Hash

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

Annotations already indicate safe, idempotent read. Description adds default model, cost model (BYO key for Anthropic), return structure. 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?

Four sentences, each adds value: action+output, model details, return structure, use cases. No filler.

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

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

Covers all essential aspects: input, output, usage context, cost implications. Adequate given low complexity and good annotations.

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

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

Schema covers all 4 params. Description adds context about default model, API key purpose, and disambiguation role of context param. Meaningful additions.

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?

Specifies verb 'probe', resource 'LLMs', output 'score visibility 0-100 per model'. Clear and detailed. Not explicitly distinguished from siblings but self-contained.

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?

Lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) but does not advise when to avoid or compare with siblings like scan_competitor_ai_presence.

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 that it returns structured answers with citation URIs and routes questions internally. 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?

Front-loaded with key instruction, well-organized but somewhat lengthy. Each sentence adds value, but could be more concise while retaining all 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?

Comprehensive for a complex tool covering purpose, usage, return structure (citations), alternatives, and examples. No output schema, but description covers output adequately.

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 all parameters are described as aliases for the question. Description adds no new semantics beyond listing aliases and providing examples already 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?

Clearly defines the tool as a default entry point for factual questions, routing to thousands of sources, with examples and contrast to alternatives. Distinguishes from siblings like ask_pipeworx_grounded and deep_research.

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

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

Explicitly instructs to prefer over web search, lists specific use cases, provides examples, and details when to step up to other tools (grounded, deep_research) and when it already handles breaking news.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds valuable behavioral context: internal routing, extraction process, refusal reasons, and cost tradeoff. 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 information-dense but front-loaded with the core purpose. It could be slightly trimmed, but every sentence adds value (usage, return format, refusal reasons, cost).

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

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

Given the complexity (4,774 tools across 1242 sources, refusal reasons, sibling differentiation), the description is thorough. No output schema exists, but the return format is well-documented.

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 6 parameters. The description adds minimal extra meaning beyond noting aliases. Baseline 3 is appropriate given high schema coverage.

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

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

The description states a specific verb ('extracts the answer using ONLY what the tool result contains') and a unique resource (hallucination-resistant answer mode). It explicitly distinguishes itself from ask_pipeworx by noting the extra LLM call and use case preference.

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 clearly says when to use (high-stakes reads, quoting/citing, financial/legal/medical) and when not (prefer ask_pipeworx for casual lookups). This is explicit guidance with alternatives.

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

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

The description exhaustively details behaviors beyond annotations: resolution mechanisms (market_match_confidence, low_confidence_match), fan-out logic for various bet types, safety features (illiquid_wide_spread, closed markets), response shapes for analysis and evidence, news fallback handling, and cancellation rule parsing. Annotations are readOnlyHint=true, idempotentHint=true, etc., which are consistent with the research nature; the description adds extensive context.

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

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

The description is long but well-structured: it starts with a clear one-sentence purpose, then uses organized sections (classifiers, fan-out examples, response shapes, etc.). Every sentence provides value, though an agent might find the length daunting. It is front-loaded with the core purpose, making it efficient for quick understanding.

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, no output schema), the description is remarkably complete. It details response shapes (result.market, result.analysis, result.evidence), resolver contract, parent_event extraction, news fields, and safety mechanisms. It covers edge cases like low-confidence matches, closed markets, wide spreads, and cancellation rules. No critical 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 coverage is 100%, so baseline is 3. The description adds meaningful context: explains the market parameter with examples (slug, URL, question), clarifies the depth parameter (quick vs thorough, default thorough) and its implications, and provides guidance on include_raw (summarized vs full payloads, with size estimates). This exceeds simple repetition of schema fields.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on comprehensive data gathering and evidence packet 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 explicitly states usage: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' While it doesn't explicitly exclude scenarios, the context is clear enough to differentiate from siblings. It could be improved by stating when not to use this tool (e.g., for simple price checks), but the guidance is 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 already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. Description adds valuable context: off-calendar fiscal year handling, sorted results by primary metric, and return format (paired data + citation URIs). No contradictions found.

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 comprehensive but somewhat verbose. It includes example queries and detailed explanations, which are helpful but could be tightened without losing clarity.

