Ticketmaster

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the return format (per-model {score, confidence, signals, raw_response} + combined view) and cost implications (free default, BYO key for Anthropic), which goes beyond the annotation cues.

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 (3 sentences) and well-structured: first sentence states the core action, second adds model details and key requirement, third lists use cases. No filler or redundancy.

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

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

For a tool with 4 parameters and no output schema, the description covers the main functionality, model selection, return format, and use cases. It could briefly note how scores are interpreted or limitations, but overall it is adequately 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?

All parameters are described in the input schema (100% coverage). The description adds important behavioral context: the default model for the 'models' array and the requirement for '_apiKey' when probing Anthropic, which are not evident from 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 probes LLMs for knowledge about an entity and scores visibility per model. It specifies default vs. BYO key models and lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring), distinguishing it from siblings like 'scan_competitor_ai_presence' by being more general.

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

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

The description provides clear context on when to use this tool (AI-marketing audits, etc.) and explains the optional Anthropic key for extended probing. However, it does not explicitly mention when not to use it or list alternative sibling tools.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context beyond annotations: it returns structured answers with stable pipeworx:// citation URIs, works on every tier, and makes one fast call. 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 relatively long but well-structured with clear sections: preference statement, explanation of routing, examples, and guidance on alternatives. It front-loads the key message. A minor deduction for verbosity.

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

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

Given the tool's complexity (routing to thousands of sources), lack of output schema, and multiple siblings, the description is remarkably complete. It covers what the tool does, when to use it, alternatives, example queries, and return format (structured answer with citations). No gaps identified.

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 6 parameters with 100% description coverage, so baseline is 3. The description adds example queries and explains that the 'question' parameter accepts natural language, but does not add substantial meaning beyond what the schema already provides. The examples are helpful but not transformative.

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

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

The description clearly states the tool routes questions to a large set of sources for factual data, specifying verb 'routes the question' and resource '4,774 tools across 1242 verified sources'. It distinguishes itself from siblings by explicitly saying 'PREFER OVER WEB SEARCH' and listing alternatives like 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 guidance on when to use this tool (factual questions, structured data) and when not to use it (e.g., hallucination-resistant answers use ask_pipeworx_grounded, broad questions use deep_research). Also includes example queries and states 'START HERE for most 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds valuable context: hallucination resistance, exclusive use of tool results, explicit refusal reasons, and extra LLM call cost. 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 well-structured and front-loaded with key information. It is somewhat lengthy but every sentence serves a purpose, earning a high conciseness score.

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

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

The description covers return format (answer, evidence, confidence, etc.) and refusal reasons, and explains the tool's routing behavior. Given no output schema, this is complete and informative.

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 aliases fully documented. The description does not add significant meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes it from the sibling 'ask_pipeworx' by explaining that it extracts answers only from tool results. This provides a specific verb+resource description with clear differentiation.

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 specifies when to use ('whenever an answer will be quoted, cited, or acted on') and when not to use ('prefer ask_pipeworx for casual lookups'), including guidance on cost. This provides clear context and 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no behavioral context beyond that, missing details like potential authorization requirements or response structure.

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?

Extremely concise but at the expense of informativeness. The description is a fragment that lacks key details, making it insufficient for reliable tool selection and invocation.

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?

While an output schema exists, the description is too brief to cover the tool's purpose, parameter details, or behavioral traits. For a tool with no schema parameter descriptions, it fails to provide necessary 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 0%, and the description does not explain the parameters 'id' or 'locale'. No meaning is added beyond the schema, making it impossible for the agent to understand parameter formats or 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?

Description 'Single attraction.' is overly vague and barely more than a tautology with the title. It fails to specify the verb (e.g., retrieve, fetch) or the resource clearly, and does not distinguish from sibling tools like attraction_search or event.

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

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

No guidance provided on when to use this tool versus alternatives such as attraction_search. No context about prerequisites, input constraints, or expected use cases.

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 the tool as read-only, open-world, idempotent, and non-destructive. The description adds useful behavioral context: it uses the Ticketmaster Discovery API, supports specific filters, and returns particular fields (name, ID, classifications, images). This goes beyond the annotations without contradicting them, though it omits details like rate limits or pagination.

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

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

The description is a single, well-structured sentence that front-loads the tool's purpose (Ticketmaster Discovery API — search attractions) and briefly lists key filters and return fields. Every word adds value, with no filler or redundancy.

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

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

Given that the tool has an output schema (not shown) and zero schema-defined parameters, the description provides sufficient context: it names the filters and return fields. However, it lacks information about pagination, sorting, or error handling, which would be helpful for a search tool, but the core functionality is adequately covered.

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

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

The input schema is empty (no properties defined), yet the description names the parameters: keyword, classificationName, and other filters. This adds critical meaning that the schema alone does not convey. The description compensates for the lack of schema detail, although it does not specify parameter types or optionality.

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 action ('search attractions') and the resource (attractions via Ticketmaster Discovery API). It lists the return fields (name, ID, classifications, images) and filters (keyword, classificationName), which distinguishes it from sibling tools like 'attraction' (single lookup) and 'event_search'.

