Seatgeek

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds behavioral context: default model is free (Workers AI Llama-3.3-70b), passing _apiKey enables Anthropic with BYO key, and returns per-model {score, confidence, signals, raw_response} + combined view. 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 two sentences long, front-loaded with main purpose, and every sentence adds essential information without redundancy. It is highly efficient.

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

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

Given 4 parameters, no output schema, and moderate complexity, the description covers all needed aspects: parameter semantics, return structure, use cases, and pricing. It is fully adequate for agent selection.

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. The description adds value by clarifying default model, free vs paid usage, that _apiKey is passed directly to Anthropic, and that context disambiguates. This goes beyond the basic schema descriptions.

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

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

The description clearly states the tool's purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It uses a specific verb-resource combination and differentiates from siblings like 'scan_competitor_ai_presence' by emphasizing entity-agnostic probing.

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

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

The description explicitly lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not mention when not to use or alternatives, but the context is clear enough for an AI agent to decide when to invoke this 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?

Discloses key behaviors: routes to 4,462 tools across 1,129 sources, returns structured answer with stable pipeworx:// citation URIs, works on every tier, one fast call. Annotations already provide readOnly/openWorld/idempotent hints, and description adds valuable context beyond that.

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

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

Well-structured with front-loaded key guidance ('PREFER OVER WEB SEARCH'). All sentences add value, though slightly verbose. Could be tighter but still effective.

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

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

Comprehensive given no output schema: covers purpose, usage, behavior, parameters, and examples. Addresses all necessary context for 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?

Schema coverage is 100% with all parameters described. The description adds minimal extra meaning beyond schema (e.g., examples of valid questions). Per guidelines, baseline 3 is appropriate when schema covers parameters fully.

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: it answers factual questions by routing to authoritative sources with citations. It uses specific verbs ('routes', 'returns') and distinguishes from siblings 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?

Explicitly advises to prefer over web search, provides when-to-use ('START HERE for most questions') and when-to-step-up to alternatives (ask_pipeworx_grounded for single answers, deep_research for multi-part). Also mentions breaking news coverage.

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

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

Annotations already provide safety profile (readOnly, idempotent). Description adds valuable behavior: extracts only from tool results, returns structured refusals on various failure modes, and costs one extra LLM call. 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?

Front-loaded with purpose and alternatives, then return format and usage guidance. Every sentence adds value; no fluff.

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

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

Given no output schema, description fully specifies return format (answer, evidence, etc.) and refusal reasons. Covers edge cases and cost trade-off.

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?

100% schema coverage with 6 alias parameters. Description clarifies they accept question in natural language, adding that aliases are accepted. Adequate but not exceptional 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 'hallucination-resistant answer mode' and contrasts with ask_pipeworx, distinguishing the tool as extracting answers using only tool results. Verb 'extracts' plus resource specification provides high clarity.

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

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

Explicit guidance: 'Use whenever an answer will be quoted, cited, or acted on...' and 'prefer ask_pipeworx for casual lookups.' Tells when to use and when not, with specific high-stakes examples.

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 is exceptionally detailed about behavior: it explains the resolver contract (market_match_confidence, score, alternatives, suggestions), parent event extraction, news fallback mechanisms, safety short-circuits (low-confidence, closed markets), wide-spread handling, and resolution-rule parsing. This far exceeds what annotations (readOnlyHint, idempotentHint) provide, offering crucial decision-making context.

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

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

The description is long but well-structured, with the core purpose up front followed by categorized details (classifiers, fan-out examples, response shapes, safety). Every sentence adds value, but some sections (e.g., specific fan-out examples) could be condensed without losing essential information. It balances completeness with readability.

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

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

Given the complexity of the tool (3 parameters, no output schema, intricate behavior), the description is remarkably complete. It covers input handling, classification, fan-out logic, response structure, edge cases (closed markets, low confidence, wide spreads), resolution-rule risk, and fallback mechanisms. No critical gaps are apparent.

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

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

Schema coverage is 100%, so the schema already documents all three parameters. The description adds meaning by explaining input formats (slug, URL, question text), depth values (quick vs. thorough with example outputs), and include_raw's impact (response size). This provides practical guidance beyond the schema's property descriptions.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data. It specifies three input types (slug, URL, question text) and the overall process: resolve, classify, fan out, return evidence and comparison. It distinguishes itself from sibling tools by focusing specifically on Polymarket bets with a structured evidence-gathering process.

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: 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'. It also provides examples of fan-out for different bet types. However, it does not explicitly state when NOT to use this tool or compare it to alternatives among the sibling tools, leaving some ambiguity.

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

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

Beyond annotations (readOnlyHint, idempotentHint, openWorldHint), the description reveals specific data sources (SEC EDGAR/XBRL, FAERS, FDA, clinical trials), sorting behavior, handling of off-calendar fiscal years, and output format (paired data + citation URIs). This adds significant 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?

The description is front-loaded with example queries and efficiently covers behavior, type differences, output, and benefits. Every sentence adds value, though slightly lengthy. No wasted words.

