Steam

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by explaining the probing behavior, scoring mechanism, and the financial implication of using Anthropic (BYO key, user pays). No contradictions with annotations.

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

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

The description is two sentences: the first explains the core function and output, the second provides defaults and optional configurations. It is front-loaded with the most important information and contains no unnecessary words.

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

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

Given the tool has 4 parameters with 100% schema coverage, rich annotations, and no output schema, the description is complete. It covers purpose, usage, behavior, parameter guidance, and output structure (per-model and combined view). No gaps remain.

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

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

Schema description coverage is 100% for all 4 parameters. The description adds meaning by explaining the default model, the requirement for _apiKey with 'anthropic' model, and the purpose of the 'context' parameter for disambiguation. It builds on the schema effectively.

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 specific verb 'probe' and resource 'LLMs', and explains the output is a visibility score per model. It distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on brand/topic visibility rather than competitor scanning.

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 it is useful for 'AI-marketing audits, pre-launch brand checks, competitive monitoring,' providing clear context. It also explains when to use the default model and when to provide an API key for Anthropic, though it does not explicitly state when not to use the tool.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the external API source (Steam Store API) which provides some behavioral context, but no further details on response characteristics or error behavior.

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

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

The description is extremely brief, but it is under-specified rather than efficiently concise. It omits necessary details, making it less useful. Every word counts, but more words are needed.

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

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

Given the presence of an output schema, the description does not need to document return values. However, it lacks usage context, parameter guidance, and behavioral information beyond what annotations already provide. The tool is too minimally described for its complexity.

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

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

Schema description coverage is 0%, and the description does not explain any parameter meaning. Despite the parameter names being somewhat self-explanatory, the lack of any description reduces usability for an agent.

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

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

The description clearly states the tool retrieves store page details via the Steam Store API. The verb is implied but sufficient, and it distinguishes from sibling tools like app_news or app_list.

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. No context about prerequisites or typical use cases.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description adds minimal behavioral context. It flags the list as 'large', which is useful for performance awareness, but does not disclose potential pagination 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?

The description is extremely concise at four words and directly front-loads the key information. It earns its place, though slightly more context (e.g., scope) could be added without sacrificing brevity.

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 parameters and rich annotations, the description is adequate for a simple list tool. However, it lacks details about output formatting, potential size limits, or pagination behavior, which would help an agent handle the 'large' response.

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 zero parameters, so the description adds no parameter details. However, it correctly implies the tool takes no filters, and with 100% schema description coverage, 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 'Full app list (large)' clearly states the tool returns a complete list of apps and indicates its large size. It is distinguished from sibling tools like app_details and app_news which focus on specific app information.

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 is provided on when to use this tool versus alternatives. For example, it does not mention that if a single app's details are needed, app_details would be more appropriate, nor does it indicate performance considerations despite noting 'large'.

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, establishing safety. The description adds 'feed' implying list retrieval but lacks detail on behavior like pagination or error handling.

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

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

Extremely concise at three words, but this brevity sacrifices clarity regarding functionality and parameters, making it under-specified for effective use.

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 existence of an output schema, the description is incomplete. It omits explanations for three parameters and usage contexts, leaving the agent to rely on examples alone.

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

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

Schema description coverage is 0% and the description does not mention parameters (appid, count, maxlength). The agent gains no semantic meaning beyond the schema 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 'App news feed' names the resource and implies retrieval, but it is too vague to distinguish from siblings like app_details or app_list. It does not specify scope or output nature.

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 such as app_details or search_within. The description provides no context for 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 declare readOnlyHint, openWorldHint, idempotentHint. Description adds value by explaining it routes to 4,552 tools across 1,160 sources and returns citation URIs, but does not disclose potential latency or 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?

Description is well-structured with front-loaded preference statement, then usage guidance, examples, and escalation paths. Some redundancy in examples but overall efficient given tool complexity.

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

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

Covers return format (structured answer with citation URIs), prerequisites (none), and provides extensive examples. No output schema exists, so description adequately compensates.

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 natural language question parameter and multiple aliases. Description reinforces that the parameter is a natural language question but does not add structural details 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 answers factual questions using authoritative structured data, distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, and provides specific use cases (SEC filings, FDA data, etc.).

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 instructs to prefer over web search for factual questions, provides guidance on when to use alternatives (grounded for hallucination-resistant, deep_research for broad questions), and gives concrete examples of 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, etc., but the description adds substantial behavioral details: picks from 4552 tools, extracts answer only from result, returns refusal reasons with specific fields. No contradiction with annotations.

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

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

Well-structured with key points front-loaded, but slightly verbose. Every sentence adds value, though could be tightened slightly 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?

Fully describes tool behavior, output format, refusal reasons, and trade-offs given the high-stakes context. No output schema, so the description appropriately covers return value semantics.

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

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

Schema coverage is 100%, so description adds minimal new information beyond restating that the parameter accepts natural language. Adequate but not enhanced.

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 a 'hallucination-resistant answer mode' for high-stakes reads, distinguishing it from the sibling 'ask_pipeworx' by explicitly contrasting its grounded behavior and output format.

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

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

Explicitly advises use when answers will be quoted or acted on, and suggests preferring ask_pipeworx for casual lookups due to extra cost, providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations declare `readOnlyHint: true`, `destructiveHint: false`, and `openWorldHint: true`. The description adds extensive behavioral context: short-circuits on low-confidence/closed markets, skips fan-out, includes spread warnings, parses cancellation rules, and explains parent event extraction and news fallback behavior. No contradictions.

