Pipedrive

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

Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description adds meaningful context about cost (BYO key for Anthropic) and return structure (per-model fields), 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?

Description is concise (3 sentences), front-loaded with main action, and every sentence adds relevant 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 includes return shape, parameter dependencies, and use cases, providing sufficient completeness for an AI agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining default model behavior, optionality of _apiKey depending on models selected, and the role of context for disambiguation.

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 probes LLMs for knowledge about an entity and scores visibility, using specific verbs and resources. It distinguishes itself from sibling tools like ask_pipeworx and compare_entities by focusing on AI visibility scoring for brands/topics.

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 use cases (AI-marketing audits, pre-launch checks) and explains default vs. paid model usage, but does not explicitly mention when not to use or offer alternative 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds that the tool routes to 3,745 tools and returns structured answers with stable pipeworx:// URIs, which is valuable behavioral context beyond the annotations. 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?

The description is a single well-structured paragraph, front-loaded with the key directive to prefer over web search. Every sentence adds value, including concrete examples. 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, the description adequately explains the return format (structured answer with citation URIs). It covers the breadth of supported question types and provides examples, making it complete for agent understanding.

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 comprehensive alias documentation. The description merely restates that the parameter is a natural language question, adding minimal value 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 routes questions to a vast array of verified sources and returns structured answers with citations. It explicitly distinguishes itself from web search by saying 'PREFER OVER WEB SEARCH'. The verb 'routes' and resource '3,745 tools across 884 sources' provide specific 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 gives explicit guidance on when to use it: for factual questions about real-world entities, events, or numbers, and lists example query patterns. It says to prefer over web search, but does not explicitly state when not to use it or mention alternative tools among the listed 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?

Discloses extra LLM cost, refusal reasons, and return structure beyond annotations (readOnlyHint, idempotentHint, etc.). 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?

Well-structured with front-loaded purpose, but the second paragraph could be slightly more concise. Every sentence adds value.

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

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

Despite no output schema, the description fully explains return format, refusal types, extra LLM cost, and when to prefer sibling, making it complete for a complex routing 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 descriptions for all 6 aliases for the question parameter. Description adds no additional parameter-level semantics 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 clearly states the tool's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It specifies it extracts answers only from tool results and distinguishes it from sibling `ask_pipeworx` by mentioning the extra LLM call and explicit refusal handling.

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 (quoted/cited/acted-on answers, high-stakes domains) and when not ('prefer ask_pipeworx for casual lookups'), providing clear context vs sibling.

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

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

The description extensively goes beyond annotations, detailing safety mechanisms (low-confidence short-circuit, closed market handling), resolution-rule risk, spread warnings, and fallback behavior. Annotations (readOnlyHint, idempotentHint, destructiveHint) are consistent and the description adds rich context 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 lengthy (approx 500 words) but well-structured with clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). Information is front-loaded, and the density is justified by the tool's complexity. Minor redundancy in use-case statements.

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 (no output schema, 3 parameters, rich behavior), the description covers all necessary aspects: resolution confidence, parent event extraction, news fallback, safety checks, and return value shapes. No gaps are apparent.

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 immense value by explaining how the tool resolves markets, classifiers, fan-out examples, response shapes, and edge cases. This far exceeds the schema's minimal 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 in one call. It specifies inputs (slug, URL, question text) and distinguishes it from sibling tools like polymarket_edges and polymarket_arbitrage, which serve different purposes.

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

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

Explicit use cases provided: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. Extensive examples and classifiers give context. However, it does not explicitly state when not to use this tool versus alternatives, which would merit a 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 readOnly, idempotent, etc. The description adds significant behavioral details: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), fiscal year handling, sorting by primary metric, and citation URIs. Lacks explicit return-field list but still highly transparent.

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

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

The description is front-loaded with examples and a clear purpose. It is informative but slightly dense; could be more structured for easier parsing. Still concise relative to the complexity.

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

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

Given no output schema, the description thoroughly explains behavior for both entity types, data sources, sorting, and citation handling. It is fully adequate for an agent to know what to expect.

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

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

Schema coverage is 100%, so the baseline is 3. The description restates the parameter types and constraints already in the schema without adding new semantic detail beyond examples.

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

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

The description clearly states that the tool performs side-by-side comparisons of 2-5 companies or drugs in one parallel call. It uses specific verbs like 'compare', 'rank', 'head to head', and distinguishes from sibling tools by noting it replaces 8-15 sequential lookups.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides natural language triggers and outlines when to use, effectively guiding the agent to prefer this tool for comparisons.