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

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

Given two parameters, enum, and no output schema, the description covers inputs, data sources, sorting, and output format. It is sufficient for an agent to use correctly, though could mention potential limitations like data freshness or error handling.

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 what data each type (company/drug) retrieves and provides examples of valid values (tickers, drug names). This goes beyond the schema descriptions.

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

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

The description clearly explains the tool's purpose: side-by-side comparison of 2–5 companies or drugs in a single parallel call. It provides example queries like 'compare X and Y' and 'rank these companies,' and distinguishes itself from siblings by replacing sequential lookups.

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

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

The description explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear when-to-use guidance. However, it does not mention when not to use or what alternative tools to use for non-comparison tasks.

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: account requirement, paid plan for 'thorough' depth, output format (findings packet with evidence, confidence, source, citation, gaps), timing expectation (15-60s), and that it never invents data. 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?

Long but well-structured with sections: account requirements, use cases, inner workings, output description. Every sentence adds value, though could be slightly more concise 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?

Very complete for a complex tool with 2 parameters and no output schema. Covers input, output, timing, caveats, and comparisons. Minor omissions (e.g., rate limits, handling of overly broad questions) are acceptable given the thoroughness.

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%; both parameters have descriptions. Description adds value by explaining depth options (quick=3, standard=5, thorough=8) and that broad questions are acceptable. Marginally improves on schema.

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

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

Clearly describes grounded multi-source research across structured data, decomposing questions into facets and routing to tools in parallel. Distinguishes from sibling tools like ask_pipeworx (single lookup) and ask_pipeworx_grounded.

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

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

Explicitly states when to use (broad/multi-part questions over structured data) and when not (single lookup, breaking/current news). Also provides account requirement and fallback (ask_pipeworx if not signed in).

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, idempotentHint, and non-destructive nature. The description adds behavioral context: it returns top-N relevant tools with names, descriptions, and full schemas, and notes that results are ready to call directly without a second schema lookup. This extra detail goes beyond annotations and confirms the tool's read-only, safe characteristics.

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, front-loading the core purpose and then providing examples and return value. It is somewhat lengthy but every sentence adds value. Minor improvement could be trimming redundant listing of data types.

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

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

The description covers the tool's purpose, usage, parameters, and return value (top-N tools with names, descriptions, schemas). Given the lack of an output schema, the description adequately explains what to expect. No missing critical 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?

The input schema has 100% coverage with descriptions for all 6 parameters. The description reinforces the primary 'query' parameter with natural language examples and explains aliases (q, task, search, description). This adds semantic clarity beyond the schema alone.

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

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

The description clearly states 'Find tools by describing the data or task' and provides specific use cases (SEC filings, FDA, etc.). It distinguishes itself from sibling tools by explicitly stating 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' The purpose is unambiguous and differentiates this tool as a discovery gateway.

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 when you need to browse, search, look up, or discover what tools exist for: ...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This clearly indicates when to use this tool instead of directly using specific data tools.

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

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

The description details the tool's behavior: it fans out across multiple sources, returns specific fields (cik, filings with URIs, fundamentals sorted, patents with a soft-fail caveat, news, LEI). This adds value beyond annotations (which indicate read-only and idempotent) by explaining the internal fan-out and potential failure modes.

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

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

The description is a single, well-structured paragraph that front-loads the main purpose and examples. Every sentence adds value: usage guidance, input format, output details, and caveats. It is concise while being comprehensive.

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

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

For a complex tool pulling from multiple APIs, the description covers inputs, outputs, caveats (USPTO sunset), and usage preferences. Despite no output schema, the description lists expected return fields. The context is complete for an AI agent to invoke appropriately.

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 enriches the schema by clarifying that 'type' is limited to 'company' and 'value' must be a ticker or zero-padded CIK, with explicit examples and a note that names require a prior call to resolve_entity. This provides practical context beyond the schema's type and description.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It specifies the sources (SEC EDGAR, XBRL, USPTO, news, GLEIF) and distinguishes from chaining individual lookups, making the purpose unambiguous and differentiated 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?

Explicit guidance is provided: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also notes when not to use (names not supported, requiring resolve_entity first). Examples of queries are given, clarifying appropriate usage.

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

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

Annotations already mark as readOnly, idempotent, etc. The description adds value by detailing the returned fields (venue, performers, datetime, ticket counts, prices), providing context beyond the annotations.

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

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

Single sentence, front-loaded with purpose, no redundant 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?

Given the simple one-parameter tool, the description covers typical return fields. Output schema exists, so return structure is documented. No major 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 0%, so description must compensate. It only restates that id is numeric, adding no new meaning about format, source, or constraints. Schema already shows 'type: number'.

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

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

The description clearly states the verb 'Fetch' and resource 'full details for a single SeatGeek event by numeric id', listing specific included fields. It distinguishes from sibling tools like 'events' (plural) and 'performer'.

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 detailed event info by ID, but does not explicitly state when to use versus alternatives like 'events' or 'performer', or provide any 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, openWorldHint=true, idempotentHint=true, destructiveHint=false, covering safety and idempotency. Description adds context about return contents (event listings with name, datetime, venue, performers, ticket URLs) and search capabilities, enhancing transparency 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?

