Youtube

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

The description discloses that the tool 'picks the right tool' and 'fills the arguments,' implying automated action, which is beyond the minimal schema. Since no annotations are provided, the description carries the burden and does a decent job but does not detail potential side effects, rate limits, or data sources used.

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

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

The description is concise (3 sentences) and front-loaded with the main purpose. Examples add clarity without being verbose. Slight redundancy in 'no need to browse tools or learn schemas' could be trimmed, but overall efficient.

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

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

Given the tool's simplicity (1 parameter, no output schema, no nested objects), the description adequately covers what the agent needs: how to ask questions and what to expect. It does not explain return format, but that is acceptable without an output schema.

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

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

With 100% schema coverage and only one parameter described in the schema as 'Your question or request in natural language,' the description adds value by explaining how to use it (plain English, no need to browse tools) and giving concrete examples, which enriches the parameter semantics.

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

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

The description clearly states that this tool answers plain English questions by selecting the best data source, filling arguments, and returning results. It distinguishes itself from sibling tools that are specific to YouTube or memory operations by focusing on generic query resolution.

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 'no need to browse tools or learn schemas' and provides example questions, guiding the user on when to use this tool. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools list provides implicit context.

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

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

Annotations already declare readOnlyHint, openWorldHint, destructiveHint. Description adds: it resolves markets, classifies bets, fans out to packs, returns evidence packet and comparison. No contradictions; adds 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?

Three sentences, front-loaded with purpose and input format, then process, then usage. No wasted words; each sentence adds value.

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

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

Despite no output schema, description explains the return: evidence packet plus market-vs-model comparison. Covers internal steps, depth options, and use context. Sufficient for an agent to use correctly.

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

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

Schema coverage is 100% with good property descriptions. Description adds that market accepts slug, URL, or question text, going beyond schema. Depth description in schema is already clear; description reinforces but doesn't add new semantics.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data, and specifies input formats. It distinguishes itself from siblings like ask_pipeworx by being specialized for betting context.

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 cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. Notes it's the core demo product and agents using it convert better, implying preference over alternatives. No explicit when-not-to-use, but clear context.

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

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

No annotations are provided, so the description carries the full burden. It discloses data sources (SEC EDGAR, FDA, etc.) and return types (paired data, URIs), but does not mention safety traits like readonly or rate limits. The behavioral transparency is adequate but not comprehensive.

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

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

Three focused sentences with no fluff. The description front-loads the core action, then details each entity type, and ends with a efficiency statement. Every sentence earns its place.

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

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

Given the detailed input schema and lack of output schema, the description does a good job explaining the data returned (revenue, adverse events, etc.) and the use case. However, 'paired data' is vague, and error handling is not addressed, but overall it is sufficiently complete for an agent to decide.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the specific data fields returned for each type and providing examples for the values parameter (tickers/CIKs vs drug names). This goes beyond the schema alone.

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

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

The description clearly states the tool compares 2–5 entities side by side, with specific data points for company and drug types. It distinguishes itself by replacing multiple sequential calls, and the purpose is unambiguous.

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

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

The description implies usage for comparing entities when efficiency matters, but does not explicitly state when not to use it or provide alternatives. However, since no sibling tool performs comparisons, this is acceptable.

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 it searches by natural language description and returns tool names and descriptions. It also implies ranking by relevance. Since annotations are absent, the description carries full burden, but it lacks details on whether results are sorted, paginated, or whether the tool has any side effects. However, for a read-only search tool, these are minor gaps.

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

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

Three sentences, each carrying important information: purpose, output, and usage guidance. No wasted words. Front-loaded with the main action.

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

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

Given that the tool has no output schema and is a simple search tool, the description is complete enough. It explains what it does, when to use it, and what it returns. No gaps for its intended use case.

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

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

Input schema has 100% coverage, so baseline is 3. The description does not add extra meaning beyond what the schema provides, but it does give an example for the 'query' parameter ('e.g., "analyze housing market trends"'), which is helpful but not required given the schema's description already provides a similar example.