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 return format (verbatim evidence, confidence, source, fetched_at, citations, gaps) and emphasizes no invented facts. Annotations already indicate safety, and this description adds concrete behavioral details 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?

Relatively long but each sentence adds value. Front-loaded with main benefit. Slightly verbose but justified by complexity.

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

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

Comprehensive coverage: purpose, usage, behavior, parameters, output (findings packet, gaps), and requirements. No output schema, so return is well explained.

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

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

Schema coverage is 100%, but description adds meaning: question is natural language, depth options have descriptions, and depth:thorough requires a paid plan. 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 identifies 'deep_research' as a grounded multi-source research tool that decomposes questions into sub-questions and routes them in parallel. It differentiates from siblings like ask_pipeworx for single lookups.

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

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

Explicitly recommends use for broad or multi-part questions and contrasts with ask_pipeworx for single lookups. Also notes account and paid plan requirements for depth:thorough.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that the tool returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly.' This adds valuable behavioral context beyond 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 about 5 sentences, front-loaded with the main purpose. Every sentence adds value: purpose, use contexts, return details, and explicit when-to-use instruction. No fluff or redundancy.

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

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

Given 6 parameters and no output schema, the description explains the purpose, usage, and what is returned (tool list with schemas). It omits exactly how relevance is determined, but states 'top-N most relevant' and 'ready to call directly,' which is sufficient. Slight gap in relevance explanation, but overall 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 description coverage is 100%. The description mentions that query accepts natural language and aliases, and limit has default/max, but these are already in the schema description. The description does not add new meaning beyond what the schema provides, 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 'Find tools by describing the data or task.' It uses a specific verb (discover) and resource (tools). It lists many example use cases (SEC filings, financials, etc.), distinguishing it from sibling tools like search_within or bet_research, which are about using tools rather than discovering them.

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

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

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use guidance and implies when not to use (if already know the tool). Also mentions 'browse, search, look up, or discover what tools exist' for 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?

While annotations already indicate readOnly, openWorld, idempotent, and not destructive, the description adds significant behavioral context: it fans out across multiple sources (SEC EDGAR, XBRL, USPTO, GDELT/GNews, GLEIF), describes return fields, notes USPTO sunset with soft-fail behavior, and explains fallback chains. 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?

The description is moderately long but well-structured: example queries, then purpose, then detailed breakdown of sources and returns. Every sentence serves a purpose. Minor opportunity to shorten by moving examples to a separate note, but still effective.

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

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

Given the complexity (multiple data sources, fallback mechanisms, deprecation warnings), the description covers all essential aspects. Lists exactly what is returned, including URIs for filings. No output schema, but the description sufficiently explains return structure.

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

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

Schema already provides descriptions for both parameters (type enum with only 'company', value as ticker or CIK). The description reinforces this with examples ('AAPL', '0000320193'), explains that names are not supported, and adds context about zero-padding. 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 starts with natural language examples ('Tell me about X', 'research Acme', 'company profile for Microsoft') then explicitly states 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes from sibling tools like resolve_entity (for name resolution) and deep_research/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?

States 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Explicitly says to use resolve_entity first if only a name is available. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate destructiveHint=true, and the description adds context about clearing sensitive data. No contradictions.

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

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

Two sentences, no wasted words, front-loaded with core purpose.

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

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

For a simple tool with one parameter, annotations, and no output schema, the description covers use cases and pairing 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%, so the description adds marginal value beyond the schema's description of the 'key' parameter.

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 'Delete a previously stored memory by key,' specifying the verb and resource. Distinct from sibling tools 'remember' and 'recall'.

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 describes when to use (stale context, task done, clear sensitive data) and recommends pairing with 'remember' and 'recall', providing clear guidance.

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

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

Annotations fully declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds process details (fetches page, extracts title/description/key links) beyond annotations, which is helpful. 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 (three sentences) with purpose front-loaded. Every sentence adds value without redundancy or unnecessary detail.

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

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

Given full annotation coverage, 100% parameter schema coverage, and lack of output schema, the description effectively explains the tool's operation and output format (single text blob). It is complete for a simple generation 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 both parameters are documented. The description adds context about the tool's process but does not provide new parameter-level details beyond what schema already offers. Baseline 3 is appropriate.