Single sentence, no wasted words. Action verb 'Search' front-loads the purpose. All essential information (filters, return fields) is included 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 the tool's complexity (search with multiple filters), existence of output schema covers return values. Description covers input capabilities and output fields. Annotations handle behavioral aspects. 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?

Input schema has no defined properties (additionalProperties allowed), so schema coverage is effectively 100% with 0 parameters. Description compensates by listing acceptable filter parameters (keyword, venue, performer, location, date range, category), adding meaning beyond the empty schema. With 0 params, baseline is 4, and description meets that.

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 uses specific verb 'Search' and resource 'SeatGeek events', listing multiple filtering dimensions (keyword, venue, performer, location, date range, or category) and return fields (name, datetime, venue, performers, ticket URLs). It clearly distinguishes from sibling tools like 'event', 'venue', 'performer'.

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

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

Description implies usage context (searching events with various filters) but provides no explicit guidance on when to use this tool over alternatives like 'event', 'venue', or 'performer'. No exclusions or when-not-to-use conditions mentioned.

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

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

Annotations already declare destructiveHint=true. Description adds usage context but no additional behavioral traits beyond deletion, so adequate but not above baseline.

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 with no wasted words: first states the action, second provides usage guidelines and sibling context.

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 good annotations, the description fully covers purpose, usage, and sibling 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 100% with key description 'Memory key to delete'. Description does not add meaning beyond schema, so 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?

Clearly states 'Delete a previously stored memory by key' with specific verb and resource, and distinguishes from siblings by mentioning 'remember' and 'recall'.

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

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

Explicitly provides when to use: 'when context is stale, the task is done, or you want to clear sensitive data' and mentions paired tools 'remember and recall' as 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 non-destructive. The description adds behavioral detail: fetches the page, extracts title/description/key links, and emits standard markdown. This provides useful context beyond the annotations.

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

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

The description is concise and well-structured in three sentences: purpose, process, output/use cases. Every sentence adds value, and the key information is front-loaded.

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

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

Given the tool's simplicity (2 params, no output schema), the description adequately explains the output format and steps. It could mention error handling or limitations, but overall it is complete enough for the agent to understand usage.

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

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

Schema coverage is 100%, and the description does not add significant meaning beyond what the schema already provides. The description mentions 'with default 25, max 50' for max_links, but this is also in the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: generating a production-ready llms.txt file for a given URL. It specifies the action (generate), resource (llms.txt for any URL), and the output format. The description is specific and distinct from siblings like ai_visibility_check.

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 concrete use cases: getting a client's site indexed, drafting for a project, or auditing competitors. While it doesn't explicitly say when not to use or compare to alternatives, the scenarios are clear and helpful for 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 indicate readonly and nondestructive. Description adds that only active subscriptions are returned by default, and lists return fields.

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

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

Two sentences, front-loaded with action and output, 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 single-parameter read tool with annotations, description is 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 parameter description. Description does not add extra 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 'List the caller's active subscriptions' with specific verb and resource. It distinguishes from siblings like 'subscribe' 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?

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel' provides context for when to use vs 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 cover readOnly, idempotent, non-destructive. Description adds detail on what data is returned, which is useful behavioral context beyond annotations.

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

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

Single sentence, front-loaded with purpose, 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?

For a simple one-param fetch tool with rich annotations and output schema, the description is complete and 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?

Despite 0% schema coverage, the single parameter 'id' is explained as a numeric id, adding meaning beyond the schema type 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?

Clearly states fetching full SeatGeek performer profile by numeric id, listing included data fields (bio, images, taxonomies, stats, related performers), distinguishing from siblings like performer_by_slug.

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

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

Implicitly indicates when to use (by numeric id) but does not explicitly state when not to use or mention alternatives. Context of siblings provides some 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, and non-destructive nature. Description adds that it returns performer detail, supplementing the structured data 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?

Two concise sentences, front-loaded with key information, 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?

With one parameter, existing annotations, and an output schema, the description fully covers the tool's purpose and usage for a simple lookup.

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%, but the description explains the 'slug' parameter as a URL slug with an example (e.g., 'taylor-swift'), adding meaning beyond the schema's type-only 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?

Clearly specifies action (look up), resource (SeatGeek performer profile), method (by URL slug), and provides an example. Distinguishes from sibling 'performer' by noting it returns the same detail.

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

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

States when to use (when you have a slug) and references the performer tool as equivalent, implying an alternative. However, it does not explicitly exclude scenarios or provide when-not-to-use guidance.

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

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

Annotations already declare readOnly, openWorld, and idempotent. Description adds return format details (profiles with images, genres, event counts), which is useful context beyond annotations. No mention of pagination or limits, but annotations reduce burden.

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

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

Single sentence that is front-loaded and to the point. Every word earns its place; no redundancy or filler.

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

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

For a simple search tool with 0 required parameters and an output schema, the description covers purpose and return shape adequately. Could mention pagination (per_page) but is otherwise sufficient for the complexity level.