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 a tool catalog by natural language query and returns the most relevant tools. It uses specific verbs ('Search', 'Returns') and distinguishes itself as a discovery tool to be called first when many tools are available.

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

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

Explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task', providing clear when-to-use guidance. This contrasts with sibling tools which are likely task-specific, implying the tool should be used for selection rather than execution.

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

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

With no annotations, the description carries the burden. It discloses that it bundles multiple sources, returns citation URIs, and mentions performance trade-off for federal contracts. Lacks explicit side-effect or auth details but adds useful behavioral 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 well-structured with a clear headline, specifics, and guidelines. Each sentence serves a purpose, though slightly longer than minimal.

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 (bundling many sources) and simple schema, the description covers what data is included, format of returns, and relationships to other tools. No output schema, but description provides sufficient context for an agent.

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

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

Adds meaning beyond the schema: type description notes future expansion, value description explicitly says names not supported and directs to resolve_entity. This compensates for any lack of schema detail.

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

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

The description clearly states it returns a full profile of an entity across multiple Pipeworx packs, listing specific data types for companies. It differentiates from siblings by mentioning federal contracts should use a different tool and names should use resolve_entity.

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

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

Explicitly tells when to use (for full entity profile) and when not (federal contracts -> usa_recipient_profile; names -> resolve_entity). Provides context on replacing 10-15 sequential calls.

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

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

No annotations are provided, so description carries full burden. It states the action is destructive (delete) but does not disclose irreversibility, permissions, or effects on related data. A 2 is appropriate as it only conveys the basic destructive nature.

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

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

One sentence, 7 words, zero waste. Every word is necessary and informative.

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 (1 param, no output schema, no nested objects), the description is adequate but minimal. It covers the basic purpose but lacks behavioral details like confirmation, reversibility, or error states that would aid an agent.

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

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

Schema coverage is 100% and the single parameter is described in schema as 'Memory key to delete'. Description adds 'by key', confirming usage, but does not add new semantic information beyond the schema. Baseline 3 is correct.

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 'Delete a stored memory by key', which is a specific verb (Delete) and resource (stored memory), and uniquely distinguishes from siblings like 'recall' (retrieve) and 'remember' (store).

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 when needing to delete a memory, but provides no guidance on when not to use it or alternatives. For a destructive action, more context would be helpful.

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 rate limiting (5 per identifier per day) and specifies that it's free. No annotations are provided, so the description carries the full burden. It could mention idempotency or side effects, but for a feedback tool this is adequate.

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

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

The description is three sentences, front-loaded with the main action, and every sentence provides essential information without redundancy.

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

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

For a simple feedback tool with 3 parameters and full schema coverage, the description is complete. It covers usage, constraints, and rate limits. No output schema needed.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by elaborating on message content (be specific, 1-2 sentences, 2000 char max) and providing context for the type parameter with clear examples.

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

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

The description clearly states the tool is for sending feedback (bug reports, feature requests, missing data, or praise). It distinguishes itself from sibling tools like ask_pipeworx or discover_tools by specifying its unique purpose.

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

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

The description explicitly lists use cases and provides guidance on what to include (describe what you tried, not user prompt). It mentions rate limits. However, it does not state when not to use it or suggest alternatives, though 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 indicate read-only and non-destructive behavior; the description adds context on how markets are checked and grouped. No contradictions. It explains that the tool searches, groups, and checks monotonicity, which is 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 concise and well-structured, using bullet points for modes and examples. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description explains what the tool returns: 'ranked opportunities with suggested trade direction + reasoning'. Inputs are fully covered. The description is complete given 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 the schema already describes parameters. The description adds value by explaining their purpose in detail (e.g., 'pass a single Polymarket event slug' for event, cross-event search for topic), making it clearer than the schema alone.

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

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

The description clearly states that the tool finds arbitrage opportunities on Polymarket by checking monotonicity violations. It distinguishes two modes (event and topic), making its purpose specific and distinct from sibling tools.