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

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

The description is front-loaded with the core purpose and usage, then dives into detailed sections (classifiers, fan-out examples, response shapes, edge cases). Every sentence adds value, but the length could be slightly trimmed for readability. Still, very well-structured.

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

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

For a complex tool with no output schema, the description covers all key aspects: input forms, fan-out logic per category, response shape (market, analysis, evidence), resolver contract, parent event extraction, news fallback, safety mechanisms, spread warnings, and resolution-rule risk. No gaps.

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

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

Schema covers 100% of parameters. The description adds meaning by providing examples for `market` (slug, URL, question text), explaining default behavior for `depth` and `include_raw`, and clarifying when to use `include_raw: true` (large payloads for recomputation).

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

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

The description specifies the verb 'Research', the resource 'Polymarket bet', and the action 'pulling Pipeworx data' in one call. It distinguishes from sibling tools like `polymarket_arbitrage` and `polymarket_edges` by focusing on evidence gathering and market-vs-model comparison for betting decisions.

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: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. Provides guidance on when not to rely on results (low-confidence matches, closed markets) and when to adjust `include_raw` parameter. Covers resolution-rule risk as a prerequisite 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?

Annotations already indicate read-only, idempotent, open-world, and non-destructive. The description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, and return format (paired data + 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-structured, with front-loaded examples in quotes and bold for emphasis. Every sentence provides unique information without redundancy. It's appropriately sized given the complexity of the tool.

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

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

Given the simple schema (2 params, 100% coverage, no output schema), the description covers return format (paired data, citation URIs), sorting behavior, data sources, and edge cases (off-calendar fiscal years). It fully equips an agent to use the tool effectively.

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

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

Schema coverage is 100%, so parameter descriptions exist. The description adds significant value: explains what data each type pulls (revenue, net income, etc. for company; adverse events, approvals, trials for drug), and clarifies the format for values (tickers/CIKs for company, names for drug). This goes 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 uses specific verbs ('compare', 'side-by-side comparison') and resources ('entities', 'companies or drugs'). It clearly distinguishes from sibling tools by stating 'ALWAYS PREFER over sequential single-pack lookups' and mentions it replaces 8-15 sequential lookups. Example queries are provided.

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 (comparing 2-5 entities, head-to-head, ranking) and when not to (over sequential lookups). Describes limitations (max 5, min 2) and data sources per type. Also explains how results are sorted to answer questions like 'largest'.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no additional behavioral information such as authentication, rate limits, or response format.

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

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

Extremely short (3 words) but lacks essential context. It under-specifies the tool rather than being efficiently 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?

Even with an output schema present, the description fails to explain the input parameter or usage context. For a simple tool with one param, the description should at least mention the appid's 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?

The single parameter 'appid' is not described in the schema (0% coverage) and the description does not explain what it represents (e.g., a Steam appID). The agent has no way to infer its meaning.

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

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

The description 'Current concurrent players' states what the tool returns, but lacks context that it is per appid. It does not differentiate from siblings like 'player_stats' which also involve player numbers.

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

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

No guidance on when to use this tool vs alternatives. No mention of prerequisites, typical use cases, or when not to use it compared 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 provide readOnlyHint, idempotentHint, and openWorldHint. The description adds significant behavioral context: account required, expected 15-60s response time, returns gaps[] for unanswerable facets, and clarifies it is not open-web search. No contradictions with annotations.

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

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

The description is verbose but well-structured and front-loaded with critical prerequisites (account requirement, alternative tool). Every sentence adds information; however, slight reorganization could improve scannability. Still earns a 4 for density of useful content.

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

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

Given the tool's complexity (multi-source, parallel execution), the description comprehensively covers purpose, prerequisites, behavior, timing, and output format (findings packet with citations and gaps). No output schema exists, but the description adequately explains return values.

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, so baseline is 3. The description adds value by explaining the depth enum values correspond to facet counts (quick=3, standard=5, thorough=8) and that question is natural language. This enhances understanding 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 performs grounded multi-source research across 1,160 structured data sources, decomposing questions into facets and using 4,552 tools in parallel. It distinguishes itself from the sibling ask_pipeworx by specifying it is for broad/multi-part questions over structured data, not for single lookups or breaking news.

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 to (breaking/colloquial current news), providing a direct alternative (ask_pipeworx). Also notes account requirements and that depth:'thorough' needs a paid plan.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: results are 'ready to call directly, no second schema lookup needed' and include full input schemas with curated examples, enhancing behavioral transparency 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: two sentences. The first states the purpose, the second explains the return value and usage guidance. Every sentence adds value without unnecessary detail.

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

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

Despite no output schema, the description explains what is returned: 'the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples).' It also mentions default limit (20) and max (50) as per schema. This is complete for the tool's purpose.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds semantics by stating that the `query` parameter 'Accepts task, q, description, search as aliases,' which is not fully captured in the schema where each alias has its own description. This extra clarity 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 tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, financials, etc.) and distinguishes itself from sibling tools by emphasizing discovery and the return of ready-to-call results with full schemas.

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

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

The description explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context for when to use the tool, though it does not explicitly mention when not to use it or name alternatives.

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

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

Annotations already declare readOnly and idempotent hints. Description adds value by detailing the fan-out across multiple sources, specific return fields, and the patent soft-fail due to USPTO API sunset. 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?

Front-loaded with example queries, well-structured listing of sources and fields. Slightly verbose in the middle but each sentence earns its place by adding necessary context (e.g., patent sunset, fallback mechanisms).