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

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

Schema has no declared parameters (0 params, 100% coverage), so baseline is 3. Description mentions 'by name or genre' which hints at query parameter usage, but does not explicitly name or describe the 'q' or 'per_page' parameters shown in the example. Adds some meaning but not fully compensating.

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 uses specific verb 'search' and resource 'performers' with clear scope (by name or genre). It lists examples of performer types (artists, teams, comedians) and return contents, distinguishing it from sibling tools like 'performer' and 'performer_by_slug' which are singular lookups.

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

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

No explicit guidance on when to use this tool vs alternatives. The sibling list includes 'performer' and 'performer_by_slug' for singular lookups, but description does not state that 'performers' is for broad search while others are for specific entities. Usage is implied but not clarified.

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 behavioral information beyond annotations: rate-limited to 5 per identifier per day, free (no quota impact), and that feedback is read daily and influences roadmap. 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 about 5 sentences, each earning its place. It is front-loaded with the primary purpose and immediately provides actionable 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?

Given 3 parameters and no output schema, the description covers all necessary context: purpose, usage guidelines, formatting, rate limits, and impact. It leaves no gaps for an agent to misuse the tool.

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

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

Schema coverage is 100%, but the description adds further meaning: for 'type' it clarifies each enum value; for 'message' it specifies formatting (1-2 sentences, max 2000 chars, be specific about which tool); and for 'context' it explains it's optional and relates to tool/pack/vertical.

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

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

The description clearly states the tool's purpose: sending feedback about broken, missing, or needed functionality. It lists specific use cases (bug, feature, data_gap, praise) and differentiates from siblings by focusing on feedback, which none of the sibling tools do.

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

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

The description explicitly tells when to use each feedback type (bug for wrong/stale data, feature for missing tools, praise for good experiences) and what not to do (don't paste end-user's prompt). It also mentions rate limits and that it's free/quota-free.

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 value beyond annotations by disclosing data source (CF analytics-engine), no PII, and caching window (5min-1h). Annotations already indicate read-only, idempotent, non-destructive, so description complements well.

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 main purpose, then use cases, then provenance. No redundant words, 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?

No output schema, but description clearly states what is returned (top tools, top packs, total call volume). For a simple read-only tool with one parameter, it is sufficiently complete. Could optionally mention response format but not required.

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 one parameter with enum and description (24h, 7d, 30d). Description adds context: shorter windows surface hot trends, longer show steady-state demand, providing meaning beyond schema alone.

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

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

Description clearly states the tool returns top tools, packs, and total call volume over recent windows, with specific verb 'returns' and resource 'Pipeworx trending data'. Distinguishes from siblings like discover_tools by focusing on call volume and trending.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming popular tool, checking alignment). Does not explicitly name alternative tools for non-trending needs, but context signals provide sibling names. Could be improved with 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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are consistent. The description adds significant behavioral context: details the partition_check, Jaccard similarity filter, placeholder filter, fill_check with realizable_edge_pp, and the fact that it analyzes rather than mutates data. No contradictions.

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

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

The description is detailed but front-loaded with the critical requirement. It is well-structured with sections for each mode. However, it could be slightly more concise; some sentences are dense but contain necessary information.

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

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

Given the tool's complexity (two modes, multiple checks, fill risk integration), the description covers all key aspects: response structure (opportunities[], partition_check), filler checks, and reference to sibling tool polymarket_fill_risk for custom sizing. No output schema, but the response structure is described.

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 main description adds substantial meaning beyond schema: explains the semantics of event (single-event mode with slug examples) and topic (cross-event mode with seed question examples), and clarifies mutual exclusivity.

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 arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) with specific examples, and differentiates from sibling tools like polymarket_edges by focusing on arbitrage detection.

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 the requirement: 'REQUIRES one of `event` or `topic` — call with no args fails.' Provides clear guidance on when to use each mode, including recommendations ('event recommended for a specific market') and context for topic mode. Also explains when not to trade via fill_check.

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 goes far beyond the annotations (readOnlyHint, etc.) by detailing the three model families, how Kelly sizing works, the 24h-move warning, per-sport bias corrections, placeholder-slug filters, and caching behavior. This provides rich behavioral context that annotations alone do not cover.

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

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

The description is overly verbose with detailed model explanations that could reside in documentation. While front-loaded with purpose, the bulk of the text is dense and technical, making it less concise than ideal for quick agent scanning.

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

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

Given the tool's complexity (9 parameters, no output schema), the description covers output structure (by_segment, diagnostics), caching, edge cases (Fed bets), and explains why segments might be empty via _diagnostics. It is thoroughly complete for agent use.

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

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

Schema coverage is 100% with detailed descriptions. The description adds value by explaining parameters as 'tradeable-edge filters' with concrete examples (e.g., 'Set to 5000 to drop thin-book opportunities'). It integrates parameter usage into the tool's narrative, though not dramatically beyond the schema.

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

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

The description clearly states it 'scan[s] top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' The specific verb 'scan' and resource 'Polymarket markets' are present, and the tool is distinguished from siblings like polymarket_arbitrage and polymarket_kalshi_spread by focusing on predictive edge from Pipeworx models.