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

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

The description explains when to use the tool (searching for attractions with filters) but does not provide explicit guidance on when not to use it or how it compares to alternatives like 'attraction' for single lookups. The context is implied by the verb 'search' but lacks exclusions or direct sibling comparisons.

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

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

The description extensively documents behavioral traits beyond annotations: fan-out patterns, resolver contract with match confidence, parent event extraction, news fallback fields, safety short-circuits for low-confidence and closed markets, spread warnings, and resolution-rule risk. It aligns with annotations (readOnlyHint, idempotentHint) and adds critical context for safe usage.

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 verbose but well-structured, with clear sections (classifiers, fan-out examples, response shapes, resolver contract, etc.). Every sentence adds value, though the length could be slightly reduced for improved conciseness. The main purpose 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?

With no output schema, the description fully compensates by detailing the response shape (result.market, result.analysis, result.evidence, match confidence, parent event, news fields). It also covers edge cases like low-confidence matches, closed markets, wide spreads, and resolution-rule risk. Exceptionally complete for a complex tool.

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

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

All three parameters (market, depth, include_raw) are described in the schema with 100% coverage. The description adds meaning: explains depth values ('quick' vs 'thorough'), clarifies include_raw impact on response size, and gives examples for market input. This goes 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 it researches a Polymarket bet by pulling Pipeworx data, accepts multiple input formats (slug, URL, question text), and returns an evidence packet with market-vs-model comparison. It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on research and data gathering for a single bet.

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

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

Explicit use cases are given: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also provides examples of what to expect for different bet types. However, it does not explicitly state when not to use this tool or compare it to alternatives, though the specific use case is clear enough.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context about the return format: segment, genre, sub-genre hierarchy with names and IDs. This goes beyond annotations and clarifies the response structure.

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

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

The description is a single sentence that is mostly concise, though it includes the unnecessary prefix 'Ticketmaster Discovery API —' which adds no value for the agent. The core meaning is conveyed efficiently.

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 an output schema exists (as indicated by context signals), the description adequately covers the return value structure. It explains the hierarchical data returned (segment, genre, sub-genre). For a simple retrieval tool, this is sufficient.

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

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

Schema description coverage is 0%, so the description must compensate. The description mentions 'by ID' but does not explain the ID format or where to obtain it, and the 'locale' parameter is completely unexplained. This leaves significant ambiguity for an agent invoking the tool.

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 full details for a single classification by ID, with hierarchy information. It is specific (verb+resource+scope) but does not explicitly differentiate from the sibling 'classifications' tool, which likely handles listing. Thus a 4.

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

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

The description implies usage for retrieving a single classification, but provides no explicit guidance on when to use this tool versus alternatives (e.g., the sibling 'classifications' tool). No when-not or prerequisite information is 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 indicate readOnly, openWorld, idempotent, and non-destructive. The description adds context that the data comes from Ticketmaster Discovery API and includes hierarchical examples, enhancing understanding beyond annotations.

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

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

Two concise sentences with a clear structure: source, action, example, and use case. No unnecessary words.

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

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

Given zero parameters and an existing output schema, the description is complete. It covers the tool's source, functionality, example, and intended usage without gaps.

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

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

There are no parameters; baseline is 4. The description explains the purpose and output structure well, compensating for the lack of parameters by detailing what the tool returns (classification hierarchy).

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

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

The description explicitly states it lists all classification segments, genres, and sub-genres with a concrete example (Music > Rock > Alternative). It clearly distinguishes itself from the sibling 'classification' tool by emphasizing the list/collection aspect.

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: 'Use to discover valid classificationName values for filtering event_search.' It does not mention when to avoid using it or alternatives, but the guidance is direct and actionable.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive. The description adds rich detail: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. It also notes performance benefit ('replaces 8-15 sequential lookups'). 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?

One dense paragraph that efficiently conveys all necessary information. It could be slightly more structured (e.g., bullet points for key behaviors), but it remains concise and front-loads the purpose. Every sentence contributes 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 no output schema, the description adequately explains return values (paired data, citation URIs, sorted results). Covers edge cases like off-calendar fiscal years. Could mention error handling or limits beyond max 5, but overall complete for a well-annotated 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 parameters with descriptions. Description adds meaning: explains what data each entity type returns (e.g., company: latest 10-K revenue, net income, cash, debt; drug: adverse events, FDA approvals, trials). Specifies value formats (tickers/CIKs for companies, drug names) and constraints (2-5 items). Adds significant value beyond schema.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs, with specific example queries ('compare X and Y', 'X vs Y'). It distinguishes itself from sibling tools by stating 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', making its purpose unambiguous.

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

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

Provides explicit when-to-use guidance with examples and a strong preference directive over alternatives. Lacks explicit when-not-to-use scenarios, but the context is clear enough for an AI agent to apply correctly.

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?

Description adds behavioral details beyond annotations: parallel decomposition, return format (findings packet with evidence, confidence, source, gaps), timing (15-60s), and requirement for paid plan for thorough depth. No contradiction with readOnlyHint.

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 dense but every sentence adds value. It is front-loaded with essential account requirement. Slightly lengthy but justified by tool complexity.

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

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

Given no output schema, description thoroughly explains return structure (findings packet with fields), limitations (gaps when topic not in catalog), and behavior (parallel, routed to 4,774 tools). Fully covers what the agent needs to know.