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 covers all major return fields and caveats (patent sunset, source fallback). For a complex multi-source profile tool, it provides adequate completeness without being overwhelming.

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 includes detailed descriptions for both parameters. Description repeats the same information (ticker/CIK vs. names not supported) without adding new semantic meaning beyond what the schema already provides.

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

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

The description clearly states it provides a full cross-source profile of a US public company in one parallel call, listing specific data sources (SEC EDGAR, XBRL, USPTO, etc.). It distinguishes itself from sibling tools like resolve_entity and compare_entities by emphasizing holistic view and avoiding chaining.

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 'ALWAYS PREFER over chaining' for holistic user queries. Also warns that names are not supported and directs to use resolve_entity first. Lacks explicit exclusions for when not to use, but the guidelines are strong for the intended use case.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description's mention of 'delete' aligns with destructiveHint, and it adds contextual usage advice. No contradiction; description enhances transparency with scenario-based guidance.

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 the core purpose. Every sentence adds value; no unnecessary words.

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

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

Given the tool's simplicity (single required parameter, no output schema, no nested objects), the description fully covers purpose, usage, and relationship to siblings. No missing information.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add extra meaning beyond the schema for the 'key' parameter, which is already documented.

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 'Delete a previously stored memory by key,' identifying the specific verb and resource. It also distinguishes itself from sibling tools 'remember' and 'recall' by mentioning them.

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

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

Provides explicit guidance on when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also notes alternative tools, fulfilling the dimension requirements.

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 that it returns an array with relationship status, but no new behavioral traits 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?

Two concise sentences, no wasted words. The key action and details are 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?

The description fully explains input, optional filter, and output format, which is sufficient for this simple tool. Output schema likely provides further details.

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

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

With 0% schema description coverage, the description explains both parameters well: 'steamid' is a SteamID64, 'relationship' can be 'friend' or 'all'. This adds meaning beyond the raw 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 specifies the verb 'List' and resource 'friend list', with clear input (SteamID64), optional filter, and output. This distinguishes it from other 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 tool's purpose is self-evident, but it does not explicitly mention when to use it over siblings like 'player_summary' or 'owned_games'. However, the description is clear enough for selection.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which already indicate a safe, idempotent operation. The description adds behavioral details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format', as well as output format. No contradictions, but it doesn't address potential failure modes or limitations.

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

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

The description is a single paragraph of 4 sentences, tightly packed with essential information: purpose, process, output, and use cases. Every sentence adds value with no redundancy or fluff.

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

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

With 2 parameters, 100% schema coverage, no output schema, and strong annotations, the description provides a complete overview: what the tool does, how it works, and output format. It covers usage scenarios but omits error handling (e.g., invalid URL) and specific output structure details. Overall sufficient for the tool's simplicity.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description adds slight context (e.g., 'Full URL' example and default/max for max_links) but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Generate a production-ready llms.txt file for any URL'. It specifies the verb (generate), resource (llms.txt file), and provides concrete use cases. The title is consistent, and the description distinguishes the tool from siblings by focusing on llms.txt generation.

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 explicit use cases (getting a client's site indexed, drafting for own project, auditing competitor), which guides when to use the tool. However, it does not explicitly state when not to use it or mention alternatives among sibling tools, though the uniqueness of the tool reduces the need for 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 provide readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which sufficiently describe the tool's safety and idempotence. The description adds minimal behavioral context beyond listing return fields and mentioning the include_inactive parameter, but it does not contradict annotations.

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

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

The description is concise with two sentences: the first clearly states purpose and return fields, the second provides usage guidance. Every sentence adds value with 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?

The description is complete given the tool's simplicity. Annotations cover safety and idempotence, the schema covers the parameter, and the description lists return fields (since there is no output schema). The usage guidance completes the picture, making it fully actionable for an AI agent.

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

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

Schema coverage is 100%, with the single parameter 'include_inactive' already described in the schema. The description does not add any additional parameter semantics beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states 'List the caller's active subscriptions,' specifying a specific verb and resource. It distinguishes itself from sibling tools like subscribe, unsubscribe, and other list tools by focusing on 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?

The description explicitly advises when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context, though it does not explicitly mention when not to use it or list alternatives.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds specific behavioral details, including return fields (appid, playtime_forever in minutes, per-game metadata) and optional inclusion of app info and free-to-play games. 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, front-loaded with the primary action, and every word adds value. No extraneous information or repetition of schema defaults.

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 existence of an output schema (not shown but indicated), the description appropriately lists key return fields. Combined with annotations, the description is largely complete. Minor gaps: no mention that the SteamID must be a valid 64-bit ID or that results exclude non-owned games, but these are reasonable assumptions.

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 0% schema description coverage, the description compensates by explaining the purpose of each parameter: 'include app name/icon' for include_appinfo, 'free-to-play games' for include_played_free, and the required steamid is clearly a SteamID64. This provides adequate meaning beyond the raw 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 lists all games owned by a SteamID64, which is a specific verb-resource combination. It distinguishes from sibling tools like 'recently_played' by emphasizing 'all games'. The mention of optional parameters and return fields adds precision.

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 (list all owned games) but does not explicitly state when not to use it or mention alternatives like 'recently_played'. The guidance is implied but not fully explicit.

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 no behavioral hints, so description carries full burden. It discloses rate limits (5 per identifier per day), free status (no quota usage), and that feedback is reviewed daily and affects roadmap. Slightly more detail (e.g., expected response) could elevate to 5.

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?