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

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

Given no output schema, the description adequately explains return format (paired data + citation URIs) and the metrics for each entity type. It covers enough context for agent decision-making.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds extra context: tickers/CIKs for company type, drug names for drug type, and max/min items for values array. 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 clearly states the tool performs side-by-side comparison of 2-5 companies or drugs, with specific example queries. It distinguishes from siblings by advocating its use over sequential single-pack lookups.

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

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

The description explicitly says when to use it (comparison queries) and prefers it over sequential lookups. However, it does not explicitly state when not to use it or list alternatives beyond the 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?

Beyond annotations (readOnly, openWorld, idempotent), description adds parallel execution, findings packet format with gaps[], time estimate (15-60s), and explicit statement that it never invents. No contradiction with annotations.

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

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

Long but well-structured. Critical info (account, alternative) front-loaded. Each sentence adds value, though could be tightened slightly.

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 complex tool thoroughly: data sources, parallel execution, return format, edge cases (empty gaps), time estimate, tier restrictions. No gaps given complexity.

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

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

Schema coverage is 100%. Description adds meaning: explains depth enum values (quick=3, standard=5, thorough=8) and clarifies that question can be broad/multi-part. Slightly above baseline.

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 it performs multi-source structured research in one call, decomposing questions into facets. Distinguishes from open-web search and siblings like ask_pipeworx.

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 (broad/multi-part structured questions), when-not-to-use (single lookups, breaking news), and alternatives (ask_pipeworx). Also covers account requirement and tier limitations.

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, so safety is covered. The description adds useful behavioral context: results are 'ready to call directly, no second schema lookup needed'. No contradiction.

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

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

The description is a single well-structured paragraph with no wasted words. Each sentence earns its place: purpose, usage, output format, strategic advice. Front-loaded with core function.

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 param, no output schema), the description explains the return format sufficiently. It tells the agent what to expect (names, descriptions, schemas) and that results are callable directly. Complete for the task.

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 adds meaning by explaining the 'query' parameter accepts natural language with examples, lists aliases, and gives default/max for 'limit'. It goes beyond the schema's bare 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 verb 'find' and resource 'tools', enumerates many domains, and explicitly distinguishes from siblings by recommending calling it first. It avoids tautology and provides specific usage context.

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

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

The description gives explicit when-to-use ('Use when you need to browse...') and strategic guidance ('Call this FIRST...when you have many tools'). It implies not to use when you already know the tool, but doesn't name specific alternatives among siblings.

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

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false), the description adds key behaviors: it fans out across multiple sources in parallel, returns specific data fields, notes that the patents API may soft-fail (sunset May 2025), and describes news fallback. This is comprehensive.

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

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

The description is informative but somewhat lengthy. It starts with example queries and includes detailed return fields. While each part adds value, it could be more concise. Still, it is well-structured with logical flow.

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

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

Given the complexity (multiple data sources, potential failures, no output schema), the description is quite complete. It lists return fields and notes limitations. However, it could be more explicit about the output structure (e.g., nested objects) to fully compensate for the missing 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?

The input schema has two parameters (type, value) with high coverage. The description adds significant context: type is limited to 'company' currently, value expects ticker or zero-padded CIK, names are not supported, and it references resolve_entity for name resolution. This enriches understanding beyond schema.

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

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

The description clearly states the tool provides a holistic profile of a US public company across multiple sources (SEC, XBRL, USPTO, news, GLEIF). It distinguishes from chaining single-pack lookups and from sibling tools like compare_entities, making the 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 says to prefer this tool over chaining single-pack lookups for holistic views and specifies when not to use—if only a name is provided, use resolve_entity first. This provides clear context and alternatives.

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

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

Annotations already provide destructiveHint=true and readOnlyHint=false, so the description adds context about clearing sensitive data. No contradictions, but limited additional behavioral insight beyond annotations.

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

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

Two efficient sentences with no fluff—first states purpose, second provides usage guidance and sibling context.