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

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

The description explicitly says 'Built for "what should I bet on today"' and explains how knobs like min_liquidity and max_spread_pp filter out non-tradeable opportunities. It also notes Fed bets are excluded from ranking but surfaced separately. However, it does not explicitly state when not to use this tool versus alternatives like polymarket_arbitrage.

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

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds significant behavioral context: snapshots are written on cache-miss (so gaps mean no scan that day), history depth bounded by 60-day TTL, and decay computed from daily closes not intraday. 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?

Description is well-structured: begins with a one-sentence summary, then param explanations, then detailed response format, then limitations. It is longer than necessary but every sentence adds value. Could be slightly more concise without losing meaning.

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

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

Despite no output schema, the description exhaustively documents the response: tracked[] with time-series, trend, decay; expired[] with lifespan; snapshot_dates[]; and limits like TTL and intraday caveat. For a telemetry tool with 2 optional params, this covers all necessary context for an agent to use it correctly.

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

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

Schema coverage is 100% but description adds meaning: it explains that 'days' controls lookback (default 14, max 30 consistent with schema clamp 2-30) and 'window' selects snapshot family (default 1wk). More importantly, the description details the full response structure, compensating for lack of output 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 opens with a specific verb-resource pair: 'Edge persistence and decay telemetry'. It immediately distinguishes from sibling tools like polymarket_edges (which provides snapshots) by focusing on temporal persistence and decay across snapshots. The example question clarifies the unique value proposition.

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

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

The description provides a concrete use case example ('a fresh wide edge and a 3-week-old wide edge are different trades') and explains what the tool answers. It does not explicitly state when not to use or list alternative tools, but context signals and sibling names imply that other polymarket tools exist. Slight deduction for lack of explicit 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 indicate read-only, idempotent, not destructive. Description adds detailed behavioral info: walks the ladder, returns specific fields, explains it does not execute, and warns about thin books and partial fills, aligning 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?

Front-loaded summary, each sentence adds value for a complex two-mode tool. Length is justified by necessary detail; 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 complex tool with no output schema, description fully explains all return fields (top_of_book, vwap, slippage, etc.), covers edge cases (thin legs, forced directional risk), and provides complete usage guidance.

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

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

Schema has 100% coverage with descriptions. Description adds significant meaning: explains two modes, default values, size_usd interpretation for basket, and side auto-detection for basket, going well 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 it is an edge check against live order-book depth, with specific verbs ('check', 'walk the ladder') and distinguishes from siblings like polymarket_arbitrage and polymarket_edges by explicitly stating when to use 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?

Explicitly says to use before acting on arb signals or trades above $500, covers both single-market and basket modes, and warns about partial basket fills converting arb to unhedged directional position, with alternatives named.

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 extensive behavioral context beyond annotations: it explains the compatibility_warning conditions (non-equivalent bet shapes, semantically unrelated events), temporal alignment checks, and skipped counters. It discloses that real spreads are rare despite the shortcut list, managing user expectations 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 long but well-structured: it opens with the core purpose, then logically moves through modes, response details, and safety explanations. Every sentence adds value, though it could be slightly more 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?

Without an output schema, the description fully explains the response structure: leg-by-leg prices, matched spreads, and safety fields (compatibility_warning, temporal_alignment, skipped counters). It also covers edge cases, making the tool's behavior predictable.

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

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

The schema has 100% coverage with parameter descriptions, but the description adds value by explaining the override logic (explicit params override topic) and providing concrete examples for each field. The additional context elevates it above the baseline of 3.

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

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

The description clearly states the tool's purpose as computing cross-venue spread between Kalshi and Polymarket for equivalent outcomes. It distinguishes itself from siblings like polymarket_arbitrage by focusing on dual-venue comparisons and provides specific modes (topic shortcuts and explicit pairings).

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

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

The description explicitly explains when to use the tool (to compare prices across venues for the same resolving question) and when not to rely on it (if compatibility_warning fires). It details two usage modes and warns that most pre-mapped topics are not yet tradeable, providing clear guidance on expectations.

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

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

Annotations already mark it read-only, idempotent, non-destructive. Description adds scoping to identifier and lifecycle pairing. No contradictions. Could mention behavior on missing keys but not necessary.

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 that front-load purpose, provide usage examples, and reference siblings. Every sentence adds value, no fluff.

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

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

For a simple tool with one optional parameter and no output schema, the description covers purpose, usage, examples, scoping, and lifecycle pairing. Complete given the complexity.

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

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

Schema covers the parameter with description. Tool description adds real-world examples (ticker, address, notes) and clarifies scoping, providing extra 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 value by key or lists all keys when omitted. It uses specific verb 'retrieve' and resource 'saved values', and distinguishes from sibling tools 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 hints at alternatives ('without re-deriving from scratch'). Mentions pairing with remember/forget for lifecycle. Could include explicit 'when not to use' but still clear.