Every sentence adds value; no redundancy. Front-loaded with purpose, then usage, then constraints. Well-organized and concise for the information conveyed.

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

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

Given the tool's simplicity (no output schema), description fully covers input purpose, allowed scenarios, behavioral constraints, and integration notes. No gaps identified for effective invocation.

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

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

Schema coverage is 100%, but description adds value by explaining enum values for 'type' (bug, feature, etc.), clarifying optional 'context' fields, and imposing constraints on 'message' (1-2 sentences, 2000 chars max) and content guidelines ('describe in terms of Pipeworx tools').

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 explicitly states 'Tell the Pipeworx team something is broken, missing, or needs to exist' and enumerates specific use cases: bug, feature/data_gap, praise. It clearly distinguishes this feedback tool from siblings, which are predominantly query/action tools.

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

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

Provides explicit when-to-use scenarios (bug, feature/data_gap, praise) and what not to do ('don't paste the end-user's prompt'). Also mentions rate-limiting and quota exemption, aiding appropriate invocation.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: data source (CF analytics-engine), no PII, caching behavior (5min-1h), and aggregated nature. No contradictions.

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

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

The description is concise (three sentences) with bulleted use cases. Every sentence adds value. No redundant information.

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

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

Despite no output schema, the description clearly states what the tool returns (top tools, top packs, total call volume). It also covers caching and data source, providing a complete understanding of the tool's 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?

Schema coverage is 100% with description for window parameter. The tool description adds additional meaning by explaining the trade-off between shorter and longer windows, which helps agents choose appropriately.

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

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

The description clearly states the tool returns trending data: 'top tools, top packs, and total call volume'. It uses specific verbs like 'returns' and 'calling on', and distinguishes from siblings like discover_tools by focusing on what other agents are currently using.

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 three explicit use cases (discovering hot sources, confirming canonical choices, aligning use cases) and explains window choice trade-offs. However, it does not explicitly state when not to use this tool or compare it to alternatives like discover_tools.

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

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

Annotations already declare readOnlyHint and idempotentHint, but description adds no behavioral context beyond a generic phrase; 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?

Extremely short but under-specified; fails to convey necessary detail.

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

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

With 3 parameters and no parameter info, description is insufficient; output schema exists but description does not leverage it.

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

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

Schema description coverage is 0% and description mentions none of the 3 parameters (steamid, appid, l); agent gets no help understanding meanings.

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 'Achievements for a game' is vague; does not specify that it retrieves achievements for a player or distinguish it from siblings like player_stats or player_bans.

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

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

No guidance on when to use this tool vs alternatives; sibling tools list is long but description provides no 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 read-only, idempotent, non-destructive behavior. The description adds value by naming the specific fields returned (CommunityBanned, VACBanned, NumberOfVACBans, DaysSinceLastBan), providing transparency on what the tool checks and returns. No contradictions.

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

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

Two sentences: first states purpose and input format, second lists return fields. No extraneous information. Front-loaded with key action and resource.

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 existing output schema (not shown but present) and comprehensive annotations, the description covers the tool's functionality and return values. It lacks edge-case handling or limitations but is sufficient for correct 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 has a single string parameter 'steamids' with no description. The description explains it should be one or more comma-separated SteamID64s, compensating for the lack of schema documentation. This adds essential semantic meaning.

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

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

Description clearly states the tool checks VAC, community, and economy ban status for one or more SteamID64s. The verb 'Check' and resource 'bans' are specific, and it distinguishes from sibling tools that deal with other player data (e.g., player_level, player_stats).

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

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

The description explains the input format (comma-separated SteamID64s) but does not provide guidance on when to use this tool versus alternatives like player_summaries or resolve_vanity_url. No explicit when-not or alternative tool recommendations.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds no behavioral context beyond that, such as rate limits, caching, or error behavior.

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

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

Single sentence, front-loaded with the key information. No redundant 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, full annotations, and an output schema, the description is complete. It tells the user exactly what to expect.

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

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

Schema has 0% description coverage, but the description adds meaning by specifying that the parameter is a 'SteamID64'. This clarifies the required format beyond the schema's plain string type.

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

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

Description clearly states the action (Return), the resource (Steam Community level), and the input (SteamID64). It is specific and distinct from siblings like player_summaries or player_stats, which return multiple fields.

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

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

No guidance on when to use this tool vs alternatives. For example, it does not mention that this tool is best when only the level is needed, or differentiate from player_summaries which also includes level.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds no behavioral details beyond a generic statement, failing to disclose any specific traits like response format or scope.

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?

While the description is short, it is under-specified and uninformative. The single sentence does not earn its place as it adds almost no useful information.

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

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

Despite having an output schema, the description fails to clarify what specific stats are returned. The lack of context makes it difficult for an agent to determine if this tool meets the user's needs, especially with many similar siblings.

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

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

Schema description coverage is 0%, and the description does not explain the meaning or expected format of the required 'appid' and 'steamid' parameters. The agent receives no semantic value beyond the raw 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 'User stats for a game' is extremely vague. 'Stats' could refer to many different metrics, and the tool does nothing to distinguish itself from siblings like player_achievements, player_bans, or player_level, which all provide more specific types of user statistics.

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 is provided on when to use this tool versus alternatives. Given the number of related sibling tools, an agent would have no basis to choose this one without additional 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?

Description adds batch limit (100), comma-separated format, and required SteamID64. Annotations already cover read-only, idempotent, and non-destructive nature. No contradictions.

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

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

Single sentence, no unnecessary words, front-loaded with action and scope.