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 meaning: depth parameter explains the number of facets (quick=3, etc.), and question parameter clarifies that broad/multi-part is acceptable.

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 'grounded multi-source research across Pipeworx's 1242 STRUCTURED data sources' in one call, distinguishing it from siblings like ask_pipeworx (single lookup) and note it is not open-web search.

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

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

Explicitly provides when to use (broad/multi-part questions) and when not to (breaking news, current events), and names alternatives (ask_pipeworx). Also specifies account requirements and plan restrictions.

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, idempotentHint true. The description adds that results include 'full input schemas (with curated examples)' and are 'ready to call directly, no second schema lookup needed.' This provides behavioral context beyond annotations without contradiction.

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

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

The description is a single clear paragraph with a strong front-loaded statement. It is concise but could be slightly more structured (e.g., bullet points for examples). Every sentence adds value.

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

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

Given 6 parameters (mostly aliases), no output schema, and a relatively simple purpose, the description covers what the tool does, when to use it, and the nature of results. It is complete for a discovery tool, though details on result ranking are absent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining that 'query' accepts natural language, listing example queries, and noting aliases. It also mentions 'top-N' relevance, which goes beyond schema details.

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: 'Find tools by describing the data or task.' It uses a specific verb ('find') and resource ('tools') and distinguishes from siblings by positioning it as a discovery mechanism to be called first.

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

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

The description explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set.' It also lists example use cases, though it doesn't explicitly state when not to use it. The context is clear enough.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context: fans out across multiple sources, notable patents soft-fail, parallel call. Could mention rate limits or latency but adds significant 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?

Front-loaded with example queries, then lists data sources and return fields. Every sentence adds value, though slightly verbose. Well structured for clarity.

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

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

Despite no output schema, description explains return structure (cik, company_name, recent_filings, etc.) and limitations (patents soft-fail). Covers complexity well; could include error handling or pagination details.

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

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

Schema coverage is 100% but description adds meaning: clarifies type is only 'company', value formats (ticker or CIK), and that names not supported. Provides examples and limitations, raising the value beyond schema.

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

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

The description clearly states the tool creates a full cross-source profile of US public companies with specific verb+resource. It lists example queries and distinguishes from chaining individual lookups, making it highly specific and distinct from siblings.

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

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

Explicitly advises prefer this over chaining single-pack lookups for holistic views and specifies when to use resolve_entity first. Provides clear when-to-use and when-not-to 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, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. However, the description 'Single event.' adds no behavioral context beyond what annotations provide. It does not contradict annotations, but it fails to add value such as explaining that this tool by ID or that no side effects occur.

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 two-word sentence, which is concise but severely underspecified. It does not earn its place because it provides almost no useful information. Effective conciseness would include key details without verbosity; this lacks substance.

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 2 parameters (one required), an output schema, and is one of many sibling tools, the description is incomplete. It does not explain the output format, how to interpret results, or the difference from event_search. The presence of an output schema partially mitigates the need to describe return values, but the overall lack of context harms usability.

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 0%, meaning no parameter descriptions exist in the schema. The description 'Single event.' adds no meaning to the parameters 'id' and 'locale'. The agent receives no help understanding what values are valid or how to use the parameters, especially 'locale'.

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 'Single event.' is extremely minimal. It vaguely indicates that the tool retrieves a single event, but does not specify the action (get/fetch) or the resource clearly. The title 'Event' and annotations suggest read-only retrieval, but the description alone is insufficient for an agent to confidently understand 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?

No usage guidance is provided. The description does not differentiate this tool from the sibling tool 'event_search' or explain when to use one over the other. An agent has no context for selecting this tool versus alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the agent knows it is a safe read operation. The description adds minimal behavioral context (e.g., does not specify whether images are URLs or binary data). With annotations present, a score of 3 is appropriate.

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

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

The description is extremely concise (3 words) and front-loaded. While it is clear, it could benefit from slightly more detail (e.g., 'Retrieve images for an event by its ID') without losing conciseness.

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

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

Given the simple input (one required parameter) and presence of an output schema, the description is minimally adequate. However, it does not explain the intended use or any edge cases, making it less complete for an AI agent unfamiliar with the domain.

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

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

The single parameter 'id' has no description in the schema (0% coverage). The description implies 'id' is an event identifier, providing basic semantic context. However, it does not specify the format or example values, limiting its usefulness 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 'Images for an event.' clearly indicates the tool returns images related to an event, distinguished from siblings like 'event' or 'event_search' which return event details or search results. However, it lacks a verb and could be more explicit about the operation (e.g., 'Get' or 'Retrieve').

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

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

No usage guidance is provided. The description does not specify when to use this tool over alternatives (e.g., 'event' for general info, 'venue_search' for venues) or any prerequisites or exclusions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that it returns events with dates, venue, and ticket links, and mentions the third-party source (Ticketmaster). It does not disclose behavior like pagination, rate limits, or default sorting, so additional value is modest.

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, efficient sentence that front-loads the core purpose. It lists parameters but could be more structured (e.g., bullet points). No wasted words, but slight improvement in organization would elevate it.

