Calendly

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: default model (free), optional Anthropic probe (BYO key with cost implications), and the return structure (per-model fields + combined view). 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?

Three sentences, front-loaded with the core purpose, followed by key details and use cases. No wasted words; every sentence earns its place.

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

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

With no output schema, the description clearly explains the return format (per-model score, confidence, signals, raw_response, plus combined view). It covers all parameters, use cases, and cost implications, making it complete for the tool's complexity.

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

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

Schema description coverage is 100%, so the schema already documents each parameter. The description adds context about defaults and cost but does not significantly enhance parameter semantics beyond what is in 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 specifies the verb 'probe' and 'score', the resource (LLMs about an entity), and the output (visibility score 0-100 per model). It distinguishes itself from siblings by focusing on AI visibility scoring for brands/products.

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: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the optional API key for Anthropic and the default model. However, it does not explicitly state when not to use the tool, missing a minor opportunity for clarity.

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, non-destructive, open-world. Description adds that it returns structured answers with citation URIs, routes across many tools, and is fast. 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?

Description is front-loaded with key message, but somewhat verbose with many examples. Still well-structured and 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?

Covers purpose, usage, behavior, and output type (structured answer with citations). Adequate given complexity and no output schema.

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

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

Schema coverage 100%, but description adds context that the question parameter is natural language and accepts multiple aliases, enhancing schema meaning.

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

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

Description clearly states the tool answers factual questions using structured data, explicitly preferencing over web search, and lists specific examples. It distinguishes from siblings ask_pipeworx_grounded and deep_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?

Provides explicit when-to-use (factual questions, trigger phrases), when-not-to-use (step up to grounded or deep_research), and contrasts with web search. Examples are given.

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

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

Annotations already cover read-only, idempotent, and non-destructive behavior. Description adds extra behavioral context: costs one extra LLM call, returns explicit refusal reasons (e.g., 'not_in_source'), and provides evidence quoting. No contradiction with annotations.

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

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

Description is well-structured and concise: begins with purpose, then usage guidelines, then return format. Every sentence adds value; no redundancy 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 complexity (18 siblings, 6 parameters, no output schema), the description is complete. It specifies return structure with keys like answer, evidence, confidence, source, and lists possible refusal reasons, compensating for lack of output schema.

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

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

Schema covers all 6 parameters with descriptions, so baseline is 3. Description adds no additional parameter semantics beyond noting that it accepts aliases for question, which is already implied by 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?

Clearly defines the tool as a hallucination-resistant answer mode for high-stakes reads. Distinguishes from sibling ask_pipeworx by specifying extra LLM call and appropriate use cases (e.g., legal, medical).

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 an answer will be quoted, cited, or acted on, and the agent must not invent facts' with examples. Also advises preferring ask_pipeworx for casual lookups, providing a clear alternative.

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 details behavioral traits beyond annotations: fan-out logic, response shapes (market, analysis, evidence), resolver contract, parent event extractor, news fallback fields, safety short-circuits, wide-spread handling, and resolution-rule risk. This is a comprehensive disclosure that far exceeds what the annotations provide.

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

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

Despite being long, the description is well-structured with clear headings (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) that facilitate AI scanning. It front-loads the core purpose and progressively adds detail. Could be slightly more concise, but the structure minimizes cognitive load.

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

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

Given the tool's complexity (multiple bet types, fan-out, safety, response variants), the description covers all essential aspects: input resolution, category-specific data sources, analysis output, evidence structure, resolver confidence, parent event extraction, and edge cases like closed markets or wide spreads. No output schema exists, so the description must and does explain return values 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%, but the description adds significant context: market parameter explains three input forms, depth clarifies quick vs thorough, include_raw gives rationale for true/false. The description enriches understanding 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and output (evidence packet + comparison). This distinct functionality sets it apart from sibling tools like polymarket_edges or deep_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?

Explicit usage scenarios are given: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides examples of fan-out per bet category, and describes blocking conditions like low-confidence matches or closed markets, guiding when not to rely on the output.

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 readOnly, openWorld, idempotent, non-destructive. Description adds valuable behavior: pulls latest 10-K data from SEC EDGAR/XBRL with correct fiscal year handling for companies, and FAERS/FDA data for drugs, plus sorted results. 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?

Description is front-loaded with common trigger phrases, and each sentence adds value. Slightly long but well-structured. 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?

Despite no output schema, description explains return includes paired data and citation URIs, and details metrics compared (revenue, income, etc. for companies; adverse events, trials for drugs). Complete for decision-making.