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 1 parameter, annotations covering safety, and output schema present, the description provides complete context: input format, batch limit, and output 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 has 0% coverage for 'steamids' parameter; description explains it takes up to 100 comma-separated SteamID64 values, adding crucial meaning beyond the raw 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 action (fetch), resource (public Steam profiles), and what is returned (persona name, avatar, country, status). It also specifies batch size and format, distinguishing it from singular player_summary and other 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?

The description implies usage for batch profile retrieval, but does not explicitly mention when to avoid or alternatives like player_summary for single queries. Nonetheless, context is clear.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by specifying exact output fields and confirming the tool accesses public profile data. It does not mention rate limits or error behavior, but the annotation coverage reduces the 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?

The description is a single concise sentence front-loaded with the verb 'Fetch' and resource 'public Steam profile', followed by a comma-separated list of returned fields. Every word serves a purpose 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?

The description sufficiently covers the tool's purpose, input, and output. With an output schema present, it need not detail return types. However, it lacks guidance on prerequisites (e.g., valid SteamID64 format) and edge cases (e.g., non-existent profile), which would improve completeness for new users.

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 zero description coverage for the sole parameter 'steamid'. The tool description specifies it expects a 'SteamID64', providing format context that the schema lacks. This compensates well for a single parameter, though additional validation hints (e.g., length, pattern) could further clarify.

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 'Fetch public Steam profile for a single SteamID64' and lists specific fields returned (persona name, avatar, profile URL, country, last logoff, visibility status). It distinguishes from sibling 'player_summaries' by emphasizing 'single SteamID64'.

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 a single player's profile but does not explicitly state when to use this tool over alternatives like 'resolve_vanity_url' or 'player_summaries'. No exclusions or alternative guidance is provided.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description goes well beyond by disclosing internal logic (Jaccard similarity threshold, partition filter, fill_check behavior), realizable vs theoretical edge, and what happens when partition signal fires. 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 lengthy but well-structured with clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every sentence adds necessary detail for correct usage. It is front-loaded with the core purpose and mode selection, making it efficient despite its length.

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

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

Despite the absence of an output schema, the description fully documents the response format: 'opportunities[] (gap_pp, suggested_trade, reasoning, ...) and partition_check{...}'. It covers edge cases (placeholder slugs, low similarity, thin legs) and references a companion tool for sizing, making it self-contained.

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

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

Schema coverage is 100% and schema descriptions already define parameters. The description adds value by providing concrete examples ('fed-decision-may-2026'), explaining that full URLs are accepted, and specifying the corresponding behavior for event vs topic modes. This enriches 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 explicitly states 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks', a specific verb-resource-mechanism combo. It distinguishes itself from sibling tools like polymarket_fill_risk and polymarket_edge_tracker by detailing its unique algorithmic approach.

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 when-to-use guidance: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', 'pass topic for a themed cross-event scan'. It recommends event for specific markets and explains when to use fill_check before trading. It also mentions alternative tools for custom sizing.

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 exceeds annotations by detailing caching (1h), model families, edge calculations, filtering logic, and diagnostics. No contradiction with readOnlyHint=true, openWorldHint=true, 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?

Description is very long and dense but well-structured with clear segments, knobs, and diagnostics. Could be more concise without losing key details.

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

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

Extremely complete given complexity: explains response structure, edge calculations, caching, diagnostics, and why fed_candidates are excluded. No output schema needed due to thorough description.

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

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

Schema coverage is 100% with rich descriptions. Description adds context like 'Tradeable-edge filter' for max_spread_pp and explains interaction of min_partition_leg_kelly. Slight additional value beyond schema.

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

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

The description clearly states it scans Polymarket markets to return opportunities where Pipeworx data disagrees with market price, built for 'what should I bet on today'. It specifies three segments and distinguishes itself from siblings 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?

Provides extensive guidance: when to use (discovery), how to set knobs (min_liquidity, max_spread_pp), includes diagnostics for empty segments, and explains tradeable-edge filters. Explicitly says 'agents discover opportunities without paging hundreds of markets.'

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

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

Annotations already indicate safe, idempotent behavior. Description adds extensive behavioral details: return structure (tracked, expired, snapshot_dates), computation of trend/decay, limits (60-day TTL, daily closes), exceeding annotation requirements.

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 comprehensive and well-structured, front-loading the key question and proceeding to details. Slightly verbose but each part adds value; could be more concise but not at cost of clarity.

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

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

Despite no output schema, the description thoroughly explains all return fields, their meanings, and constraints (e.g., snapshot gaps, decay calculation). Covers interpretation guidance and limitations, making it complete for a complex analytical 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 has 100% coverage with descriptions. Description adds operational context: defaults for days (14) and window ('1wk'), clamp range for days, and window options (24hr, 1wk, 1mo), which are not enumerated in 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 explicitly states the tool provides 'edge persistence and decay telemetry' and answers a specific question about edge longevity, clearly distinguishing it from sibling tools like polymarket_edges which likely provides current snapshots.

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

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

The description provides context for when to use this tool by contrasting fresh vs old edges, implying its utility for decay analysis. However, it does not explicitly state when not to use it or compare with alternatives, but the context is clear.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds significant context: warns about partial basket fills converting an arb into an unhedged directional position, and explains that theoretical overround on thin books is not capturable. No contradictions.

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

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

Description is longer than typical but well-structured with clear sections (SINGLE-MARKET, BASKET) and bullet-like lists. Front-loaded with core purpose. Every sentence adds value, though could be slightly more concise.