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?

An output schema exists, so explaining return values is not required. However, the input parameters are not defined in the schema, so the description must compensate. It lists parameters but lacks details on types, requiredness, and formats (e.g., date format). The examples in the schema help, but the description alone is incomplete for an agent to correctly format parameters.

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

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

The input schema has no properties defined (empty), so the description must and does compensate by listing many parameter names (keyword, city, countryCode, etc.) and providing examples. This adds critical meaning beyond the schema. Despite schema coverage being 100% for zero parameters, the description provides essential parameter semantics.

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 searches live events via the Ticketmaster Discovery API, listing specific filterable fields (keyword, city, countryCode, etc.). The verb 'search' and resource 'live events' are specific, and the tool is easily distinguished from siblings like 'event' (single event lookup) and 'event_images'.

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 lists many search parameters, implying when to use it (for finding events by criteria). However, it does not explicitly specify when not to use it or compare it to alternatives like 'attraction_search' or 'venue_search'. The context is clear but lacks exclusions.

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

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

Annotations declare destructiveHint=true and readOnlyHint=false, and the description reinforces the delete action. Adds context about clearing sensitive data, which aids 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?

Two sentences efficiently convey purpose and usage guidance with no extraneous information.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose, usage context, and sibling pairing, making it fully adequate.

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

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

Schema coverage is 100% with clear parameter description. Description adds no new semantics beyond 'by key', so baseline 3 applies.

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

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

The description clearly states the tool deletes a memory by key, using specific verb (delete) and resource (memory), distinguishing it 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?

Provides explicit when-to-use scenarios: stale context, task done, clear sensitive data. Mentions pairing with remember and recall, but lacks explicit when-not-to-use instructions.

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 explains the internal steps: fetch page, extract title/description/key links, emit markdown. This aligns with annotations (read-only, idempotent, non-destructive) and adds detail beyond what annotations provide. No contradictions.

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

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

The description is concise at three sentences, front-loaded with the core purpose, then technical details, then use cases. Every sentence adds value with no redundancy.

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

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

For a simple tool with 2 parameters and no output schema, the description fully covers what the tool does, what it outputs, and why it's useful. No gaps remain.

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

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

The description adds value beyond the schema by providing examples for the 'url' parameter and stating default and max for 'max_links'. With 100% schema coverage, the description still enriches the 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?

The description clearly states the action ('Generate a production-ready llms.txt file'), the resource ('for any URL'), and the specific use cases. It distinguishes from sibling tools like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on generating the standard llms.txt format.

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

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

The description gives explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitors. It does not explicitly mention when not to use or list alternative tools, but the examples provide clear guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds return field details and default behavior (active only), but no additional behavioral traits beyond schema. Moderate added value.

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 efficient sentences: first states purpose and output fields, second gives usage guidance. No wasted words.

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

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

For a simple list tool with rich annotations, description covers purpose, usage, and return fields. Lacks pagination info but adequate for selection and 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 covers 100% of parameters with descriptions. Description repeats the parameter's existence but adds no new meaning beyond schema.

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

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

Description clearly states verb (list), resource (subscriptions), and scope (caller's active). Lists key return fields, distinguishing it from sibling tools like subscribe/unsubscribe.

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 advises using this tool to review monitoring before adding more or to find an ID to cancel. While no alternative is named, context with sibling tools makes usage 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 declare readOnlyHint=false etc., and the description adds context: it's free, rate-limited, and how the team uses the feedback. 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 covering all key aspects. Front-loaded with purpose. No unnecessary words.

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

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

For a feedback tool with no output schema, the description covers purpose, usage triggers, rate limits, and parameter guidance completely.

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

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

Schema coverage is 100%, but description adds extra guidance: 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt' and for message 'Be specific...'. Slightly above baseline 3.

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

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

The description states exactly what the tool does: send feedback to the Pipeworx team. It specifies the types (bug, feature, data_gap, praise) and distinguishes from siblings that are query/action tools.

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

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

Explicit when-to-use guidance: 'Use when a tool returns wrong/stale data...when a tool you wish existed isn't in the catalog...or when something worked surprisingly well.' Also mentions rate limits and quota exemption.

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 non-destructive behavior. The description adds valuable context: data source (CF analytics-engine), no PII, and caching behavior (5min-1h). No contradictions.

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

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

The description is concise (95 words) and front-loaded with the main purpose. Every sentence adds information without redundancy.

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

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

Given the simple schema (1 optional param, no nested objects, no output schema), the description fully covers tool functionality, usage context, and behavioral traits. Return format is implied by the purpose statement.

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

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

Schema coverage is 100% with clear enum and description. The description enriches this by explaining the semantic difference between window choices: shorter windows for hot trends, longer for 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. It specifies the verb 'returns' and the resource 'trending data from Pipeworx', distinguishing it from siblings by its unique focus on aggregate call volume trends from other AI agents.

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

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

Three explicit use cases are provided: discovering hot data sources, confirming canonical choices, and checking alignment. While it does not mention alternatives or when not to use it, the guidance is contextually sufficient for the tool's specific purpose.