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

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

Schema has 100% coverage with enum for type and description for values. Description enriches meaning by explaining what data each type pulls and that values are tickers/CIKs or drug names, with max constraints.

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

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

The description clearly specifies that the tool performs side-by-side comparisons of 2-5 companies or drugs, using trigger phrases like 'compare X and Y'. It distinguishes itself from siblings like entity_profile by focusing on multi-entity comparison.

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

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

Explicitly states to prefer this tool over sequential single-entity lookups for comparisons, and explains that it replaces 8-15 calls. Does not detail when not to use, but the context is clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds behavioral context by stating the return values (user URI and organization URI) and the prerequisite role for other tools, complementing the annotations without contradiction.

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

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

The description is concise with two sentences, front-loaded with the action and then the return value. It efficiently communicates what the tool does and how to use it without superfluous content.

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

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

Given the tool's simplicity, rich annotations, and full schema coverage, the description is complete. It explains the purpose, return values, and usage order relative to sibling tools, covering all necessary context for correct invocation.

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

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

Schema description coverage is 100% with the parameter _apiKey described. The description does not add any further meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves the current Calendly user and returns user URI and organization URI. It distinguishes from sibling tools by noting it is a prerequisite for list_scheduled_events and list_event_types, which require those URIs.

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 instructs to call this tool first because list_scheduled_events and list_event_types require the user or organization URI. This provides clear when-to-use guidance and implies the tool is for initial setup before those other 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, openWorld, idempotent, and non-destructive. The description adds substantial behavioral context: account requirement, paid plan for 'thorough', parallel decomposition across 4,819 tools, 15-60s latency, and output format (findings packet with evidence, confidence, source, fetched_at, citation, gaps array). 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 quite long (over 150 words) but front-loads critical information (account required, fallback tool). Every sentence adds value, but some details (e.g., number of sources, tools, exact output format) could be streamlined or moved to output schema if it existed. It earns a 3 for being fully informative but somewhat verbose.

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

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

Given the tool's complexity (1259 sources, 4819 tools, parallel research, structured output) and lack of output schema, the description thoroughly covers prerequisites, use cases, limitations, expected timing, output structure, and gap handling. No missing critical information remains.

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 already documents both parameters. The description adds value by explaining the depth enum (quick=3, standard=5, thorough=8) and clarifying that the question parameter accepts natural language and multi-part queries. However, the additional context is not extensive beyond what the schema implies.

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: 'grounded multi-source research across Pipeworx's 1259 STRUCTURED data sources' and explicitly distinguishes it from ask_pipeworx and open-web search. It provides a specific verb-resource combination (research across structured data sources) and contrasts with sibling tools.

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

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

The description gives explicit guidance on when to use ('broad/multi-part questions over structured data') and when not ('BREAKING or colloquial CURRENT-NEWS...'). It names the alternative tool (ask_pipeworx) for single lookups and current news. This provides clear decision criteria.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: returns top-N results with schemas and examples, ready to call directly. 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 front-loaded with purpose and usage, but the inline list of domains makes it somewhat lengthy. Every sentence adds value; minor trimming could improve 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?

Given the tool's role, schema coverage, and annotations, the description fully covers what the tool does, what it returns, and how to use it. No missing context.

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

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

Schema coverage is 100% with descriptions for each parameter, including aliases. The description does not significantly augment parameter meaning beyond noting that examples are included and the query takes natural language. Baseline 3 is appropriate.

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

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

The description clearly states the verb ('find') and resource ('tools'), lists concrete domains, and contrasts with sibling tools by positioning itself as the first call for tool discovery.

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 ('when you need to browse, search, look up, or discover what tools exist') and gives a usage hint ('Call this FIRST when you have many tools available').

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable behavioral context: fans out across multiple sources, specifics on return fields, patents API sunset status, and fallback behavior. 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 packed with information in a single paragraph. It front-loads with example queries. Although dense, it remains efficient without excessive verbosity. Slight improvement would be breaking into bullet points 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?

Given the tool's complexity (multiple data sources, many return fields) and lack of output schema, the description is thorough. It enumerates all return fields (cik, filings, fundamentals, patents, news, LEI) and explains the data sources, making it complete for agent invocation.

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

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

Schema coverage is 100% with descriptions. Description adds meaning by explaining the 'value' parameter accepts ticker or zero-padded CIK and reiterates that names are unsupported, which goes beyond the schema's basic description.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It uses specific verbs ('profile', 'fans out across') and lists concrete data sources, distinguishing it from siblings by advocating preference over chaining single-pack 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?