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

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

Despite no output schema, description thoroughly lists return values for both modes (top_of_book, vwap_fill_price, slippage_pp, etc. for single-market; theoretical_sum, realizable_sum, thin_legs, etc. for basket). Also provides context about limitations and risks.

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% already. Description adds value by explaining the meaning of size_usd differently for single-market (spend/target) vs basket (settlement notional), and clarifies side defaults (auto for basket). This enhances 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 defines the tool's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes two modes (single-market and basket) and explicitly contrasts with sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly states when to use this tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also explains why (theoretical overround not capturable, partial fills lead to unhedged directional risk).

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and nondestructive. The description adds extensive context: explains compatibility_warning conditions, temporal_alignment, skipped_cross counters, and that real spreads are rarer than shortcuts suggest. 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 well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and is front-loaded with the core purpose. While comprehensive, it is slightly verbose; some minor redundancy could be trimmed, but overall it earns its length.

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

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

Despite lacking an output schema, the description thoroughly explains the response structure (leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters). It covers all key aspects needed for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100%, but the description adds significant meaning: explains the two operational modes, gives example values for topic, and clearly states that explicit parameters override the topic-mapped side. This goes 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 the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifies two distinct modes (topic shortcuts and explicit tickers) and distinguishes itself from sibling tools like polymarket_arbitrage by focusing on cross-venue 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?

Provides clear guidance on when to use topic shortcuts vs explicit tickers, and warns that pre-mapped topics often yield compatibility warnings. However, it does not explicitly state when to avoid using the tool or what alternatives exist for pure single-venue analysis.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds scoping and behavior when key omitted, providing useful 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 clear sentences, no wasted words. Front-loaded with purpose, then usage details.

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

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

Given simple schema (1 optional param) and comprehensive annotations, the description fully covers behavior, scope, and relationship to sibling tools.

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

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

Schema coverage is 100% for the single optional parameter. Description adds that omitting key lists all saved keys, which is not in the schema.

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

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

The description clearly states it retrieves a saved value or lists all keys, with specific examples like user's target ticker or research notes. It's distinguished from siblings remember and forget.

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

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

Explicitly explains when to use: to retrieve context stored earlier. Mentions scope and pairing with remember/forget. Could add when not to use, but sufficient.

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

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

Annotations declare readOnlyHint=true, indicating no state modification, but description states mark_read:true flags events as read, which is a write operation. This is a direct contradiction, earning 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?

Four well-structured sentences, front-loaded with action, no unnecessary words. All information is relevant and efficiently presented.

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

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

Covers return fields, filtering options, side effects of mark_read, and alternative access URL. However, contradictions undermine trust and completeness for a tool with no output schema.

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

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

Schema coverage is 100% (each parameter described in schema). Description adds examples (type filter like 'sec_8k') and clarifies mark_read effect, but does not significantly exceed schema detail. 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?

Clearly states it pulls fired events from subscription feed and lists return fields. Lacks explicit differentiation from sibling tools like list_subscriptions, but purpose is specific and unambiguous.

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

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

Provides guidance on filtering by type and since, using mark_read for incremental reads, and notes polling is fine. Does not explicitly exclude use cases or list alternatives, but context is clear.

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

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

Even with strong annotations (readOnlyHint, idempotentHint, etc.), the description adds significant behavioral details: fan-out to multiple sources, fallback logic, date formats accepted, return structure (changes grouped by source, total_changes, URIs), and the inactive state of USPTO. 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, but it efficiently covers purpose, usage, behavior, and parameter details. It front-loads example queries, making it immediately scannable. Could be slightly more structured (e.g., bullet points), but it avoids verbosity.

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

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

Given the tool's complexity (multiple sources, fallback, no output schema), the description does a good job explaining the overall behavior and output structure. It covers key aspects but lacks explicit mention of pagination or result limits. Still, it is mostly complete and helps an agent assess usefulness.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for 'value' (ticker or CIK), explaining accepted formats and typical values for 'since' (e.g., '30d'), and noting that only 'company' is supported for 'type'. This enhances understanding beyond the schema.

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

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

The description clearly states it provides a change feed for a company in a recent time window, fanning out to multiple data sources (SEC EDGAR, GDELT/GNews, USPTO). It includes realistic example queries and distinguishes itself from sibling tool 'entity_profile', making its purpose unambiguous.

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

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

The description explicitly tells when to use this tool (for recent changes) and when to prefer 'entity_profile' instead. It explains fallback behavior between GDELT and GNews, and caveats about USPTO soft-failure. This provides clear usage context and alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds useful behavioral details: it returns both past-2-weeks and total playtime, and the count parameter caps results. 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, efficient sentence with no wasted words. It immediately states the verb and resource, then adds return details and an optional parameter behavior. Perfectly 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 parameters, existing output schema), the description covers the essential purpose and parameter functionality. It omits nothing critical, though slight additional context on ordering or default count could improve completeness. Output schema likely fills remaining 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?

With 0% schema parameter description coverage, the description must compensate. It explains the 'count' parameter as an optional cap. However, it does not detail the 'steamid' parameter beyond its name in the description (though it implies a SteamID64 format). This is adequate but not thorough.

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

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

The description clearly specifies the action ('list'), the resource ('games played by a SteamID64 in the last two weeks'), and the returned data ('playtime in the last 2 weeks and total playtime'). It effectively distinguishes from sibling tools like 'owned_games' or 'player_achievements' by focusing on recent playtime.

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?