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

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

Adequately covers purpose, usage, and sibling relationship for a simple tool with one parameter and no output schema. Missing explicit mention of error behavior or idempotency, but annotations hint at idempotent 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 the key parameter well-described. The description adds little beyond 'by key', which is already conveyed by the schema's 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 action ('Delete a previously stored memory by key') with a specific verb and resource, and distinguishes from siblings by mentioning pairing with 'remember' and 'recall'.

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

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

Explicitly states when to use the tool (context stale, task done, clear sensitive data) and pairs with siblings, but does not explicitly state when not to use or provide alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context by detailing the process: fetching the page, extracting title/description/key links, and outputting in standard llms.txt markdown format. 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 concise, with the main purpose front-loaded. It uses only three sentences to convey purpose, process, and use cases, with no redundant or extraneous information.

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

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

Despite no output schema, the description explains the output format ('single text blob ready to drop at site-root/llms.txt'). The tool is simple (two parameters, no nested objects), and the description covers the entire workflow and intended outputs adequately.

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 does not add significant semantic detail beyond the schema, stating only that the tool takes a URL and optionally a max_links parameter. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's action ('Generate'), the resource ('llms.txt file'), and the scope ('for any URL'). It also mentions the specific use case for AI crawlers, distinguishing it from sibling tools that might not generate this specific file format.

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

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

The description explicitly lists three use cases (client site indexing, personal project drafting, competitor auditing), providing clear context. However, it does not specify when NOT to use the tool or mention alternatives, lacking exclusion criteria.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds valuable context: keyless/offline operation (no external calls), input confidentiality, and MD5/SHA-1 suitability 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?

Two concise sentences: first states purpose and options, second adds privacy and legacy warnings. No filler, 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 complete schema coverage and rich annotations, the description covers algorithm options, defaults, legacy guidance, and privacy. Output format (hex digest) is standard. 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 both parameters with full descriptions. Description adds default algorithm, hex digest output format, and legacy algorithm caveats, providing extra meaning beyond schema.

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

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

Clearly states 'Compute a cryptographic hash (hex digest) of text' and lists all supported algorithms with default. Distinguishes from sibling 'hmac' by noting 'keyless/offline'.

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 (for hashing text), warns about legacy algorithms for checksums only, and notes privacy (input not stored/transmitted). Implicitly tells not to use for keyed hashing.

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

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

Annotations already convey safety (readOnly, idempotent, non-destructive). The description adds useful behavioral context: output is hex, algorithm choices with default, and the keyless/offline 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?

Extremely concise: two sentences delivering the core action, output format, algorithm options, and a special property ('keyless/offline'). No wasted words.

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

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

Given the tool's simplicity, the description together with annotations and schema is fairly complete. Output format is mentioned (hex), but no details about return structure or potential errors. Lacks explicit mention of idempotency, though annotations cover that.

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

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

Input schema has 100% coverage with descriptions for all three parameters, so baseline is 3. The description adds no extra semantic meaning beyond what the schema already provides (algorithm list is duplicated). The 'keyless/offline' note is ambiguous regarding parameters.

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 an HMAC and outputs hex, and lists algorithm options. It is specific about the resource (HMAC) and action (compute), but does not explicitly differentiate from the sibling 'hash' tool, though the unique mention of 'keyless/offline' provides some distinction.

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 offers no guidance on when to use this tool vs. alternatives. It mentions 'keyless/offline' but does not explain what that implies for usage context or provide exclusions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description is not burdened with safety disclosure. The description adds the return fields (id, type, params, etc.) and notes that include_inactive can be used, but does not disclose additional behavioral traits like rate limits or pagination. With strong annotations, a 3 is appropriate.

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

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

Two sentences: first sentence states purpose and return fields, second sentence provides usage guidance. No wasted words, information is front-loaded, and every sentence adds value.

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

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

For a simple tool with one optional boolean parameter and no output schema, the description covers the core functionality, return fields, and usage context. It could be considered complete; however, it does not mention that the list is scoped to the caller (though 'caller's' implies it), and there is no pagination info. Still, well within the appropriate level of detail.

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% (the parameter include_inactive has a description in the schema). The tool description does not add new meaning beyond what the schema provides; it merely mentions 'active subscriptions' which aligns with the default false. 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?

Starts with specific verb and resource ('List the caller's active subscriptions'), details returned fields, and clearly distinguishes from sibling tools like 'subscribe' and 'unsubscribe'. The description is unambiguous and directly states what the tool does.