Provides explicit when-to-use guidance ('when the user asks for a holistic view') and when-not-to ('names not supported – use resolve_entity first'). Also includes notes on soft-fail behavior for patents API sunset.

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 idempotentHint=true. The description adds behavioral context like clearing sensitive data, which goes beyond 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?

Two sentences, front-loaded with the core action, and no wasted words. Efficient and easy to parse.

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 purpose, usage, and pairing. It is complete enough for an agent to decide when to invoke it.

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 parameter description in the schema is sufficient. The description adds no extra meaning beyond what the 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 that the tool deletes a previously stored memory by key, with the specific verb 'Delete' and resource 'memory'. It distinguishes itself from siblings like '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?

The description provides explicit guidance on when to use the tool: when context is stale, the task is done, or to clear sensitive data. It also recommends pairing with 'remember' and 'recall', giving 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 already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral details: it fetches the page, extracts title/description/key links, and emits standard llms.txt format. No contradictions.

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

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

Three sentences, no fluff. First sentence states purpose, second explains process, third lists use cases. Efficiently front-loaded with key 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 two well-documented parameters and no output schema, the description covers purpose, process, use cases, and parameter details sufficiently. It lacks explicit mention of error handling or edge cases, but overall complete for the tool's complexity.

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

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

Schema covers both parameters (url, max_links) with descriptions. The description adds extra detail: default (25) and max (50) for max_links, and clarifies that url can be full URL or specific landing page. This provides meaning beyond the schema.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifies the output format (standard markdown), and lists specific use cases. It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on generation rather than analysis.

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

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

The description explicitly lists three practical use cases (client indexing, own project drafting, competitor auditing), giving clear context on when to use the tool. It does not explicitly mention when not to use it or alternatives, but the context is sufficient for an AI agent.

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

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

Annotations already indicate read-only and idempotent behavior; the description adds what details are returned (name, times, status, location, membership), providing useful context.

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

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

Two sentences, no redundant information, and the key verb 'Get' is front-loaded.

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

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

Given the simple retrieval operation, good annotations, and full parameter descriptions, the description adequately covers what the tool does and returns.

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

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

Input schema covers both parameters with descriptions; the description adds minor clarification about the UUID being the last path segment, but does not significantly enhance understanding 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 retrieves a single scheduled event by UUID, which differentiates it from sibling tools like list_scheduled_events.

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

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

The description does not explicitly state when to use it over alternatives, but the context of retrieving a single event by UUID implies its use case.

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 destructiveHint=false, so the agent knows it's safe. The description adds behavioral details: pagination support (count, page_token), authentication requirement (_apiKey), and the mutual exclusivity of user/organization parameters. 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: first defines the core purpose, second adds prerequisites and output summary. No fluff, front-loaded with key information. Every word earns its place.

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

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

Given 6 parameters and no output schema, the description covers the essential: mutual exclusivity of user/organization, authentication, and return fields. It does not detail pagination behavior (but schema provides that) or error handling. Slight gap: no guidance if both user and organization are provided.

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 critical semantics beyond the schema by clarifying that 'user' and 'organization' are mutually exclusive (only one should be provided) and that they come from 'current_user'. This reduces ambiguity not resolvable from 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 action ('List') and the resource ('bookable event types'), with the qualifier 'bookable' distinguishing it from general event listing. The mention of return fields ('URIs, names, durations, scheduling URLs, and active state') reinforces the purpose.

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

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

The description explicitly states the prerequisite of a user or organization URI and directs the agent to get them from 'current_user'. This provides clear context for when to use the tool, though it does not explicitly mention when not to use it or list alternatives.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context by listing the returned fields (name, email, status, timezone, answers), which are beyond the schema. No contradictions.

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

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

Single sentence, front-loaded with purpose and output. No wasted words. Every part earns its place.

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

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

The description mentions key returned fields, which is helpful since there is no output schema. However, it omits pagination behavior and filter options (status, count, page_token), though these are covered in the input schema. Still fairly complete 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 description coverage is 100%, so baseline is 3. The description does not add additional meaning to parameters beyond what the schema already provides; it only mentions output fields.

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

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

The description clearly states the verb 'List' and the resource 'invitees of a scheduled event by event UUID'. It distinguishes from sibling tools like get_event (single event) and list_scheduled_events (events list) by focusing on invitees.