While the description implies usage for recent playtime, it does not explicitly state when to use this tool over siblings or provide exclusion criteria. No alternative tools or when-not scenarios are mentioned, which is a limitation.

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

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

The description adds significant behavioral context beyond the annotations: 'Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' This explains scope, persistence, and retention, which are not covered by readOnlyHint, idempotentHint, or 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?

The description is concise—four sentences covering purpose, usage, storage mechanics, and retention. No redundant phrases. Information is front-loaded with the primary action, then context, then details.

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

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

For a simple two-parameter tool with no output schema, the description fully addresses all contextual needs: what it does, when to use it, how data is stored, persistence differences, and companion tools. Nothing is missing.

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 has 100% coverage with clear descriptions for both key and value. The description adds value by providing example key patterns ('subject_property', 'target_ticker', 'user_preference') and clarifying that value is 'any text'. This enriches the parameter semantics 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: 'Save data the agent will need to reuse later.' It uses a specific verb ('save') and resource ('data'), and distinguishes from sibling tools by mentioning 'recall' and 'forget' as companions.

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

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

The description provides explicit guidance on when to use: 'when you discover something worth carrying forward... so you don't have to look it up again.' It also mentions pairing with recall and forget. While it doesn't explicitly state when not to use, the context is clear enough.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable detail: internal cascading through multiple endpoints, auto-disambiguation for companies, and the exact fields returned for each type (ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand for drug). No contradictions with annotations.

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

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

The description is front-loaded with examples and a clear purpose statement, followed by usage guidance and detailed type breakdowns. Each sentence contributes meaningful information. It is slightly longer than strictly necessary but remains well-organized and readable.

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

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

Given the tool has no output schema, the description compensates by detailing the exact return fields for each entity type, including citation URIs. It explains the internal cascading mechanism. It covers the two supported types thoroughly, though it does not hint at potential future types or edge cases (e.g., what if the name matches multiple entities?). Still, it provides sufficient context for correct invocation.

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

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

Schema coverage is 100% (both parameters described). The description adds meaning beyond the schema by explaining how the 'value' parameter accepts multiple input formats (ticker, CIK, or name for company; brand or generic for drug) and mentions auto-disambiguation. This provides semantic richness beyond the enum and type constraints.

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

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

The description opens with concrete examples ('What's the ticker for…', 'find the CIK for…', etc.) and clearly states the tool resolves names to canonical identifiers. It specifies supported types (company, drug) and contrasts with sibling tools by emphasizing it should be used first when converting names to IDs.

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 includes the explicit directive 'Use FIRST whenever you have a name but need an ID.' It also notes that using this tool replaces '2-3 manual lookups,' implying efficiency. While it doesn't explicitly list when not to use it, the context from sibling tools and the clear purpose make alternative choices reasonably obvious.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds minimal behavioral context beyond stating the mapping; it does not mention what happens if the vanity URL is invalid, the format of the output, or any rate limiting.

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

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

The description is extremely concise: a single phrase with no wasted words. It is front-loaded and provides the essential purpose immediately.

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 required string parameter, no nested objects), the minimal description is somewhat adequate. An output schema exists, so return values need not be described. However, the description could be more helpful by noting that the conversion is to a 64-bit integer or that it resolves a vanity URL from Steam.

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

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

Schema description coverage is 0%, meaning the description does not explain the 'vanityurl' parameter at all. Despite having an example in the schema, the description adds no meaning about the expected format, constraints, or 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 'Vanity URL → SteamID64' clearly states the tool's function: it converts a Steam vanity URL to a SteamID64. This is a specific verb and resource, and it distinguishes itself from siblings like 'resolve_entity' by specifying the exact conversion target.

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 no guidance on when to use this tool versus alternatives, such as 'resolve_entity' or other resolution tools. It does not mention prerequisites, limitations, 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.

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

The description discloses that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, and signal density. This adds context beyond the readOnlyHint and idempotentHint annotations. Minor gap: does not mention that using the 'anthropic' model requires an API key and calls an external API, but this is partially covered in 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 three sentences long, front-loaded with the main action, and every sentence adds value: comparison definition, internal mechanism, and use case. No redundant or vague wording.

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

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

Given the tool has no output schema, the description adequately explains the return format (ranked list with score, confidence, signal density). It also covers the use case and internal mechanism. Could be more complete by mentioning external API calls when using Anthropic model, but overall sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra value by specifying that the first entity in 'entities' is treated as the subject for narrative and that omitting 'models' uses only workers-ai, which is not in the schema descriptions.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, uses ai_visibility_check internally, and returns a ranked list. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic) by specifying the competitive AI-marketing audit use case.

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

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

The description provides a clear use case ('competitive AI-marketing audits') and an example question. However, it does not explicitly state when not to use this tool (e.g., for single entity checks, use ai_visibility_check) or provide alternative tool 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 mark the tool as readOnly, idempotent, non-destructive, and open world. The description adds valuable behavioral detail: partial failures (bundlephobia's first measurement can take 5-30s), graceful degradation with 'sources_failed', and a list of return fields. 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 fairly long but well-structured, starting with purpose, then usage scenarios, then behavioral details and ecosystem limitations. Every sentence adds value, though it could be slightly more concise. A score of 4 reflects its efficiency.