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

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

The description discloses behavioral traits such as mark_read side effect, but it contradicts the idempotentHint annotation. The annotation indicates idempotency, yet the description states that mark_read changes state, making the tool non-idempotent. This is a clear contradiction, resulting in a score of 1.

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-organized and includes necessary details without being overly verbose. It front-loads the main purpose and then provides filtering and behavioral details in a logical order.

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 the description covers return fields, filtering, and an alternative access method, the contradiction regarding idempotency introduces confusion. Without an output schema, the description is fairly complete, but the inconsistency reduces its reliability.

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 have schema descriptions (100% coverage). The description adds extra context for 'type', 'since', and 'mark_read' with examples and usage semantics, enhancing understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Pull fired events from your subscription feed.' It specifies the resource (alerts/events), action (pull), and key filtering options, distinguishing it from sibling tools like 'event' and 'events' which likely operate differently.

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 context for when to use this tool, mentioning that polling works fine and offering an alternative REST endpoint for scripts. However, it does not explicitly state when not to use it or compare it to sibling tools.

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

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

Annotations already declare read-only, idempotent, non-destructive. The description adds rich behavioral detail: fans out to multiple sources (SEC, GDELT→GNews fallback, USPTO), mentions API sunset and soft-fail behavior, and describes the return structure (changes[] grouped by source, total_changes, citation URIs). No contradictions.

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

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

The description is dense but well-organized. It front-loads the purpose with several example queries, then concisely lists data sources, parameter details, and return structure. Every sentence earns its place, though it could be slightly trimmed without losing clarity.

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

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

Given the tool's complexity (multiple data sources, fallback logic, output shape), the description is remarkably complete. It explains when to use it vs. sibling, how all parameters work, what sources are consulted, and the structure of the response. No output schema exists, but the description covers the key return fields.

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

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

Schema coverage is 100%, but the description adds significant value: it explains that 'since' accepts ISO dates or relative shorthand ('7d', '30d', '3m', '1y') and that 'value' can be a ticker or zero-padded CIK. Examples like 'AAPL' and '0000320193' clarify usage 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 opens with concrete example queries ('What's new with X', 'latest on Y', etc.) and states it provides a 'change feed for a company'. It clearly distinguishes itself from 'entity_profile' by specifying when to use the alternative.

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

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

The description explicitly says to use this tool for recent changes and suggests 'entity_profile' for static profiles. It provides example queries and parameter guidance ('Use '30d' or '1m' for typical monitoring'). It doesn't explicitly list when not to use it, but the sibling differentiation is clear.

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

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

Annotations already provide read-only, idempotent, and non-destructive hints. The description adds that results are ranked and personalized, which are behavioral traits beyond what annotations convey. It does not disclose any potential side effects or limitations, but given the annotation coverage, this is 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 a single 15-word sentence that front-loads the main purpose and action. Every word is necessary, and there is no verbosity or redundancy. It is efficiently structured for quick parsing.

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

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

Given the low tool complexity, rich annotations, and presence of an output schema, the description is complete. It covers the core purpose, optional filters, and the nature of the output (ranked events). No additional information is needed for an agent to understand when and how to use 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 is open (no defined properties, additionalProperties true). The description adds meaningful parameter semantics by listing optional filters (location, datetime, genres), which are not specified in the schema. However, it misses the 'per_page' parameter shown in the schema example, and the list is not exhaustive. Still, it significantly compensates for the schema's lack of structure.

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: getting personalized, ranked event recommendations from SeatGeek with optional filters. It uses a specific verb ('Get') and resource ('personalized SeatGeek event recommendations'), and the emphasis on personalization and ranking distinguishes it from sibling tools like 'events' which likely provides general listings.

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 (when personalized recommendations are desired) but does not explicitly state when not to use it or mention alternative tools like 'events' or 'performers' for different use cases. It is adequate but lacks explicit 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 provide idempotentHint=true and destructiveHint=false. Description adds scoping by identifier and expiration details (24h for anonymous, persistent for authenticated). This supplements annotations with useful behavioral context without contradiction.

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

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

Four-sentence description is concise, front-loaded with purpose, and every sentence adds value. 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?

Given the tool's simplicity (2 params, no output schema, annotations present), the description covers all necessary aspects: purpose, usage, behavioral details, and related tools. 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 coverage is 100% with good descriptions for both parameters. The description provides additional examples but does not add significant new semantics beyond schema. Baseline of 3 is appropriate.

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

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

Description clearly states the tool's purpose: saving data for reuse across conversations/sessions. It uses specific verbs ('save', 'store') and resource ('data'), and 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 states when to use (discover something worth carrying forward) with concrete examples (ticker, address, preference). Mentions pairing with recall and forget, and explains persistence differences between authenticated and anonymous users.

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, openWorldHint, idempotentHint, and non-destructive. The description adds behavioral context by noting that each call cascades through several lookup endpoints, which is valuable for understanding potential latency or multiple API calls. No contradictions.

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

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

The description is concise yet comprehensive, front-loaded with a clear single-line purpose followed by specifics. Every sentence adds value: examples, usage guidance, and type details. No redundant or vague language.