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

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

The description does not explicitly state when to use this tool vs alternatives like get_event or list_scheduled_events. It implies usage when invitee details are needed for a specific event, but lacks exclusion criteria or alternative 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 safe read-only operation. Description adds behavioral context: need to provide one of user/org, returns specific fields. 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, front-loaded with purpose, then prerequisites, capabilities, and output. No superfluous content.

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

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

Covers key aspects: what it does, prerequisites, filters, return fields. Minor gap: pagination flow (page_token usage) not explicitly described, but schema covers it and standard list behavior is implied.

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

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

Schema has 100% description coverage. Description adds cross-parameter meaning (user or org required) and overall filtering context, going beyond individual parameter descriptions.

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

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

The description clearly states it lists scheduled meetings/events, specifies required parameters (user or org URI), and distinguishes from siblings like get_event (single event) and list_event_types (different resource).

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

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

Provides clear context: requires user or org URI from current_user, filters by status/time window, paginates. Lacks explicit when-not-to-use or comparison with alternatives, but the prerequisites and filtering capabilities are well-stated.

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description doesn't need to repeat safety. It adds value by listing the specific fields returned, but beyond that no additional behavioral details are needed.

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

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

Two sentences, front-loaded with action and output, followed by usage guidance. Every word earns its place; 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?

The tool has one optional parameter and no output schema. The description explains what is returned. For a simple listing tool, this is fully adequate 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?

Schema coverage is 100%, so the description doesn't need to elaborate on parameters. The description implies active subscriptions by default, which aligns with the schema. No extra parameter guidance 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 it lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.). It distinguishes from sibling tools like subscribe and unsubscribe by giving usage context (review before adding or find id to cancel).

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

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

The description explicitly tells when to use the tool: to review subscriptions before adding more or to find an id to cancel. While it doesn't list when not to use it, the context is clear and the sibling set provides 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?

The description discloses key behavioral traits beyond annotations: it sends feedback to a team, is rate-limited, free, and does not consume tool-call quota. No annotation contradictions exist; the description adds helpful context about the team's digest reading and roadmap impact.

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

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

The description is concise (4-5 sentences) and front-loaded with the core purpose. Every sentence adds value without redundancy, making it easy for an agent to quickly understand the tool.

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

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

For a simple feedback submission tool with no output schema, the description covers all essential aspects: purpose, usage guidelines, behavioral constraints (rate limits, quota), and parameter semantics. It is fully 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 descriptions for all parameters. The description adds value by providing concrete examples for the 'type' enum and explaining the 'context' object fields (pack, tool, vertical). It also advises on 'message' specificity, going beyond the schema.

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

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

The description clearly states the tool's purpose: sending feedback about bugs, missing features, or praise to the Pipeworx team. It specifies distinct use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools, none of which serve a feedback function.

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

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

Explicitly describes when to use the tool (for bugs, features, data gaps, praise) and provides guidance on how to structure feedback (focus on tools/packs, avoid pasting prompts). It also mentions rate limits (5 per identifier per day) and confirms the tool is free and doesn't count against quota.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds extra behavioral details: caching (5min-1h), derived from CF analytics, no PII, self-aggregating, which significantly enriches understanding.

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?

Front-loaded with key purpose and use cases. Some additional explanatory text is present but each sentence adds value. Could be slightly more concise but overall well-structured.

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

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

Despite no output schema, description adequately explains return values (top tools, packs, volume). For a tool with one optional parameter and simple output, this is complete.

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

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

Schema coverage is 100% and schema already describes the window parameter with enum values. Description adds extra semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.'

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a window, with a specific verb and resource. It distinguishes from siblings by being a trending/aggregation tool.

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 lists three detailed use cases (discovering hot data sources, confirming canonical tool, seeing alignment). Does not mention when not to use or alternatives, but context is clear.

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

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

Annotations already declare read-only, idempotent, non-destructive. The description adds critical behavioral details: fill check mechanism, partition filter, semantic anchor for cross-event pairs, and explanation of response structure (opportunities[], partition_check, fill_check). 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 long but well-structured: purpose first, then modes, then technical details, then output. Could be slightly more concise by grouping related details, but all content earns its place.

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

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

Given two-param tool with no output schema, the description fully explains input modes, internal algorithms (monotonicity, partition sum, semantic anchor, partition filter), output structure, and fill-check behavior. It provides enough context for correct invocation and result interpretation.

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 descriptions already explain each parameter's purpose (single-event vs cross-event mode). The description adds further context: example values, how each mode triggers specific logic, and fallback to trending scan when no args given. Schema coverage is 100% so baseline is high.