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

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

Given the tool's complexity (composite check across multiple data sources), the description is comprehensive. It covers the return summary fields, failure modes, ecosystem scope, and even mentions links and alternative versions. Although there is no output schema, the description fills that gap 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 description coverage is 100%, so the schema already documents both parameters. The description adds minor context (scoped packages accepted, version defaults to latest) but does not significantly enhance meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states that this tool is a composite check for adding npm packages, covering license, advisories, version history, and bundle size. It explicitly distinguishes from sibling tools like scan_competitor_ai_presence, providing a specific verb+resource+scope.

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

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

The description explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also notes that this is for NPM ecosystem only in v1 and mentions that other ecosystems fall under a different tool (deps.dev:version), providing clear when-to-use and when-not-to-use guidance.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds specific technical details: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag. It also explains output format (passages with offsets and scores).

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 four sentences front-loaded with purpose, usage, pairing, and technical details. 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 search tool with no output schema, it fully explains what is returned (passages, offsets, scores) and sets expectations (cap, truncation). Input parameters are well-covered, and sibling differentiation is clear.

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

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

Schema coverage is 100% with descriptions for all 3 parameters. The description adds a few examples for the query parameter and mentions the 200K char limit for text, but does not add substantial new meaning beyond the schema descriptions.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' and gives concrete examples (SEC 10-K, article, tool result). It distinguishes itself from siblings like ask_pipeworx_grounded by explaining pairing and complementary use.

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

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

It explicitly tells when to use: 'when the record is too big to cram into the prompt' and suggests pairing with ask_pipeworx_grounded. However, it does not explicitly state when not to use or list alternatives beyond that pairing.

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 expands on annotations by detailing idempotent creation behavior, authentication requirements (OAuth), rate limits (SMS 10/day), and operational details (webhook auto-disable after failures). No contradiction with annotations (readOnlyHint=false, idempotentHint=true).

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

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

The description is informative and front-loaded with the main purpose, but its single-paragraph format could be considered slightly dense. It efficiently uses sentences to explain types, parameters, and channels, earning a 4.

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

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

Given the tool's complexity (3 parameters, nested objects, 5 enum types, 3 delivery channels) and no output schema, the description covers all necessary aspects: input details, constraints, behaviors, and alternatives. It is complete for an agent to select and invoke 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?

Although schema coverage is 100%, the description adds substantial value by explaining each subscription type with examples, parameter constraints (required vs optional), and delivery channel behaviors (phone verification, webhook signing). This helps the agent interpret parameters correctly.

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: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' This is a specific verb-resource combination and distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides explicit context on when to use this tool, including supported subscription types with parameter examples, and hints at alternative tools (recent_alerts for pulling feed data). It also mentions prerequisites (Pipeworx OAuth account) and delivery channel options.

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, openWorldHint. Description adds that it returns example questions with tool shapes and is the onboarding entry point, providing 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?

The description is fairly long but well-organized, front-loading with alternative phrasings and purpose. Every sentence adds value, though it could be slightly more concise.

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

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

Given 1 optional parameter and no output schema, the description adequately explains what is returned (category-bucketed questions with tool shapes) and when to use it. Missing explicit output format but sufficient for understanding.

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

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

Schema coverage is 100%, and the description enriches by listing concrete topic values (finance, pharma, etc.) and explaining that omitting gives full spread. This adds significant value beyond the schema description.

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

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

The description clearly states the tool returns category-bucketed example questions for onboarding, with alternative phrasings. It distinguishes from siblings by advising to use this FIRST when unknown about Pipeworx capabilities.

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

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

Explicitly says when to use (first when unfamiliar, or to learn meta-tools) and how to adjust with topic parameter. Does not explicitly state when not to use, but context implies after onboarding, use specific 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 adds behavioral insight beyond annotations by clarifying that the row is deactivated (not deleted) and historical events remain available. No contradiction with annotations (destructiveHint false, idempotentHint true).

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

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

Two sentences, no redundant information. The description is front-loaded with the action and key constraints, making it highly efficient.

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

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

For a simple tool with one parameter and no output schema, the description covers ownership, effect on the row, and historical data retention. It is complete enough, though it could optionally leverage the idempotency hint from 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?

The input schema provides 100% coverage for the 'id' parameter with a clear description. The tool description does not add significant parameter-specific meaning beyond what the schema already provides.

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

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

The description uses the specific verb 'Cancel' and the resource 'subscription', clearly stating the action and object. It implies a distinct purpose from sibling tools 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?

The description explicitly states ownership enforcement and the effect of deactivation (not deletion), which guides when to use the tool. However, it does not explicitly mention when not to use it or suggest alternative tools for related actions.

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, and idempotentHint. The description adds beyond that by specifying the return format (verdict, structured form, actual value with citation, percent delta) and efficiency gain (replaces 4-6 sequential calls), offering valuable 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 front-loaded with intuitive examples in quotes, followed by purpose and details. It is relatively concise given the complexity, with each sentence earning its place. Minor redundancy exists 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 no output schema, the description adequately explains return values and data source. It covers the domain scope and efficiency. Could mention error cases or limitations more explicitly, but sufficiently 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?

With 100% schema description coverage, the schema already describes the 'claim' parameter. The tool description reinforces this with concrete examples ('Apple's FY2024 revenue was $400 billion') and clarifies natural-language format, adding value beyond the schema.

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

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

The description explicitly states the tool validates natural-language claims against authoritative sources, with concrete examples like 'fact check' and specific domain (company-financial claims via SEC EDGAR). It clearly distinguishes itself from sibling tools by describing its purpose unambiguously.

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 includes explicit instructions on when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also notes the domain limitation (v1 only supports company-financial claims), providing clear context. However, it lacks explicit exclusions for non-financial claims.

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