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

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

The description uses specific verb 'generate' and resource 'production-ready llms.txt file', and distinguishes from sibling tools by focusing on llms.txt generation for AI crawlers. It clearly communicates the tool's function and scope.

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 (getting client site indexed, drafting for own project, auditing competitors) but lacks explicit when-not-to-use or alternative tool references. This is clear context without exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds value by listing returned fields and scoping to caller's subscriptions, beyond annotation safety profile.

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 front-loaded purpose and usage guidance. No redundant words, efficient 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?

Tool is simple with one optional param and no output schema. Description covers purpose, returned fields, and usage context completely.

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 description for the single boolean parameter. Description adds no extra parameter info beyond schema, baseline 3.

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

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

Description states verb 'List' and resource 'subscriptions' with caller scope. Distinguishes from siblings like subscribe/unsubscribe by specifying 'list' action and returning fields.

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 provides when to use: 'review what you're monitoring before adding more' and 'find an id to cancel'. Implies not for adding or canceling, which are separate 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds specifics about returned fields but no additional behavioral context (e.g., rate limits, authentication beyond schema). 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?

Single sentence, 20 words. Efficient, front-loaded, 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 presence of output schema, description sufficiently covers purpose and return fields. For a read-only tool with comprehensive annotations, 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 description coverage is 100%, so both parameters are already documented. Description mentions 'by ID' but adds no new meaning beyond schema descriptions. 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 action (Get), resource (specific deal), identifier (by ID), and lists return fields (title, value, stage, probability, owner, associated contacts, timeline). Distinguishes from sibling tools like pipedrive_list_deals and pipedrive_get_person.

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?

Implicitly guides when to use: when you need complete details for a specific deal by ID. Does not explicitly state when not to use or mention alternatives, but context from sibling names provides 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, idempotentHint, and destructiveHint. The description adds value by detailing the specific returned fields (name, emails, phones, organization, associated deals, custom fields), which helps set expectations 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 a single, front-loaded sentence that efficiently conveys the tool's purpose and output. Every word is necessary and there is no redundancy.

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

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

Given the tool's simplicity, an output schema exists, and the description covers the key return elements. No additional context is needed for a straightforward get-by-ID operation.

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

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

Schema coverage is 100%, so the schema already documents both parameters (id, _apiKey). The description does not add additional parameter details beyond what is in the schema, so baseline score of 3 applies.

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

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

The description clearly states the verb 'Get' and resource 'full contact details by ID', specifying returned fields like name, emails, phones, etc. This distinguishes it from sibling tools like pipedrive_list_persons which list multiple entities.

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

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

The description implies usage when needing details by ID, but lacks explicit guidance on when to use vs alternatives or when not to use. No exclusions or alternatives 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the description does not need to repeat these. It adds the return field context but does not disclose additional behavioral traits such as rate limits or pagination behavior beyond what the schema provides.

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

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

Two concise sentences: the first states the purpose and output, the second points to a sibling tool. No unnecessary words. Front-loaded with the most important 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 description mentions key return fields and points to the sibling for details. With an output schema present, it does not need to explain return structure. However, it could clarify that results are paginated (the schema includes limit/start but the phrase 'View all deals' might imply a single list).

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

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

Schema description coverage is 100%, so the schema fully documents all four parameters. The description does not add further meaning to the parameters themselves, only mentioning output fields, which 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 it lists deals in the pipeline and specifies the returned fields (IDs, titles, values, stages, owners). It distinguishes itself from the sibling tool pipedrive_get_deal, which is for full details on a specific deal.

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?

It explicitly directs users to pipedrive_get_deal for full details on a specific deal, providing a clear alternative. However, it does not offer guidance on when to use the status filter or pagination parameters, though these are self-explanatory in the schema.

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, idempotent, not destructive. Description adds value by listing returned fields. No contradictions, but could mention pagination behavior.

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, front-loaded with purpose, no wasted 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?

Given output schema exists, description adequately explains return data. Lacks mention of default pagination or filtering limitations, but overall sufficient for a list tool.

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

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

Schema coverage is 100%, so description need not add parameter details. The description does not elaborate on parameters beyond schema, meeting baseline expectation.

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 lists all contacts in CRM and specifies return fields (names, emails, phones, organizations, deals). Differentiates from sibling tools like pipedrive_list_deals and pipedrive_get_person.