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 readOnly, idempotent, non-destructive, and open-world. The description adds extensive behavioral context: internal checks (monotonicity, partition, Jaccard similarity, placeholder filters), fill check logic, and response structure. It warns about realizable_edge_pp ≤ 0 meaning not to trade, providing 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?

The description is long but well-structured with labeled sections for modes, filters, and fill check. It front-loads the main purpose. While dense, every sentence provides necessary detail. Could be slightly more concise but is effective.

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

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

Given the tool's complexity (multiple modes, algorithmic checks, no output schema), the description thoroughly covers behavior, edge cases (low similarity, placeholder filtering), and trade execution constraints. It references sibling tools where appropriate, making it complete for agent decision-making.

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

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

Schema coverage is 100% with descriptions for both parameters. The description enriches this by explaining what each parameter does internally (e.g., event accepts slugs or full URLs; topic is a seed question) and the corresponding mode behavior. This adds semantic value beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between three modes (trending_scan, event, topic) and is specific about verb and resource, differentiating it from siblings like polymarket_edges and polymarket_fill_risk.

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

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

The description provides clear guidance on when to use each argument: no args for trending scan, 'event' for a specific market (recommended), and 'topic' for cross-event scanning. It also advises using polymarket_fill_risk for custom sizing. However, it does not explicitly state when not to use the tool.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant context: caching at KV level for 1 hour, model families and their calculations, response structure, tradeable-edge knobs, and diagnostics for empty segments. 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 a single dense paragraph with a lot of technical detail. It front-loads the main purpose but is not structured with sections. While comprehensive, it could be more concise and easier to parse for 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?

Despite lacking an output schema, the description thoroughly explains the response structure (by_segment, diagnostics, Fed note), edge calculations, and caching. It covers edge cases and provides enough detail for an agent to understand what the tool 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?

Schema coverage is 100% with detailed descriptions. The description goes beyond by explaining the purpose of each parameter in context (e.g., min_liquidity filters thin books, slippage_pp accounts for expected execution costs). This adds meaning beyond 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 scans Polymarket markets to find opportunities where Pipeworx data disagrees with market price. It is built for the use case 'what should I bet on today' and distinguishes itself from siblings by focusing on edge detection without paging hundreds of markets.

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

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

The description provides clear context for when to use the tool (discovering trading opportunities) but does not explicitly mention when not to use it or compare it to siblings like polymarket_arbitrage. However, it explains the intended audience and the automatic discovery nature.

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

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

Annotations already indicate readOnlyHint, idempotentHint, etc. The description adds substantial behavioral details: the response structure (tracked, expired, snapshot_dates), fields like trend and decay_pp_per_day, and limitations (60-day TTL, decay from daily closes). It also explains gaps in snapshot dates. This goes well 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?

Despite being long, every sentence is purposeful. The description is front-loaded with the key question and purpose, followed by structured details via a RESPONSE section. No wasted words; all information is relevant and well-organized.

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

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

With no output schema, the description fully explains the return structure: tracked[], expired[], snapshot_dates[], including fields like first_seen, trend, decay_pp_per_day, and lifespan_days. It also covers limitations and data gaps. For the parameter count and complexity, 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%, providing baseline 3. The description reiterates and slightly expands on parameter meanings: 'days (lookback, default 14, max 30)' and 'window (snapshot family, default "1wk")' adds context about their roles in snapshot retrieval. This adds value 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 opens with a specific verb+resource: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It clearly answers the question 'how long has this edge existed and is it shrinking?' and distinguishes from the sibling tool polymarket_edges by focusing on historical tracking rather than current edge data.

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

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

The description provides clear context: it explains that a fresh wide edge and a 3-week-old wide edge are different trades, implying when to use this tool for assessing edge persistence. However, it does not explicitly state when not to use it or mention alternatives, though the sibling polymarket_edges is the likely alternative for current edges.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context beyond annotations, such as walking the ladder, returning verdicts (clean|degraded|cannot_fill), and warning about forced directional risk and thin legs. 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 relatively long (approx. 200 words) but front-loaded with the core purpose and structured with key phrases like 'REQUIRES' and 'USE THIS'. Every sentence provides necessary detail given the tool's complexity; however, some redundancy exists (e.g., repeating default values). Still well-organized.

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 (two modes, no output schema, 4 parameters), the description is highly complete. It details return fields for each mode, the verdict system, profit calculations, risk of partial fills, forced directional risk, and usage conditions. It compensates well for the lack of an output schema.

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

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

Although schema description coverage is 100% (all 4 parameters described), the description adds significant meaning: it explains the dual-mode interpretation of parameters, default values, the meaning of size_usd in each mode, and the auto-detection logic for side in basket mode. This goes well beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes with specific examples and contrasts with sibling tools like polymarket_arbitrage and polymarket_edges, explicitly stating that it should be used before acting on their signals.

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 usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (theoretical overround not capturable on thin books, partial fills cause unhedged directional positions) and gives conditions for each mode.

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; description adds rich safety fields (compatibility_warning, temporal_alignment, skipped_cross_type) that explain failure modes and data quality, going well beyond the annotation alone.

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 thorough but slightly verbose (~200 words). However, it is well-organized with sections (modes, response, safety fields) and uses capital letters to highlight key terms, making it scannable despite 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?