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 a clear verb and resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) and explains scanning approaches, differentiating it from sibling tools like 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?

Explicitly states when to use no args ('trending_scan'), event mode ('recommended for a specific market'), and topic mode ('for cross-event scanning'). Also directs to sibling tool: 'For custom sizing use polymarket_fill_risk.' Provides examples of acceptable inputs (slugs, URLs, seed questions).

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

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

Beyond the readOnlyHint and idempotentHint annotations, the description details caching behavior (1h at KV level), the exclusion of unreliable Fed signals, and the per-segment behavioral nuances (e.g., partition_overround returning kelly_fraction_half=0 at parent level). 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 lengthy and includes many technical details about model families and internal filters. While this information is valuable, it is verbose and could be structured more concisely for easier parsing by an AI agent.

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), the description thoroughly explains the output structure, including top-level segments, diagnostics, and field meanings (e.g., edge_pp_net, kelly_fraction). It also covers caching, Fed exclusion, and parameter usage examples.

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

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

The input schema covers 100% of parameters with descriptions, but the description adds significant context on parameter interplay, such as min_kelly not filtering partition arbs and min_partition_leg_kelly applying per-leg. This adds meaning 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 the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It specifies the verb 'scan' and the resource 'Polymarket markets', and distinguishes itself from siblings like polymarket_arbitrage by focusing on Pipeworx data mispricings.

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

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

The description explicitly targets the 'what should I bet on today' use case and explains that agents can discover opportunities without paging hundreds of markets. However, it does not provide explicit comparison or exclusion of sibling tools, though the unique focus on Pipeworx data is implied.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds valuable behavioral context: how trend and decay are computed, that expired opportunities are included, and limitations like 60-day TTL and daily closes not intraday. 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 long but well-organized with a clear front-loaded purpose, followed by response format and limitations. Almost every sentence adds value, though some details (e.g., 'net of default slippage') could be integrated more concisely.

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 thoroughly details the response: tracked[] with time-series, trend, decay; expired[] with lifespan; snapshot_dates[]. With annotations covering safety and no output schema needed, the description is complete and self-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 coverage is 100%, so baseline is 3. The description adds detail: days defaults to 14 with clamp 2-30, window defaults to '1wk' and lists families 24hr|1wk|1mo. This goes beyond the schema to clarify parameter behavior.

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

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

The description uses a specific verb ('tracks') and resource ('edge persistence and decay telemetry'). It directly answers the question 'how long has this edge existed and is it shrinking?' and contrasts itself with siblings by emphasizing historical analysis rather than just 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 explains the parameters (days and window) with defaults and ranges, and outlines the response structure. However, it does not explicitly state when to use this tool versus alternatives like polymarket_edges, though it is implied for historical tracking.

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) already provide a safety profile, but the description adds rich behavioral context: walks the ladder, returns verdict (clean|degraded|cannot_fill), explains basket fill details including thin_legs and forced_directional_risk. 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 dense and front-loaded with the core function, but includes extensive details that might be split into structured sections. However, every sentence adds value, so 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 two modes, no output schema, and 4 parameters fully described, the description covers purpose, usage, parameter semantics, behavioral warnings, and real-world risk. It is complete for an AI agent to safely select and invoke the tool.

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

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

Schema description coverage is 100%, and the description adds meaningful interpretation: side default behavior differs between modes (auto for basket), size_usd is 'settlement notional' for basket, and explains clamping. This goes beyond the schema's basic 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 specifies a 'realizable-vs-theoretical edge check against live CLOB order-book depth,' clearly separating single-market and basket modes, and listing detailed outputs like top_of_book, vwap_fill_price, slippage_pp, etc. This is a specific verb+resource that distinguishes it from siblings like polymarket_arbitrage and polymarket_edges.

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

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

It explicitly states 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also warns about partial fills converting an arb into an unhedged directional position, providing 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes beyond by detailing behavioral traits: compatibility_warning conditions, temporal_alignment, skipped_cross_type/subtype counters, and the fact that many pre-mapped topics return warnings. This provides rich context beyond the annotations.

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

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

The description is somewhat long but well-structured: core purpose first, then modes, then response details, then safety fields. Every sentence adds information; no fluff. Could be slightly more concise, but the complexity of the tool justifies the length.

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

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

Given the tool's complexity (cross-venue spread with safety fields) and lack of output schema, the description is fairly complete. It covers output fields like compatibility_warning, temporal_alignment, and skipped counters. It also addresses edge cases (e.g., non-equivalent bet shapes, semantic mismatch). Minor gap: does not specify response format for success vs warning cases explicitly.