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 viewing contacts but lacks explicit when-to-use or when-not-to-use guidance. No mention of alternatives like pipedrive_search for filtering.

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, idempotentHint, destructiveHint. The description adds that it returns 'matching records with IDs and basic info,' which is useful but minimal. No additional behavioral traits 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 a single sentence of 20 words, concise and to the point. No fluff, but could be structured more for readability.

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

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

With 4 parameters and an output schema, the description covers the broad search capability but misses details like default item type (all), pagination, and limit behavior. Adequate but not comprehensive.

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 description adds little beyond the schema. It lists item types but doesn't elaborate on term format, limit behavior, or API key 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 identifies the action (search) and the resources (deals, contacts, organizations, products, files). It distinguishes from sibling tools like pipedrive_get_deal by indicating it searches across multiple types, not single 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 vs alternatives. It does not mention when to use it over specialized tools like pipedrive_list_deals or other search 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?

Annotations provide minimal info (not read-only, not destructive), but description adds key behavioral details: rate-limited to 5 per identifier per day, free, no quota impact, and digests read daily. 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?

Six sentences, well-structured: purpose first, then usage conditions, then formatting tips, finally rate/limit info. No redundant phrases; every sentence serves a clear purpose.

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

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

Given the tool's simplicity (no output schema), the description covers all needed aspects: when to use, what to include, how to format, constraints (rate limits, quota). Complete for an 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?

Schema coverage is 100% with clear descriptions for all 3 parameters. The description adds extra guidance on how to write the message (e.g., describe in terms of tools/packs) and reinforces enum meaning, which adds value beyond schema alone.

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

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

The description clearly states the tool's purpose: to send feedback about Pipeworx tools (bugs, features, data gaps, praise). It uses specific verbs and defines categories, distinguishing it from sibling tools which are primarily query/action 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 tells when to use for each feedback type (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompts). Also mentions rate limits and that feedback is free and doesn't count against quota, providing clear 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?

Annotations provide readOnlyHint, idempotentHint, destructiveHint=false. Description adds valuable context: 'derived from CF analytics-engine, no PII, just (pack, tool, count)' and 'Cached 5min-1h depending on window'. This explains data provenance and caching behavior 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?

Single paragraph with front-loaded main action, immediately followed by three bullet-like use cases. Every sentence serves a purpose: what, how derived, use cases, and caching. No redundancy or fluff. Excellent 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?

For a simple tool with one optional parameter and no output schema, the description is quite complete. It states the return components (top tools, packs, volume), data derivation, and caching. Lacks explicit return format or example, but given the simplicity, it is sufficient.

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

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

Schema covers the single parameter 'window' with enum and description already explaining behavior. Description reinforces window semantics but does not add new parameter-specific information beyond what schema provides. Baseline 3 is appropriate given 100% schema coverage.

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

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

Clearly states 'Returns the top tools, top packs, and total call volume over a recent window'. Specific verb (returns) and resource (trending data) with explicit output components. Distinguishes from siblings like 'discover_tools' by focusing on aggregated trending signals rather than search.

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

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

Lists three explicit use cases (discovering hot sources, confirming canonical tool, checking alignment). Provides guidance on window selection ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). Lacks when-not-to-use or alternatives, but use cases are 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?

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds significant behavioral detail: failure on missing args, Jaccard similarity threshold, placeholder filter, fill check with realizable edge vs theoretical edge, and the response structure. No contradictions with annotations.

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

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

The description is detailed but well-organized with clear sections and logical flow. Every sentence adds value, though it could be slightly more concise by breaking out fill check or semantic anchor into separate notes. Front-loads the requirement and core purpose effectively.

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 response fields (opportunities, partition_check, fill check details) and handles edge cases like placeholder slugs and similarity thresholds. References the sibling tool for custom sizing. Covers both modes comprehensively.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds deeper context: examples of valid values, explanation of what each mode does internally (walking child markets vs searching related events), and the semantic anchor condition for topic mode. This significantly enriches 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 explicitly states the tool finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It clearly distinguishes two modes (event and topic) and provides examples. This sets it apart from sibling tools like polymarket_edges or polymarket_fill_risk.