No output schema, so the description must explain return values; it does so clearly (leg prices, matched spread, safety fields). All parameter details are covered, and examples are provided. The description fully prepares an agent to use the tool correctly.

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

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

All three parameters are documented in the schema (100% coverage). The description adds meaning by explaining the two modes (topic vs explicit), showing examples, and describing how parameters interact when overridden.

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

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

The description clearly states the tool computes cross-venue spreads between Polymarket and Kalshi, with two specific modes and a note distinguishing from intra-venue tools like polymarket_arbitrage. The verb 'spread' and resource 'cross-venue' are precise.

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 explains two usage modes (topic shortcuts vs explicit tickers) and warns that pre-mapped topics may not yield tradeable spreads. Does not explicitly list when not to use, but sibling tool polymarket_arbitrage implies intra-venue use cases are separate.

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 (readOnly, idempotent), description adds key behavioral details: omitting key lists all, scoped to identifier, and pairing with remember/forget.

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 covering action, use case, and pairing; no wasted words, front-loaded with purpose.

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

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

Fully covers the tool's behavior given single optional parameter, no output schema, and strong annotations. No gaps.

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

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

Schema coverage is 100%, but description adds value by explaining the listing behavior when key is omitted and scoping context 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 the tool retrieves a saved value or lists all keys when key is omitted, and distinguishes it from siblings 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?

Explicitly tells when to use (look up context stored earlier) and pairs with remember/forget, providing clear context and 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 declare readOnly, idempotent, non-destructive. Description adds that mark_read flags events as read, affecting subsequent calls. 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?

Four sentences with no wasted words. First sentence states purpose, second describes return fields, third covers filters, fourth adds behavioral note and alternative access. Properly 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?

Covers core functionality, filtering, mark_read behavior, and alternative access. Lacks explicit explanation of unread_only and default behavior when no filter is given, but overall complete for a list tool with good schema.

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

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

Schema coverage is 100% with descriptions. Description adds concrete examples (e.g., 'sec_8k' for type) and clarifies mark_read's effect on future calls, 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 pulls fired events from the subscription feed, listing key fields (source, citation_uri, raw payload). It uses a specific verb and resource, distinguishing it from generic event 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?

Provides explicit guidance on using mark_read to control read state and mentions polling suitability. Also offers an alternative access method via API endpoint. Does not explicitly exclude sibling tools but gives enough context for typical use.

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

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

The description explains the fan-out to multiple sources, fallback logic (GDELT to GNews), soft-failure for USPTO, and the structure of the return value, all of which add value beyond the annotations that indicate safety and idempotence.

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 useful information but slightly verbose due to example queries and multiple sources. It is well-structured and front-loaded with use cases, but could be trimmed by a line or two.

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

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

Given no output schema, the description adequately covers return structure (changes[], total_changes, URIs) and handles edge cases like fallback and API sunset, making it complete for a multi-source tool.

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

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

The description adds significant context to each parameter beyond the schema: explains the 'since' format with examples, restricts 'type' to 'company', and clarifies that 'value' can be ticker or CIK, plus a usage recommendation.

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

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

The description explicitly defines the tool as a change feed for a company using multiple sources (SEC EDGAR, GDELT/GNews, USPTO) and provides query examples that distinguish it from the sibling tool entity_profile, which is for static profiles.

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 clear when-to-use scenarios (e.g., 'What's new with X' queries) and explicitly states when not to use (use entity_profile for static profiles), with a named 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?

Discloses key-value scoping by identifier, persistent vs. 24-hour retention based on auth status. Adds value beyond annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false). Does not explicitly discuss idempotency but does not contradict 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?

Four sentences, front-loaded with purpose, no wasted words. Efficient and clear.

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?

Completely describes a simple key-value store with retention policy and pairing with recall/forget. No output schema is needed given the simplicity.

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 good descriptions for key and value. Description adds scoping context but does not significantly extend beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool saves data for reuse across conversations/sessions, using specific verb 'save' and resource 'data'. It distinguishes from sibling tools 'recall' 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?

Explicitly says 'Use when you discover something worth carrying forward' and pairs with alternatives recall and forget. Provides clear context for appropriate use.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. Description adds valuable context: 'cascades through several lookup endpoints' and 'replaces 2-3 manual lookups', enhancing understanding of internal complexity without contradicting 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?

Front-loaded with example phrases, then expands systematically. While slightly verbose, every sentence adds value with minimal redundancy. Could be slightly tighter but still efficient.

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

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

Given the tool's two entity types, multiple input formats, and rich returns (including citation URIs), the description covers all necessary aspects despite lacking an output schema. Provides enough detail 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 coverage is 100%, but the description significantly enriches meaning by providing examples of accepted values (ticker, CIK, name for company; brand/generic for drug) and detailing the output structure (e.g., ticker+CIK+URI for company).

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's purpose: resolving names to canonical IDs. Provides concrete examples ('What's the ticker for...') and distinguishes from sibling tools by emphasizing its role as a prerequisite for other tools.

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

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

Explicitly states 'Use FIRST whenever you have a name but need an ID', giving a clear when-to-use directive. Also details supported entity types and output formats, guiding the agent on appropriate scenarios.

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?