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

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

The description explains when to use each mode, including an example of cross-event mode catching cases single-event misses. It does not explicitly say when not to use it, but the guidance is clear enough.

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

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

Describes algorithm details (lognormal model from FRED + coinpaprika, grouping by asset, computing model probability, ranking by |edge|) beyond annotations. No contradictions with readOnlyHint, openWorldHint, destructiveHint.

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

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

Concise yet informative single paragraph that front-loads the core action and then explains method. 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 optional parameters and no output schema, the description fully explains inputs, algorithm, data sources, and return format (top N edges with direction). Complete for 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 coverage is 100% with adequate descriptions for all three parameters. The tool description does not add extra meaning beyond what's in the schema, so 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?

Clearly states it scans Polymarket markets, finds where Pipeworx data disagrees with market price, and returns top edges with trade direction. Distinguishes from sibling tools like polymarket_arbitrage.

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

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

Explicitly targets the 'what should I bet on today' question and mentions avoiding manual paging. Could be stronger on when not to use or alternatives, but it's clear enough.

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

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

Describes the tool's behavior: retrieving a single memory by key or listing all when key is omitted. No annotations provided, so the description carries the full burden. It could mention persistence (session vs cross-session) but does well overall.

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

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

Two sentences, no fluff. Front-loaded with action and resource, second sentence clarifies use 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?

Given no output schema, description hints at return value (the stored memory or list of keys) but doesn't detail format. For a simple retrieval tool, this is 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% and description adds context: omitting key lists all. However, the description doesn't elaborate on the format of the key or what happens if key doesn't exist, but the schema already describes it as a string.

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

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

Clearly states the verb 'Retrieve' and the resource 'stored memory by key', and distinguishes between retrieving a specific key and listing all memories. The description is precise and leaves no ambiguity.

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

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

Explicitly says when to use the tool: 'to retrieve context you saved earlier' and mentions that omitting key lists all memories. However, it does not explicitly contrast with siblings like 'remember' or 'forget', though the use case is clear.

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

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

With no annotations, the description discloses fan-out behavior, parallel execution, parameter formats, and return structure (structured changes, count, URIs). It does not mention rate limits or side effects, but transparency is adequate.

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

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

The description is compact yet informative, front-loading the purpose, then detailing sources, parameter formats, and return values. Every sentence adds essential information without redundancy.

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

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

Given the tool's moderate complexity and no output schema, the description covers the main behavior, return structure, and parameter usage. Missing edge cases, but overall sufficient for an agent to use effectively.

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

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

Schema covers all parameters (100%), and the description adds value by providing examples for 'since' (e.g., '30d', '1y') and a default recommendation ('30d' or '1m'). This is beyond the schema, thus 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 tool retrieves what's new about an entity since a given time, specifying entity type (company), multiple data sources, and output structure. It distinguishes from sibling tools like 'entity_profile' by focusing on changes over time.

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 suggests use cases ('brief me on what happened with X' or change-monitoring workflows). While it doesn't state when not to use it or alternatives, the context is clear enough for an agent to decide.

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

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

No annotations provided, so description carries full burden. It discloses memory duration (24 hours for anonymous, persistent for authenticated) which is key behavioral context beyond the basic store operation.

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

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

Three sentences, each earning its place: what it does, when to use, and behavioral nuance. 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 key-value store with no output schema and good schema coverage, description is complete. Could mention overwrite behavior or key naming conventions, but not essential.

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 does not add parameter-level detail beyond schema examples, but the schema already provides good descriptions and examples.

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

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

Description clearly states the verb (store), resource (key-value pair), and context (session memory). It distinguishes from siblings like recall and forget by specifying the write operation.

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 gives concrete examples of when to use (save findings, preferences, context) and notes persistence differences for authenticated vs anonymous users. However, no explicit exclusion or comparison to siblings.