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 fully explains return values for both entity types. It covers input formats, output structure, and usage context. The description is complete for an identifier resolution tool.

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

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

Schema coverage is 100%, but the description significantly adds semantic detail: for company, it specifies returns (ticker, 10-digit CIK, company_name, citation URI); for drug, returns (RxCUI, ingredient, brand, citation). It also clarifies accepted input formats for 'value' (e.g., ticker, CIK, name). This goes well beyond the schema's minimal descriptions.

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

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

The description clearly states the tool's purpose: resolving a user-spoken name to a canonical identifier. It provides concrete examples (ticker, CIK, RxCUI) and distinguishes between company and drug types. The verb 'resolve' accurately describes the action.

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.' It contrasts with manual lookups by noting it replaces 2-3 steps. The description clearly indicates when to use this tool and supports both entity types.

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. Description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. No contradiction.

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

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

Two concise sentences with no wasted words. Front-loaded with core functionality, then usage example, then output format.

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

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

Describes return format (ranked list with score, confidence, signal density). Mentions entity count constraint (2-8). Lacks details on error handling or rate limits, but adequate given schema coverage and annotations.

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 value by explaining that 'entities' first entry is the subject for narrative, and 'context' disambiguates common names. Also clarifies purpose of _apiKey for anthropic models.

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

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

Description clearly states it compares AI visibility across multiple entities, ranks them, and surfaces most/least recognized. It distinguishes from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (general comparison) by specifying it probes with ai_visibility_check and provides a ranked comparison.

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

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

Explicitly states use case: 'competitive AI-marketing audits'. Provides an example question. However, it does not explicitly say when NOT to use or mention alternative tools like ai_visibility_check for single-entity checks.

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, open-world, non-destructive. The description adds behavioral details: partial failures degrade gracefully, bundlephobia first measurement delay 5-30s, and sources_failed listing. 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 that front-loads the core purpose. Every sentence adds value, but it could be broken into multiple lines for readability. Minor structural improvement possible.

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 exists, so description fully describes return values: summary block, advisory details, links, alternative versions, and partial failure behavior. Also covers ecosystem limitations. Comprehensive for a composite tool.

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

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

Schema coverage is 100% with 'package' and 'version' described. The description mentions 'version' implicitly but adds little beyond schema. Baseline 3 is appropriate as schema does the heavy lifting.

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

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

The description clearly states it's a composite check for npm packages combining deps.dev and bundlephobia. It lists specific return fields and distinguishes scope (NPM ecosystem only in v1, with others covered by a different tool). This provides a specific verb+resource with clear differentiation 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 says 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. Also mentions ecosystem limitations and alternative for other ecosystems, giving clear when-to-use and when-not.

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 significant behavioral details beyond annotations: embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K with truncation flag), return format (passages with offsets and scores). Annotations already indicate read-only, idempotent, etc., so description complements well.

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: purpose, usage, technical details. Each sentence adds information. Slightly long but all content is relevant.

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

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

Given no output schema, the description explains what is returned: top-N passages with character offsets and similarity scores. Also mentions truncation behavior. Complete for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by providing example queries for the 'query' parameter and stating default value for 'limit' (5). This enriches understanding beyond schema.

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

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

The description clearly states it performs semantic search inside a fetched record, using specific verb 'search' and resource 'record'. It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and contrasting with using whole documents.

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

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

Explicitly states when to use: when the record is too big to cram into the prompt. It also suggests a complementary tool. Does not explicitly list when not to use, but the context implies small records may not need it.

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

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

The description reveals key behaviors: requires Pipeworx OAuth account (anonymous/BYO cannot persist), per-type constraints (e.g., sponsor or condition required for clinical_trial), delivery caps (10 SMS/day), and webhook auto-disable after 10 failures. This adds significant context beyond the annotations, though it does not address the idempotentHint=true annotation (which may be misleading).

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

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

The description is dense but well-structured, starting with the primary purpose, then detailing types, then delivery. It packs a lot of information without being overly verbose. Slight room for improvement by breaking into more digestible paragraphs, but overall efficient.

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

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

Given the tool's complexity (multiple subscription types, delivery channels, constraints), the description covers authentication, type-specific params, delivery options, and limits. However, it lacks full return value specification (only mentions 'subscription id') and does not describe error conditions. With no output schema, more return detail would be beneficial.

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

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

With 100% schema coverage, the baseline is 3. The description adds meaning by providing concrete examples for each type in 'params' (e.g., sec_8k with items, polymarket_edge with topic, etc.) and explaining delivery channel behaviors (email templates, SMS verification, webhook signing). This extra detail justifies a 4.

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

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

The description clearly states the action ('Create a proactive monitoring subscription to a live-data event stream') and the return value ('Returns the new subscription id'). It also enumerates supported types and delivery channels, making the tool's function highly specific and distinguishable.