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

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

Schema description coverage is 100%, with each parameter described. The description adds value by explaining the two modes (topic vs explicit), giving example values, and clarifying override behavior. It also explains the response structure, though without an output schema, the description compensates well.

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

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

The description clearly states the tool's purpose: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It details two modes (topic shortcuts and explicit tickers), distinguishing it from siblings like 'polymarket_arbitrage' which likely focuses on single-venue arbitrage. The verb 'spread' and resource 'cross-venue' are specific and unambiguous.

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

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

The description provides explicit guidance on when to use: for cross-venue spread analysis, with two modes explained. It warns that most pre-mapped topics return warnings and are not tradeable, setting realistic expectations. However, it does not explicitly mention alternative tools or when not to use, leaving a slight gap in 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 declare readOnlyHint=true, and description adds context about scoping and pairing with remember/forget, enhancing transparency beyond annotations.

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

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

Three efficient sentences with no fluff, front-loaded with action, and well-structured.

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

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

Fully explains purpose, scope, pairing, and expected results, making it complete for a simple tool with no output schema.

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

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

Schema coverage is 100%, and description adds meaningful context for the key parameter ('omit to list all keys'), which aids the 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 it retrieves a value saved via remember or lists all keys, distinguishing it 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 clear when-to-use guidance and examples (ticker, address, notes), but does not explicitly state when not to use or compare to other retrieval tools.

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

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

The description contradicts the readOnlyHint annotation by describing the mark_read parameter which flags events as read, modifying state. Annotations indicate no state change, but the description implies a mutation. This is a clear 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 concise, front-loads the purpose, and each sentence adds meaningful information. 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 no output schema, the description explains returned fields (source, citation_uri, raw event payload). It covers filtering and mark_read but lacks details on error handling or pagination. Still, it is fairly complete for a read-heavy 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%, and the description adds value beyond the schema with examples (e.g., 'sec_8k'), explains the effect of mark_read on subsequent calls, and clarifies the default limit. This extra context improves parameter understanding.

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

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

The description clearly states it pulls fired events from a subscription feed and returns alerts with source, citation_uri, and raw payload. It implies a list retrieval, but does not explicitly differentiate from siblings like get_event, so not a full 5.

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

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

The description mentions that the same feed is available via a GET endpoint for scripts/dashboards, providing an alternative access pattern. However, it does not explicitly state when to use this tool versus siblings like list_event_types or get_event.

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, idempotent, and non-destructive behavior. The description adds value by detailing the fan-out to multiple sources, fallback logic, and soft-failure for patents. It also describes the return structure (changes grouped by source, total_changes count, citation URIs), going well beyond what annotations provide.

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

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

The description is a single paragraph, but it is front-loaded with example queries and each sentence adds necessary detail (sources, parameter hints, fallback, return type, alternative tool). It could be slightly more structured for scanability, but it remains efficient without unnecessary words.

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

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

Given the tool's complexity (multiple data sources, fallback, parameters with shorthand, no output schema), the description covers every essential aspect: what it does, how parameters work, what to expect in the response, and when to use a sibling tool. An agent has enough information to select and invoke this 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 coverage is 100%, so baseline is 3. The description adds meaning by expanding on `since` with examples ('7d', '30d', '3m', '1y') and a typical monitoring recommendation, and clarifies that `value` accepts ticker or CIK. This enriches the schema's descriptions but doesn't introduce entirely new information.

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 begins with concrete example queries ('What's new with X', 'latest on Y') that immediately convey the tool's purpose as a change feed for recent company activity. It explicitly names the data sources (SEC EDGAR, GDELT/GNews, USPTO) and clearly distinguishes itself from the sibling tool entity_profile by stating when to use the alternative.

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 direct usage guidance: it recommends typical `since` values ('30d' or '1m'), explains fallback behavior between GDELT and GNews, notes a soft-fall for patents, and explicitly tells the agent to use entity_profile for static profile needs. This covers both when to use this tool and when to choose an alternative.

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 idempotentHint=true and destructiveHint=false; description adds context about scoping ('scoped by your identifier'), persistence duration ('authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'), and non-destructive nature, but could note overwrite behavior or concurrency.

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

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

The description is front-loaded with the core purpose in the first sentence, and each subsequent sentence adds distinct value (when to use, storage details, pairing instructions). No redundant or extraneous content.