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 specifies that exactly one of `event` or `topic` is required and warns against calling with no args. It recommends event mode for specific markets and topic mode for cross-event scanning. It explains the rationale for each mode but does not explicitly state when to avoid using this tool in favor of 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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are fully complemented by the description, which explains caching behavior ('Cached 1h at the KV level keyed on all knobs'), idempotency, and read-only scanning. 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 very long and dense, containing extensive implementation details (model families, Kelly formulas, filter logic). While well-structured with sections, it is not concise; many sentences could be shortened or omitted for clarity without losing essential guidance.

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 (9 parameters, no output schema, nested response structures), the description is exceptionally complete. It explains the three response segments, diagnostics, tradeable-edge filters, and even internal model logic. The absence of an output schema is compensated by a thorough description of the response format.

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 significant meaning beyond the schema by explaining how knobs like min_partition_leg_kelly work in context, why certain filters exist, and the trade-offs (e.g., slippage_pp discussion). The description enriches parameter understanding for an agent.

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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It is targeted at the use case 'what should I bet on today' and distinguishes itself from sibling tools by focusing on Pipeworx disagreement and actionable opportunities.

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

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

The tool's intended use is explicit (discovery of betting opportunities), but it lacks explicit guidance on when not to use it or alternatives. However, the context of 'scan top markets' implies a specific discovery role, and the detailed description of tradeable-edge knobs gives practical usage 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?

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds significant context: history depth bound by 60-day TTL, decay computed from daily closes of edge_pp_net (not intraday), and output structure details. No contradictions, and description enriches 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 well-structured: starts with a clear purpose, then details output fields, then limitations. Every sentence is informative and earned. No redundancy or fluff. It is appropriately sized for the complexity.

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

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

With no output schema, the description fully explains return values (tracked[], expired[], snapshot_dates[]) including field semantics. It also covers limitations (TTL, non-intraday). Given the data complexity, this is a comprehensive and complete 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 coverage is 100% with descriptions for both parameters. The description adds default values and clarifies constraints (days default 14, max 30; window default '1wk'). It also notes clamping but schema says 2-30. This adds marginal 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 it provides 'edge persistence and decay telemetry' from daily snapshots, answering the specific question of how long an edge has existed and whether it is shrinking. It differentiates between fresh and old edges, and the output structure is detailed. This distinguishes it from sibling tools like polymarket_edges which likely provide current snapshots.

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

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

The description implicitly guides usage by explaining what question the tool answers and why old edges are different. It mentions limits (60-day TTL, non-intraday data) but does not explicitly compare with sibling tools or state when to use this vs. others. The context is clear enough for an informed agent.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral details like 'walks the ladder', returns verdict (clean|degraded|cannot_fill), and explains forced directional risk, which enriches the agent's understanding 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 structured with bold headings and front-loaded purpose. While lengthy, every sentence adds necessary context given the complexity and lack of output schema. Minor redundancy could be trimmed, but overall efficient.

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

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

For a complex tool with two modes, no output schema, and multiple return fields, the description covers inputs, outputs (listed), usage guidance, and risk warnings (forced directional risk). It references sibling tools and explains when to use it, making it self-contained.

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 inline descriptions. The description adds value by explaining how each parameter behaves in the two modes (e.g., side defaults for basket mode, size_usd interpretation), going beyond the schema definitions.

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: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It specifies two distinct modes (single-market and basket) and lists exact return fields, distinguishing it from sibling tools like polymarket_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?

The description explicitly states when to use this tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why partial basket fills are risky, providing clear exclusions and 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?

While annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds extensive behavioral context: compatibility_warning conditions, temporal_alignment checks, skipped_cross_type/subtype counters, and the note that 'pre-mapped ≠ tradeable'. This fully discloses behavior 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 well-structured: opening summary, then modes, response format, safety fields. Every sentence adds information. It is slightly long but front-loaded with the core purpose. Could be tightened, but no tautology or 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 has 3 parameters, no output schema, and safety annotations already present, the description is quite complete. It explains input modes, response components (leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters). Lacks a full response example, but for a read-only analytical tool, it suffices.

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 covers 100% of parameters with descriptions. The description adds value by explaining the topic parameter's 10 shortcuts, the overriding behavior of explicit parameters, and giving examples. It enriches understanding of parameter interaction beyond the schema's individual descriptions.

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

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

The description clearly states the tool computes 'Cross-venue spread between Kalshi and Polymarket for the same resolving question', with specific verb+resource. It distinguishes from siblings like polymarket_arbitrage by focusing on two venues, and details two modes (topic shortcuts and explicit pairing), leaving no ambiguity.