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

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

No annotations exist, so description fully bears behavioral burden. It discloses accepted inputs (ticker, CIK, name), return fields, and that it replaces multiple calls, though missing details like error handling or rate limits.

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

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

Two sentences with no redundancy. Purpose stated first, followed by specific details. Front-loaded and 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?

Despite no output schema, description lists return fields (ticker, CIK, name, URIs) and the benefit over alternatives. Fully adequate for the tool's complexity.

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

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

Schema covers both parameters with descriptions, but description adds value by providing concrete examples and clarifying the value field's variants, exceeding the baseline for high schema coverage.

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

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

Description uses specific verb 'resolve' and resource 'entity to canonical IDs', clearly differentiating from sibling tools (all unrelated to entity resolution). Replaces 2-3 lookup calls, making the purpose concrete.

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 explains when to use (single call for entity resolution) and provides type-specific examples. No explicit exclusions or alternatives needed as siblings are unrelated.

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

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

No annotations are provided, so the description must carry the transparency burden. It describes return values (verdict, structured form, actual value with citation, percent delta) but does not disclose potential side effects, permissions, rate limits, or data freshness. For a read-only tool, this is adequate but not fully transparent.

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

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

The description is three sentences, each serving a distinct purpose: stating the action, specifying scope/sources, and listing outputs/benefits. No redundant information. Front-loaded with the core purpose.

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

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

Given no output schema, the description adequately explains the return information (verdict types, structured form, citation, delta). It also clarifies the tool's role as a replacement for multiple calls. Could be improved by noting error handling or unsupported claims, but overall sufficient 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% (one required parameter 'claim' with description). The description adds value with concrete examples (e.g., Apple's revenue, Microsoft profit), clarifying the expected format and scope beyond the schema's generic description.

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

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

The description explicitly states the tool fact-checks natural-language claims against authoritative sources, specifying the domain (company-financial for US public companies) and sources (SEC EDGAR + XBRL). It lists verdict types, clearly distinguishing it from sibling tools that are unrelated (e.g., YouTube tools, entity_profile).

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

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

The description indicates the tool is for financial fact-checking and mentions it replaces 4-6 sequential agent calls, implying efficiency. However, it does not explicitly state when not to use it or suggest alternatives for non-financial claims. The context of siblings provides some contrast, but explicit guidance is missing.

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

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

With no annotations provided, the description carries the full burden. It states that the tool retrieves channel information and statistics, which is a read operation. However, it does not disclose any limitations, such as API rate limits, quota costs, or that only public channel data is returned.

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 concise and front-loaded with the purpose. It lists specific data points, which is helpful. No extra 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?

The tool is relatively simple (no output schema, 3 parameters). The description covers the main purpose and return data, but could mention that at least one of username or channel_id is needed for the query.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond what the schema already provides; it only lists data returned but not parameter-specific semantics.

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

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

The description clearly states the tool retrieves YouTube channel information and statistics, listing specific data points like subscriber count, video count, view count, description, and custom URL. It uses a specific verb ('Get') and resource ('YouTube channel information'), and distinguishes from sibling tools like 'yt_channel_videos' and 'yt_video_details'.

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 fetching channel details but does not explicitly state when to use this tool versus alternatives like 'yt_channel_videos' or 'yt_search'. No exclusions or prerequisites are mentioned, though the required _apiKey parameter is a prerequisite.

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

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

With no annotations provided, the description carries the full burden. It discloses the ordering (by date) and return fields, which is good, but it does not mention that results are paginated (max 50), that an API key is required (already in schema), or any rate limits or costs. The behavior is partially transparent but not fully.

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 plus a list of returned fields. Every word adds value with no redundancy. It is concise and front-loaded with the core action.

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 5 parameters, no output schema, and no annotations, the description is adequate but not complete. It explains the returned fields but does not cover pagination behavior, error cases, or the meaning of the returned description field (which may be truncated). It also doesn't clarify that the API key parameter is required (already in schema).