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 detailed usage context (types, params, delivery) but does not explicitly advise when to use this tool over alternatives like 'events' or 'recent_alerts'. It assumes the agent can infer from context; no direct 'when-to-use' or 'when-not-to-use' guidance 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 declare readOnly, idempotent, openWorld. The description adds that it returns live catalog examples with tool shapes, which is useful context but not critical 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 front-loaded with common phrasings, then states purpose, output, and usage. 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 tool with no output schema, the description fully explains return content (category-bucketed examples with tool shapes) and covers the sole parameter, making it complete for its onboarding role.

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 listing possible values. The description adds examples ('finance', 'pharma') and explains behavior when omitted, adding significant 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?

The description clearly states the tool is an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It uses specific verbs and distinguishes itself from siblings by explicitly stating 'Use this 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 tells when to use: 'when you do not yet know what Pipeworx can do', and how to call it (no args for full spread, topic param to focus). It implies alternatives like ask_pipeworx for actual queries.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by specifying that the output includes IDs and hierarchy, and no contradictions are present.

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 front-loaded with the main action and includes key details (IDs, hierarchy) without extraneous 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, presence of an output schema, and comprehensive annotations, the description fully informs the agent about the tool's purpose and output.

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

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

With no parameters, the description correctly implies no input is needed, and the schema coverage is 100%. The baseline of 4 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 explicitly states the verb 'list' and the resource 'all SeatGeek event taxonomies', including details like IDs and hierarchy, which distinguishes it from sibling tools like 'events' or 'performers'.

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

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

The description mentions the tool is 'useful for filtering events by category', providing clear context for when to use it. However, it does not explicitly state when not to use it or suggest alternatives.

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

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

Adds behavioral details beyond annotations: the row is deactivated, not deleted, preserving history. No contradiction with annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false).

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, each adding essential information: purpose+constraint and behavioral effect. 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 simple tool with one parameter and no output schema, description fully covers purpose, ownership, and side effects. Complete given complexity.

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

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

Schema already covers the single parameter 'id' with a clear description. Description adds no new semantic value beyond 'by id', 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?

Description clearly states action (cancel) and resource (subscription by id). Distinguishes from siblings like 'subscribe' and 'list_subscriptions'.

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 mentions ownership enforcement (only cancel own subscriptions), providing clear context. No exclusions or alternative tools named, but sibling list allows inference.

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

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds valuable behavioral details: the data source (SEC EDGAR + XBRL), the return format (verdict types, structured form, actual value with citation, percent delta), and that it replaces multiple sequential calls. 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, fitting in a single paragraph without redundancy. It front-loads the core purpose with natural-language triggers, then adds usage context and technical details. Every sentence adds value, though a more structured format could improve scannability.

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

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

Given the tool's simplicity (one parameter, no output schema), the description provides sufficient context: input format, data source, return types, and use case. It fills the gap created by the lack of an output schema. It is complete enough for an agent to understand what the tool does and what to expect.

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

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

The schema already describes the single 'claim' parameter with 100% coverage, including an example. The description adds little additional meaning beyond reiterating the example usage and the input format. Baseline 3 is appropriate as the schema does the necessary work.

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: verifying natural-language factual claims against authoritative sources, with specific examples of input phrases. It also distinguishes itself by noting it replaces multiple sequential calls, and the domain is explicitly limited to company-financial claims.

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

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

The description provides a direct when-to-use instruction: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also scopes the tool to company-financial claims via SEC EDGAR + XBRL, implying when not to use. However, it does not explicitly list alternative tools for other claim types.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds valuable context on returned fields.

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

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

Single sentence, 16 words, front-loaded with action and resource. No wasted words.

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

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

Given output schema exists and annotations are rich, description lists key fields sufficiently. Minor gaps: no error handling or not-found behavior.

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

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

Only one parameter 'id'; description clarifies it's numeric and used for lookups, adding meaning beyond schema schema coverage of 0%.

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 uses specific verb 'fetch' and resource 'full SeatGeek venue record', listing key fields. Clearly distinguishes from sibling 'venues' which is plural.

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?

Implies use when numeric id is known, but no explicit when-to-use/when-not-to-use or mention of alternatives like 'venues'.

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 a safe read operation (readOnlyHint, idempotentHint, destructiveHint false). The description adds value by specifying the return fields (address, capacity, geolocation), which is helpful for understanding the output beyond the schema.

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

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

The description is a single, well-structured sentence that front-loads the action and resource. It is concise without unnecessary words, earning its place.

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

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

Given the output schema exists (as per context signals), the description explains the return fields. For a search tool with many siblings, it would benefit from mentioning when to use it versus the singular 'venue' tool, but overall it is sufficiently 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?

The input schema has no defined properties and allows additional properties. The description compensates by listing the expected keys (name, city, etc.), providing meaning that the schema lacks. With schema coverage effectively 100% (due to additionalProperties), the description's explanation adds significant 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?

The description clearly states the tool searches SeatGeek venues by various criteria and returns details. It uses a specific verb 'search' and resource 'venues'. However, it does not differentiate from the sibling 'venue' tool, which likely retrieves a single venue.

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 searchable fields (name, city, etc.) but provides no guidance on when to use this tool versus alternatives like 'venue' or 'events'. There is no mention of exclusions or when not to use it.

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