriptions. The description does not add additional meaning beyond the schema, and schema coverage is 100%, so baseline of 3 is appropriate. It does not elaborate on value formatting or size limits.

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

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

The description clearly states the tool stores a key-value pair in session memory, specifying the resource ('session memory') and action ('store'). It distinguishes from sibling tools like 'forget' and 'recall' by focusing on storing, not retrieving or deleting.

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

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

The description provides explicit use cases ('save intermediate findings, user preferences, or context across tool calls') and differentiates between authenticated and anonymous sessions. However, it does not explicitly state when not to use it or mention alternatives like 'recall' for retrieval.

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?

Without annotations, the description fully carries behavioral disclosure. It explains the tool accepts multiple input types and returns specific fields, including resource URIs. No mention of error handling or idempotency, but for a lookup tool, this is 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 two sentences, front-loaded with purpose, and contains no unnecessary words. Every sentence earns its place.

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, the description fully explains what is returned (ticker, CIK, company name, resource URIs). For a simple lookup tool with 2 params and clear examples, this is 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%, so baseline is 3. The description adds examples (AAPL, 0000320193, Apple) and context (v1 supports company), but these are only minor enhancements over the 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 verb 'resolve' and resource 'entity', and specifies the outcome: canonical IDs across Pipeworx data sources. It distinguishes itself from siblings by noting it replaces 2-3 lookup calls, making its unique value 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 gives explicit input formats (ticker, CIK, name) and mentions it replaces multiple calls, indicating when to use. It lacks explicit when-not-to-use instructions but provides sufficient context for proper usage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density per entity. It also notes that the first entity is treated as the 'subject' for narrative. These details enrich behavioral understanding beyond annotations.

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

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

The description is three sentences covering purpose, behavior, and use case, plus parameter details. It is front-loaded with the main action and remains reasonably concise. Minor redundancy with parameter list could be trimmed, but overall well structured.

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 specifies the return format: a ranked list with score, confidence, and signal density per entity. It covers purpose, parameters, behavioral traits, and usage context. No gaps remain for effective agent 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 has 100% description coverage, but the description adds critical context: entities are brand/business/product names, models (workers-ai default, anthropic requires _apiKey), and context disambiguates common names. It also clarifies the special role of the first entity. This meaningfully supplements 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 specifies a clear action: comparing AI visibility across multiple entities side-by-side. It names the underlying tool (ai_visibility_check), indicates ranking and identification of most/least recognized, and provides an example use case ('does Claude know about us as well as our competitors?'), effectively distinguishing it from the sibling ai_visibility_check which is a single-entity probe.

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 is useful for competitive AI-marketing audits and gives a concrete example. However, it does not explicitly state when not to use it or differentiate from siblings like compare_entities, though the context of AI visibility is implicit.

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 return types (verdict, extracted form, actual value with citation, percent delta) and version constraints (v1, limited domain). It implicitly describes a read-only, query behavior without stating destructive potential, which is apparent from 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?

Three concise sentences front-loaded with purpose, listing capabilities and benefits. Every sentence provides unique value with no redundant or vague phrasing.

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 lack of annotations and output schema, description thoroughly covers domain, input examples, output elements, and efficiency advantage over alternatives. Sufficient for an AI agent to correctly select and 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 only parameter, 'claim', has 100% schema coverage and is enriched with diverse natural-language examples in the description, clarifying acceptable input formats 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?

Description clearly states it fact-checks natural-language claims against authoritative sources, specifically for company-financial claims via SEC EDGAR+XBRL. It lists return values and contrasts with sequential agent calls, making its purpose distinct from sibling tools like ask_pipeworx or search 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 specifies domain (company-financial claims for public US companies) and indicates it replaces multiple sequential agent calls, guiding when to use this tool over general Q&A tools. Lacks explicit when-not-to-use guidance but domain restriction provides clear boundaries.

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