Adds significant behavioral context beyond annotations: explains it probes each entity using ai_visibility_check, ranks results, and returns specific fields (score, confidence, signal density). Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false, and description does not contradict them.

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

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

Two tight sentences front-load the main action and purpose, with no wasted words. Every sentence contributes.

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 what the tool does, how it works internally, and what output format to expect (ranked list). No output schema, so the description compensates adequately. All key behavioral aspects are covered.

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

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

Adds meaning beyond schema by clarifying that the first entity in the 'entities' array is treated as the subject for narrative purposes, and explains the purpose of the 'context' parameter. Schema already has 100% coverage, so this is extra value.

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

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

Description clearly states the verb 'compare', the resource 'AI visibility across multiple entities', and differentiates from sibling 'ai_visibility_check' by focusing on side-by-side comparison and ranking. Provides a concrete 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 describes when to use (competitive AI-marketing audits) and implies context by contrasting with single-entity probing. Lacks explicit 'when not to use' or direct alternatives, but the sibling tool 'ai_visibility_check' is implicitly the 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 already indicate read-only, open-world, idempotent, non-destructive nature. The description adds: fans out across deps.dev and bundlephobia, graceful degradation, bundlephobia first measurement can take 5-30 seconds, and NPM-only scope. This significantly enriches the behavioral understanding beyond annotations.

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

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

The description is relatively long but efficiently structured. The first sentence states the core purpose, followed by return definitions, ecosystem scope, and failure behavior. Every sentence adds value, though it could be slightly streamlined without losing 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?

Despite no output schema, the description lists all return fields (summary block with specific keys, per-advisory detail, links, alternative versions). It covers key operational details like NPM-only scope and partial failure handling. For a composite multi-source tool, this is comprehensive.

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

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

Schema has 2 parameters with full descriptions (100% coverage). The description adds that scoped packages (e.g., '@types/node') are accepted and that version defaults to latest when omitted. While the schema already covers basic meaning, this additional nuance earns a slightly higher score than baseline 3.

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

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

The description clearly states it is a composite check for 'should I add this npm package to my project', covering licenses, advisories, version history, and bundle size. It specifies the exact verb ('scan') and resource ('dependency'), and distinguishes itself by noting that other ecosystems fall under a different tool (deps.dev:version directly).

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

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

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It provides clear context on what the tool covers (npm ecosystem only) and what doesn't (other package managers), and explains partial failure behavior (bundlephobia timeout).

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?

Adds significant behavioral context beyond annotations: embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character limit (200K chars with truncation and flagging), return format (passages with offsets and scores). No contradiction with annotations.

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

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

Single dense paragraph front-loaded with main purpose and use case. Every sentence adds value—no fluff. Technical details (embedding, window, limit) are efficiently packed.

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 moderate complexity (3 params, no output schema), the description covers purpose, usage, parameters, return format, technical constraints (200K char cap), and relationships to sibling tools. Nothing is missing for a competent 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 baseline is 3. Description adds examples for text and query (SEC 10-K, article, natural-language query examples) but does not introduce new parameter semantics beyond the schema. The additional context is nice but not essential.

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?

Explicitly states 'Semantic search INSIDE a fetched record.' The verb 'search' and resource 'record' are specific. Distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and context-saving 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?

Tells when to use: 'Use when the record is too big to cram into the prompt.' Mentions alternative tool (ask_pipeworx_grounded). Could be stronger on when not to use, but the guidance is clear and actionable.

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

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

Annotations indicate a write operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds context: OAuth requirement, SMS cap, webhook auto-disable after failures, and a one-time signing secret. This goes beyond annotations, though idempotency (annotated true) is not explicitly addressed.

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

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

The description is detailed but well-organized, with the core purpose first, then requirements, type details, and delivery options. Each sentence adds necessary information, though slightly verbose—could be tightened without loss.

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

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

Given the tool's complexity (3 parameters, nested objects, no output schema), the description covers all necessary aspects: return value (id), prerequisites, type-specific param formats, delivery channels, and constraints like SMS cap and webhook signing. It provides complete guidance 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 coverage is 100%, but the description enriches parameters with concrete examples (e.g., sec_8k params, delivery constraints). It clarifies enum meanings, required fields per type, and delivery behaviors, adding substantial value beyond the schema.

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

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

The description starts by clearly stating the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream.' It specifies the verb (create), resource (subscription), and context (live-data event stream), distinguishing it from sibling tools like list_subscriptions and unsubscribe.

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 requirements (Pipeworx OAuth account) and illustrates supported types and delivery channels with examples. It does not explicitly state when not to use this tool versus alternatives, but the context signals and sibling names imply 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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing a safe, read-only profile. The description adds value by specifying the return format (matching events, attractions, venues ranked by relevance) and the source (Ticketmaster Discovery API), which goes 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 consists of two concise sentences: the first clearly states the tool's purpose and output, and the second provides usage guidance. No unnecessary words or repetition, and the critical 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?

For a single-parameter tool with strong annotations and an output schema, the description adequately covers the input (keyword), the output (ranked results), and usage guidance. It is complete enough for an AI agent to correctly select and invoke the tool.