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

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

Covers purpose, usage context, persistence behavior, and tool relationships. Lacks explicit return type or error handling, but for a simple write operation with no output schema, this is acceptable given the schema and annotations provide the rest.

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 reiterates the key-value pattern and gives examples ('resolved ticker, target address'), but adds minimal beyond the schema, warranting the baseline score.

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

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

The description uses a specific verb ('save data') and resource ('data the agent will need to reuse later'), and clearly distinguishes from sibling tools like recall and forget by stating its role as storage.

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 ('when you discover something worth carrying forward') and provides alternatives and complementary tools ('Pair with recall to retrieve later, forget to delete'), guiding correct invocation.

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 as safe/read-only. Description adds value by detailing cascading through multiple endpoints and mention of citation URIs, which annotations do not cover.

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 purpose first, then usage, then detailed output per type. Every sentence contributes meaning; 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?

Despite lacking an output schema, the description fully specifies return values for both entity types, including citation URIs. Also explains internal cascading behavior. Adequate for a lookup tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds examples (ticker, CIK, brand/generic names) and mentions auto-disambiguation, but these are minor additions over 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?

Clear verb 'resolve' with specific resource 'name to identifier', supported by user query examples ('ticker for...', 'CIK for...'). Distinct from siblings like compare_entities and entity_profile.

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

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

Explicitly states 'Use FIRST whenever you have a name but need an ID' and notes it replaces 2-3 manual lookups. Does not explicitly list alternatives for when not to use, but context implies alternatives exist when ID is already known.

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

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds that the tool internally calls ai_visibility_check, ranks results, and returns a structured output (score, confidence, signal density). It also mentions the need for an API key for certain models.

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, using four sentences to cover purpose, mechanism, use case, and output. Every sentence adds value without redundancy.

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

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

Given that there is no output schema, the description adequately describes the return format (ranked list with score, confidence, signal density). It could mention potential latency due to multiple probes, but that is not critical.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaning by explaining that the first entity in the array is treated as the 'subject' for narrative purposes, and clarifies the default model and optional API key requirement.

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 compares AI visibility across multiple entities side-by-side by probing each with ai_visibility_check, ranking by score, and surfacing most/least recognized. It distinguishes itself from the sibling tool ai_visibility_check, which likely handles 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?

The description provides a concrete use case ('competitive AI-marketing audits') and asks a motivating question. While not explicit about when not to use, it implies that for single entity checks the sibling ai_visibility_check is appropriate.

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

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

Discloses partial failure behavior, latency for first bundlephobia measurement, and that sources_failed lists timeout cases. This complements annotations (readOnly, idempotent) with useful operational details.

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

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

Description is relatively long but efficiently packs all necessary information. Front-loaded with main purpose and each sentence contributes. Minor redundancy in listing return fields.

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 details the return structure (summary fields, advisories, links, alternatives) and error handling. Covers ecosystem limitation and graceful degradation, making it complete enough 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?

Both parameters are described in schema; description adds default behavior for 'version' (defaults to latest). Schema coverage is 100%, so description adds marginal value but confirms default behavior.

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 performs a composite check for evaluating npm packages, covering license, advisories, version history, and bundle size. It distinguishes itself from sibling tools by its specific package analysis 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?

Explicitly describes when to use (agent asks about package safety/popularity/size) and when not (non-NPM ecosystems), and provides alternatives (deps.dev:version directly for other ecosystems).

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds offsets for verbatim verification, embedding model details (BGE-base-en, cosine), window size (500 chars), character cap (200K) with truncation. No contradiction.

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

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

Four sentences, highly efficient. Front-loaded with core function, then examples, pairing, technical details. Every sentence adds value without redundancy.

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

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

No output schema, but description tells what is returned (passages with offsets and similarity scores). Covers inputs, defaults, pairing, technical limitations. Complete for a search 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 covers all 3 parameters with descriptions. Description adds practical context: 'text you already pulled', 'natural-language query', 'top-N passages'. Explains behavior and purpose beyond schema.

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

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

The description clearly states it performs 'Semantic search INSIDE a fetched record' with concrete examples (SEC 10-K, article, long tool result). It distinguishes from sibling ask_pipeworx_grounded by pairing context and use case.

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

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

Explicitly tells when to use: 'when the record is too big to cram into the prompt', and explains it saves context. Mentions pairing with ask_pipeworx_grounded for grounding. Provides clear usage boundaries.

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 give idempotentHint and openWorldHint. Description adds behavioral details: external delivery channels (email/SMS/webhook), webhook signing, auto-disable after 10 failures, SMS cap, and phone verification requirement. Does not explicitly address whether duplicate subscriptions are handled idempotently, but overall adds substantial value 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?