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

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

The description explains two use modes and when to use each (topic shortcuts vs. explicit tickers). It warns that most pre-mapped topics return compatibility warnings, guiding the user against naive use. However, it doesn't explicitly compare to sibling tools or state when not to use this tool, missing a clear 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?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds scoping information (anonymous IP, BYO key hash, account ID), which is valuable 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?

Three sentences with no redundancy. The main action is front-loaded, and each sentence adds unique value without 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?

For a simple retrieval tool with one optional parameter and no output schema, the description fully explains behavior, return values, and scoping, leaving no gaps for an agent.

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

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

Schema coverage is 100% and describes the 'key' parameter. The description adds practical guidance on omitting the key to list all keys, enhancing the schema's explanation.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all keys if the argument is omitted. It uses specific verbs and distinguishes itself from sibling tools like 'remember' and 'forget'.

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

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

Provides explicit context for when to use ('look up context stored earlier without re-deriving'), suggests pairing with 'remember' and 'forget', but lacks explicit when-not-to-use or alternative 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?

Annotations already indicate readOnly, idempotent, non-destructive. Description adds value by explaining mark_read behavior and the alternative GET endpoint. 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?

Five sentences, front-loaded with purpose, each sentence adds necessary detail. 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?

Describes return fields (source, citation_uri, raw payload), filtering options, and alternative access. Lacks output schema but covers enough for an agent to use effectively.

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 description adds minimal value beyond schema. Provides example values (e.g. 'sec_8k') and clarifies mark_read usage, but mostly reinforces 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 'Pull fired events from your subscription feed. Returns the most recent alerts...' which specifies both the verb and resource. It distinguishes itself from sibling tools by focusing on alerts feed, no overlap detected.

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 usage context: filtering by type/since, mark_read behavior, and polling. Mentions alternative access via GET endpoint. Lacks explicit 'when not to use' but gives good guidance.

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

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

Annotations already indicate readOnlyHint, openWorldHint, etc. The description adds significant behavioral context: fans out to multiple sources, handles errors (GNews fallback on rate limits), soft-fails USPTO due to API sunset, and returns structured output with citation URIs. No contradictions with annotations.

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

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

The description is adequately sized for the complexity, but slightly verbose. It is well-structured: starts with query examples, then source details, parameter info, return value, and sibling distinction. Every sentence is valuable, but could be tightened slightly.

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 the return format (changes[] grouped by source, total_changes, pipeworx:// URIs). It covers fallback behavior, parameter details, and error handling. It is complete for the tool's complexity and intended 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 coverage is 100%, so baseline is 3. The description adds extra semantics: explains 'since' accepts ISO or relative shorthand with examples ('7d', '30d', '3m', '1y'), 'value' can be ticker or CIK, and 'type' only supports 'company'. This goes 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 provides a change feed for a company (SEC, news, patents) within a time window, and distinguishes from sibling tool entity_profile. It uses specific verbs like 'fans out' and references concrete sources, making the purpose unambiguous.

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

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

Explicitly states when to use this tool (recent changes) vs. entity_profile (static profile). Provides guidance on parameter usage, fallback behavior (GDELT→GNews), and soft-fail for patents. Includes example queries and relative shorthand for 'since'.

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 idempotentHint=true and destructiveHint=false, so the agent knows it's a safe write operation. The description adds valuable behavioral context: key-value storage scoped by identifier, 24-hour retention for anonymous sessions, and persistence for authenticated users. This goes beyond annotations without contradicting them.

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?

Each sentence serves a purpose: purpose, usage example, persistence details, and companion tools. The description is front-loaded with the main action. No redundant or 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?

Given the tool's simplicity (two string parameters, no output schema), the description covers all necessary aspects: what is stored, how it is scoped, retention policy, and related tools. It is complete for an agent to understand and 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% coverage with descriptions for key and value. The description reinforces this by explaining the key-value pair nature and provides examples of key names and value types (findings, addresses, preferences), adding practical guidance beyond the schema alone.

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

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

The description clearly states that the tool saves data for later reuse, gives concrete examples (resolved ticker, address, preference, research subject), and explicitly distinguishes from siblings recall and forget. It specifies the verb 'save' and the resource 'data,' making the purpose unambiguous.