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

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

The input schema has 0% description coverage for the single parameter 'keyword,' but the tool description explains it as 'autocomplete suggestions for a keyword,' clarifying its role. This compensates for the schema's lack of textual description, providing adequate semantic context.

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

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

The description explicitly states the tool provides autocomplete suggestions for a keyword, returning events, attractions, and venues ranked by relevance. This clearly distinguishes it from sibling search tools like 'event_search' and 'attraction_search'.

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

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

The description advises using the tool 'to resolve partial names before querying other endpoints,' providing explicit context for when it is appropriate. However, it does not specify when not to use it or mention alternatives beyond the implied 'other endpoints.'

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 convey idempotent and non-destructive. The description adds behavior details: it returns a spread of questions, can be focused via topic, and draws from a live catalog. One could want pagination or size info, but it's sufficient.

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 usage intent and examples, then provides usage instructions. Although somewhat lengthy, every sentence serves a purpose: stating alternate phrasings, listing categories, explaining the return value, and giving calling conventions.

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

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

Given the tool is simple (one optional parameter, no output schema), the description fully explains the purpose, return format, and usage. It covers both full spread and focused calls, and mentions what the returned examples include (tool + argument shape).

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 is already present in the schema. The description adds context about focusing the spread and gives examples, but does not add meaning substantially 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 the onboarding entry point, returning category-bucketed example questions with exact tool and argument shapes. It distinguishes from sibling tools by specifying that it's for learning what Pipeworx can do and how to call meta-tools.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. It also provides guidance on when to call with no arguments vs passing a topic, and implies alternatives like ask_pipeworx.

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 readOnlyHint=false, destructiveHint=false, etc. The description adds meaning by explaining ownership enforcement and that deactivation (not deletion) keeps historical events available. 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, 22 words, front-loaded with the main action. Every sentence is necessary and adds value, with no fluff or repetition.

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

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

For a simple tool with one parameter and no output schema, the description covers the action, constraints, and side effects (historical data retained). Could mention idempotency explicitly, but overall complete enough.

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

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

Schema covers 100% of parameters, with 'id' described as 'Subscription id (uuid) returned by subscribe.' The description adds no extra parameter detail, so it meets the baseline but doesn't enhance semantics.

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

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

The description clearly states the action 'Cancel a subscription by id', specifying the verb and resource. It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by clarifying ownership enforcement and the deactivation effect.

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 context on when to use: only for own subscriptions due to ownership enforcement, and explains the deactivation effect versus deletion. It lacks explicit alternatives but clearly distinguishes from a full delete, guiding the agent.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable context: the return format (verdict types, structured form, actual value with citation, percent delta) and the fact that it replaces multiple sequential calls. 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 moderately sized and front-loaded with example phrases. Every sentence contributes value, though it could be slightly more concise. Overall well-structured without unnecessary repetition.

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 single parameter and no output schema, the description adequately covers the return types, domain, and scope. It explains when to use and what to expect. Slight gap in explicitly stating limitations, but the scope is clearly defined as company-financial claims.

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 more detail about the natural-language nature and provides concrete examples, enhancing understanding beyond the schema definition.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It provides multiple example queries and explicitly distinguishes the tool by noting it replaces 4-6 sequential calls, differentiating it from sibling tools.

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

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

The description explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It specifies the domain (company-financial claims) and scope (public US companies). It could be improved by explicitly mentioning when not to use, but the context is clear enough.

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

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

The description adds no behavioral context beyond the annotations which already declare read-only, idempotent, and non-destructive traits. No extra value provided.

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?

Extremely concise at two words, but this is underspecification rather than efficient clarity. Still not verbose, so a middle score.

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 2 parameters and an output schema, the description should at least state that it retrieves a venue by ID. Currently it's incomplete and doesn't leverage the existing structured information.

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 0%, and the description does not explain the purpose of 'id' or 'locale' parameters. The agent gets no help understanding what to provide.

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 'Single venue.' is minimal but does indicate the tool is about a specific venue. The name and sibling 'venue_search' suggest this retrieves details for one venue, but no verb is given.

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

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

No guidance on when to use this tool versus alternatives like 'venue_search' or other entity tools. The agent has no direction about context.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, covering safety and idempotency. The description adds that it returns venue name, address, city, state, capacity, and coordinates, providing useful context about the output without contradicting annotations.

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

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

The description is a single, well-structured sentence that front-loads the API source and core purpose. It packs key information efficiently 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 the presence of annotations and an output schema, the description covers what the tool does and returns. The major gap is the empty input schema, which the description partially fills, but it could specify parameter types or requiredness for full completeness.

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

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

The input schema is empty (no properties), so schema description coverage is trivially 100%. The description lists parameters not in the schema, adding value, but lacks detailed semantics like data types or required fields. Examples partially compensate.

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

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

The description clearly identifies the tool as a venue search using the Ticketmaster Discovery API, listing specific search parameters and return fields. It distinguishes from sibling tools like 'venue' (which likely retrieves by ID) and 'event_search'.

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

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

The description enumerates valid search parameters (keyword, city, stateCode, countryCode, postalCode), making the usage context clear. However, it does not explicitly state when to prefer this tool over alternatives like 'venue' or 'event_search', so some agent inference is required.

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