At ~120 words, the description is relatively long but well-structured: first sentence states purpose, then prerequisites, then type breakdown, then delivery options. No wasted words, and the information is front-loaded.

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

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

Given the complexity (multiple subtypes, nested delivery object, enums), the description covers prerequisites, type-specific parameters, delivery constraints, and return value. Lacks error cases or throttling details, but overall provides sufficient context 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?

All 3 parameters are described in the schema, but the description adds significant semantic value: concrete examples for each type's params (e.g., sec_8k items codes), explanation of delivery channel behaviors (webhook signing, auto-disable). 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?

Clearly states the verb 'create' and resource 'subscription to live-data event stream'. Lists supported types, distinguishing it from sibling tools like list_subscriptions and unsubscribe. The description effectively conveys the tool's 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?

Provides explicit prerequisite (Pipeworx OAuth account) and explains when to choose each subscription type with concrete examples. Does not explicitly contrast with alternatives like recent_alerts or list_subscriptions, but the context is sufficiently clear for an AI agent.

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

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

Annotations already indicate readOnly, idempotent, openWorld, and non-destructive. The description adds context about the return type (category-bucketed example questions with exact tool+argument shape) but does not contradict annotations. It provides helpful behavioral insight beyond the structured fields.

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

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

The description is informative but somewhat lengthy. It is front-loaded with example queries and provides key details. While every sentence adds value, it could be slightly more concise without losing meaning.

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

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

Given the tool's simplicity (0 required params, no output schema), the description fully explains what it does, when to use it, what it returns, and how the optional parameter works. It is complete for its purpose.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the optional `topic` parameter: 'Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus'. This clarifies usage beyond the schema description.

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

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

The description clearly states the tool is an onboarding entry point that suggests example questions, with specific verbs like 'returns category-bucketed example questions'. It distinguishes from siblings by positioning itself as the first tool to use when unsure about Pipeworx's 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, or to learn how to call the meta-tools'. Provides clear guidance on when to use it and mentions alternatives (the meta-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?

Beyond annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false), description adds key behavioral details: ownership enforcement, deactivation (not deletion), and that historical events remain accessible via recent_alerts. This comprehensively discloses side effects and constraints.

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 action and ownership, followed by clarification on deactivation. Highly 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 simple one-param tool with no output schema, description fully covers purpose, usage constraints, behavioral impact, and relation to sibling tool 'recent_alerts'. Complete and 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 the single parameter 'id' with 100% description coverage via input schema. Description does not add extra meaning beyond 'by id', so baseline score of 3 is appropriate.

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

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

Description clearly states 'Cancel a subscription by id', specifying the verb and resource. It also distinguishes behavior from siblings like 'subscribe' and 'list_subscriptions' by noting ownership enforcement and deactivation.

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

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

Description provides context: ownership enforced so only your own subscriptions can be cancelled. It implies when to use (you own the subscription) but does not explicitly state when not to or mention alternative actions like using 'subscribe' to create or 'list_subscriptions' to find IDs.

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 it's read-only, idempotent, and not destructive. The description adds significant behavioral details: return verdict types (confirmed, approximately_correct, refuted, inconclusive, unsupported), extracted structured form, actual value with citation, and percent delta. It also notes the v1 limitation to specific claim types, providing full transparency.

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

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

The description is a single paragraph but is front-loaded with trigger phrases and efficiently covers purpose, usage, and output. It packs a lot of information without being verbose, though it could be slightly more structured with bullet points for the return types.

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 one parameter, no output schema, and strong annotations, the description is comprehensive. It explains what the tool does, when to use it, what it returns (including the verdict and other details), and its limitations (v1 scope). 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% with a description for the single 'claim' parameter. The description adds value by providing concrete examples ("Apple's FY2024 revenue was $400 billion") and clarifying the type of natural-language claims that are acceptable, enriching the parameter meaning beyond the schema.

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

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

The description clearly states the tool is for claim verification with specific trigger phrases ("fact check", "verify the claim that..."), specifies the domain (company-financial claims for public US companies), and highlights that it automates a multi-step process, distinguishing it from any sibling tools that might perform similar tasks piecemeal.

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

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

The description explicitly says to use when the agent needs to check factual correctness of a user's statement, providing trigger examples. It also implicitly states when not to use by noting the limitation to company-financial claims. However, it does not explicitly name alternative tools for other claim types.

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