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

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

The description explicitly states when to use the tool ('when you discover something worth carrying forward'), provides context about persistence (authenticated vs. anonymous sessions), and recommends pairing with recall and forget, guiding appropriate 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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description reveals internal cascading through multiple lookup endpoints, auto-disambiguation for companies, and specifics of what each entity type returns (ticker, CIK, RxCUI, citations). No contradictions with annotations.

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

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

The description is fairly long but front-loaded with purpose and examples. Most sentences add value, though some redundancy exists (e.g., repeated mention of citation URIs). Still efficient given the two-type complexity.

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

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

Given no output schema, the description thoroughly covers purpose, usage, parameter details, behavioral traits, and return values for both entity types. It explains internal cascading and provides enough context for an agent to use it 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?

With 100% schema coverage, the description adds substantial extra meaning: concrete examples for 'value' (AAPL, 0000320193, ozempic), clarifies acceptable inputs per type (ticker/CIK/name for company, brand/generic for drug), and explains auto-disambiguation.

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

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

The description clearly states that the tool resolves user-spoken names to canonical/official identifiers, with specific examples like 'What's the ticker for…' and 'find the CIK for…' It distinguishes itself implicitly from sibling tools like entity_profile or compare_entities by focusing solely on name-to-ID lookup.

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

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

The description explicitly advises 'Use FIRST whenever you have a name but need an ID,' providing clear when-to-use guidance. However, it does not explicitly mention when not to use it or suggest alternative tools for other purposes, though the context of sibling tools fills this gap.

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 behavioral context beyond annotations: it explains the probe mechanism (calls ai_visibility_check per entity), the ranking logic, and the output structure (ranked list with score, confidence, signal density). It also notes the optional Anthropic API key requirement. No contradiction with readOnlyHint, idempotentHint, or other 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 two sentences with an additional usage example, all front-loaded with the core action. Every sentence adds value (purpose, mechanism, use case). No fluff or repetition of schema details.

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 having no output schema, the description fully explains return values (ranked list with score, confidence, signal density). It covers all parameters, cardinality constraints (2-8 entities), and the per-probe mechanism. Given the tool's moderate complexity, the description 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 semantic value by stating the first entity is treated as the 'subject' for narrative, which meaningfully extends the schema's description of 'entities'. It also contextualizes the optional parameters (models, _apiKey, context) without redundancy.

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 AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and identifies most/least recognized. It distinguishes from sibling 'ai_visibility_check' which is a single-entity probe, making the purpose specific and actionable.

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 the tool is useful for competitive AI-marketing audits and provides a concrete example question. It implies usage for comparing multiple entities, but does not explicitly state when not to use it or mention alternatives for single-entity checks beyond the sibling 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral details beyond annotations: partial failures (bundlephobia timeout), graceful degradation (sources_failed list), and first measurement delay (5-30s). It also describes the return content.

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 dense paragraph but efficiently conveys all key points. It front-loads the main purpose and then details. Slightly long but 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 complexity (composite check over multiple sources), absence of output schema, and good annotations, the description is complete. It explains partial failures, timeouts, return structure, and ecosystem 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% with both parameters fully described. The description adds minimal extra meaning (scoped packages accepted), but baseline 3 is appropriate as schema already 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 clearly states the tool's purpose: a composite check for adding an npm package, covering license, advisories, version history, bundle size, and more. It uses specific verbs and resources and distinguishes itself from sibling tools by being a one-call composite.

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

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

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". It also clarifies limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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, non-destructive. Description adds technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and offset for verification. 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?

Concise single paragraph, front-loaded with purpose. Every sentence adds necessary 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, description explains return format (passages with offsets and scores), limitations (200K char cap, truncation flag), and internal workings. Fully adequate for an AI 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 descriptions. Description adds value with examples for query (e.g., 'supply-chain risk') and clarifications like max 200K chars for text and limit range 1-20.

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 'Semantic search INSIDE a fetched record' with specific verb and resource, and distinguishes from siblings by mentioning its pairing with ask_pipeworx_grounded.

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

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

Provides explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt' and mentions pairing with ask_pipeworx_grounded. Lacks explicit when-not-to-use, but still informative.

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 indicate a mutating, idempotent, open-world tool. The description goes well beyond: it explains account requirements, type-specific parameter structures, delivery constraints (SMS 10/day cap, webhook auto-disable after 10 failures), and the one-time webhook signing secret. All behavioral traits are thoroughly disclosed.