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

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

Schema coverage is 100%, so each parameter already has a description. The tool description adds no extra meaning beyond what the schema provides (e.g., no mention that published_after/before are optional filters or that max_results defaults to 10). 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 lists recent videos from a YouTube channel ordered by date and specifies the returned fields (ID, title, description, publish date). It distinguishes itself from siblings like yt_search (which likely searches across all of YouTube) and yt_channel_details (which gets channel metadata, not videos).

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

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

The description implicitly indicates when to use this tool (to get a list of recent videos from a specific channel) but does not explicitly mention when not to use it or name alternative tools like yt_search for broader queries. Given the context of sibling tools, the usage context is reasonably clear but lacks exclusions.

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

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

No annotations are provided, so the description carries the full burden. It mentions that the tool returns 'snippet info' and lists what's included (title, description, channel, thumbnails, publish date), but does not disclose potential rate limits, authentication requirements beyond the API key, or any side effects. The description is adequate but not comprehensive.

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 followed by a brief clarification of the return format. It is concise, front-loaded with the purpose, and every phrase 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?

The tool has 7 parameters (with 100% schema coverage) and no output schema. The description covers the return format, which partially compensates for the missing output schema. It is complete for a search tool but could mention pagination or the default number of results (already in schema).

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

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

Schema description coverage is 100%, so all parameters are described in the schema. The description adds the context that results include 'snippet info' and lists fields, but does not add meaning beyond the schema for individual parameters. A score of 3 is appropriate as the 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 the verb ('Search'), resource ('YouTube'), and specific types ('videos, channels, or playlists'), distinguishing it from sibling tools like yt_channel_details and yt_video_details which focus on specific entities.

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

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

The description implies usage for general search across different resource types but does not explicitly state when to use this tool over alternatives like yt_channel_videos or yt_video_details. The sibling tools' names suggest more specific use cases, but the description does not address 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 are empty, so the description must convey behavioral traits. It correctly indicates a read operation (returns data) and specifies returned fields. However, it does not disclose that it only fetches top-level threads (not replies), rate limits, or authentication requirements beyond the API key.

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, concise and front-loaded with the main action. It efficiently communicates the purpose and output fields with no redundancy.

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

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

Given no output schema and empty annotations, the description should compensate by explaining the return format, pagination, and potential errors. It only lists a few fields, missing details like the structure of the response, pagination through max_results, and that only top-level threads are returned.

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 describes all parameters. The description adds no extra meaning to parameters beyond what the schema provides. The schema descriptions are clear, but the tool description does not enhance them.

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 top-level comment threads from a YouTube video, specifying the returned fields (author, text, like count, publish date). This distinguishes it from sibling tools like yt_search and yt_video_details, though it does not explicitly contrast with other comment-related tools.

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

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

No guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., video must exist), limitations (e.g., only top-level threads, not replies), or context for use. The description simply states what it does without any usage advice.

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

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

Annotations are empty, so the description must disclose behavior. It mentions retrieval of details (non-destructive), but does not address rate limits, API key requirements, or response size constraints. The description is adequate but lacks depth for a mutation-free tool.

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

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

The description is a single sentence that efficiently conveys the tool's purpose and output data. No unnecessary words, but could be slightly more structured (e.g., bullet points) for readability.

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

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

Given no output schema, the description does a fair job listing returned fields. However, it omits details like error handling, quota usage, or pagination for multiple IDs. The tool is simple but could be more complete.

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

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

Schema coverage is 100%, with both parameters described in the schema. The description does not add meaning beyond the schema, but baseline is 3 due to full coverage. The mention of 'comma-separated list' and 'max 50' in the schema is sufficient.

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 'detailed information about one or more YouTube videos' and lists specific data points (title, description, channel, duration, view/like/comment counts, tags). This verb+resource combination is precise and distinguishes it from siblings like yt_search or yt_channel_videos.

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 fetching video metadata but provides no explicit guidance on when to use this vs. alternatives like yt_search or yt_channel_videos. No exclusions or context for selecting this tool over siblings are given.

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