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

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

The description is a single dense paragraph that front-loads the core purpose and returns value. While every sentence is informative, the density could be improved with bullet points or separation by section (requirements, types, delivery). It is nonetheless efficient and not redundant with schema.

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 covers all key aspects: purpose, requirements, type variants, delivery options, and constraints. The only gap is the lack of output schema detail—it only notes 'returns subscription id' and mentions webhook secret delivery. Given the complexity (nested objects, 3 params, 5 enum values), the description is nearly complete.

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

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

The input schema has 100% description coverage, but the description adds immense value: it enumerates all five type enum values with concrete examples, gives detailed type-specific params objects, and explains delivery subfields including phone verification, email validation, and webhook HMAC signing. The description effectively teaches an agent how to construct valid inputs.

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: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It specifies the resource and distinguishes it from sibling tools like list_subscriptions and unsubscribe. The supported types and delivery channels are detailed, leaving no ambiguity about what the tool does.

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

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

The description provides explicit prerequisites (Pipeworx OAuth account) and practical usage context for each type and delivery channel. However, it does not explicitly contrast this tool with alternatives like ai_visibility_check or ask_pipeworx, leaving the 'when not to use' implicit. The context given is strong enough for an agent to decide when to invoke.

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 meaningful context: the output format (category-bucketed example questions) and that results are 'drawn from the live catalog of thousands of tools'. It conveys the educational purpose beyond the safety profile. No contradictions with annotations.

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

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

The description is slightly verbose but front-loaded with alternative phrasings and structured logically from purpose to details. Every sentence adds value, but some redundancy could be trimmed. Still highly effective.

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

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

With no output schema, the description fully explains the return type (category-bucketed example questions with tool+argument shapes). The single optional parameter is completely documented. The use case and context (onboarding, first-time use) are thoroughly covered.

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

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

Schema coverage is 100% for the single optional parameter. The description adds practical examples ('finance, pharma, betting') and clarifies the effect of omitting the parameter ('full spread' vs 'focus'). This adds value beyond the schema definition.

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

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

The description uses specific verbs ('returns category-bucketed example questions') and identifies the resource ('onboarding entry point for an agent that just connected'). It clearly distinguishes from sibling tools like ask_pipeworx and entity_profile by positioning itself as the first tool for learning capabilities.

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 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides guidance on when to call with or without the topic argument. The description also mentions that the tool teaches how to call meta-tools, which is valuable usage 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?

Discloses key behaviors beyond annotations: the subscription is deactivated (not deleted) and historical events remain accessible via 'recent_alerts'. Annotations indicate it's write-only, non-destructive, and idempotent, which aligns with the description.

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

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

Two concise sentences with no redundancy. The main action is front-loaded, followed by essential constraints and behavioral details. Every sentence adds unique 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?

For a simple tool with one parameter and no output schema, the description covers the primary action, ownership constraint, deactivation behavior, and side-effect on historical data. No 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 single parameter 'id' is fully described in the schema as a subscription UUID from 'subscribe'. The description adds no additional semantics beyond the schema, but baseline 3 is appropriate given 100% schema coverage.

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

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

The description clearly states the action ('cancel a subscription'), the resource ('subscription'), and the required identifier ('by id'). It differentiates from sibling tools like 'subscribe' and 'list_subscriptions' by describing the cancellation behavior and ownership enforcement.

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 ownership enforcement ('you can only cancel your own subscriptions'), which guides when the tool is applicable. While it does not list alternatives or exclusions, the context with sibling tools implies when not to use it (e.g., for others' subscriptions).

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, openWorldHint, idempotentHint, destructiveHint false. The description adds context: v1 supports only company-financial claims via SEC EDGAR + XBRL, specifies the return format (verdict, extraction, actual value, citation, delta). 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 a single focused paragraph front-loaded with natural-language query patterns. Every sentence adds value; no 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?

Despite no output schema, the description explains return values (verdict, extraction, actual value, citation, delta). It covers domain limitation, efficiency gain over alternatives, and data source, making it self-contained.

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% with description for the single 'claim' parameter. The description adds examples and clarifies it takes natural-language input, going beyond the schema's generic 'string' type.

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 validates natural-language claims against authoritative sources, with specific examples and domain focus on company-financial claims. It distinguishes itself by noting it replaces multiple sequential calls.

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

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

Explicitly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' Mentions domain specificity (company-financial claims) but lacks explicit when-not-to-use or comparisons to siblings beyond the efficiency note.

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