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

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

Explicitly provides use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. While it doesn't explicitly state when not to use it, the guidance is clear and practical for an AI agent deciding between this and 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 are all false, so the description carries the burden. It adds important behavioral details: rate-limited to 5 per day, free, doesn't count against quota, and that the team reads digests daily. This is beyond what annotations provide.

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

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

The description is concise (one paragraph, ~100 words) and well-structured: purpose first, then usage, format, impact, limits. Every sentence adds value.

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

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

For a feedback tool with 3 parameters (1 required nested) and no output schema, the description covers purpose, when/how to use, constraints, and impact. It is complete enough for an agent to use correctly.

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

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

Schema coverage is 100%, baseline 3. The description adds extra guidance by relating parameters to the feedback scenarios (e.g., type enum examples) and instructs not to paste end-user prompts. This adds 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 it is for sending feedback about bugs, missing features, data gaps, or praise. It distinguishes itself from sibling tools (no other feedback tool exists) and uses specific verbs like 'tell the Pipeworx team' and 'use when'.

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

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

The description explicitly lists when to use the tool: for wrong/stale data, missing tools, or praise. It provides formatting instructions (describe in terms of tools/packs, no pasting prompts) and notes rate limits and cost. Since there is no alternative feedback tool, no exclusions are needed.

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 readOnly, idempotent, non-destructive. Description adds beyond: self-aggregating signal from CF analytics-engine, no PII, caching behavior (5min-1h). No contradictions.

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

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

Description is well-structured: starts with purpose, then three use cases, then technical details. Every sentence adds value, no waste. Appropriate length for the complexity.

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

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

Without output schema, description explains what is returned (top tools, packs, call volume). Caching info is included. Slightly incomplete on exact response format, but sufficient for agent invocation. Annotations cover safety.

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 description for 'window'. Description adds value by explaining semantics: shorter windows for hot right now, longer for steady-state demand. Enhances parameter understanding.

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

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

Description clearly states it returns top tools, packs, and call volume over a recent window. Uses specific verb 'returns' and resource 'trending data'. Distinguishes from sibling tools like ask_pipeworx by being a read-only aggregation of agent calls.

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?

Lists three explicit use cases: discovering hot data sources, confirming canonical tool, aligning use case. Provides clear context for when to use, but does not mention when not to use or alternatives. Still, the guidance is actionable.

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

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

Annotations already indicate readOnly, idempotent, openWorld, and non-destructive. The description adds substantial behavior: algorithms (monotonicity, partition checks), semantic anchor (Jaccard threshold), partition filter (placeholder avoidance), response structure, and fill check (CLOB depth). 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 detailed but well-structured: overall purpose first, then modes, then technical details. Every sentence adds value. It could be slightly more concise in the technical explanation, but it remains focused and 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?

Without an output schema, the description fully explains the response structure (opportunities array, partition_check object, fill check). It covers both modes, filtering criteria, and edge cases (placeholder slugs, empty scans). It also references a related tool for custom sizing, making it complete for a complex tool.

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

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

Schema coverage is 100% with descriptions. The description adds significant meaning: explains the difference between event and topic modes, gives examples (e.g., 'fed-decision-may-2026'), and details what happens when each is used, including the no-arg case. This far exceeds 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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) and a no-arg trending scan, differentiating from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

The description provides explicit guidance: use no args for trending scan, event for a specific market, topic for cross-event scanning. It explains when each mode is appropriate and notes that cross-event catches patterns single-event misses. While it doesn't list explicit 'when not to use' cases, the context is clear and 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, openWorldHint, idempotentHint, and destructiveHint. The description adds rich behavioral context: caching behavior (1h KV cache keyed on knobs), edge calculation methodology (net of slippage, Kelly sizing), and output structure. 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 dense and lengthy, packed with technical details and model explanations. While every sentence adds value, it could be more structured (e.g., bullet points). Its front-loading of purpose helps, but overall it is not concise, potentially overwhelming an agent.

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

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

Given the tool's complexity (9 optional parameters, rich output, no output schema), the description is remarkably complete. It details the response structure (three segments, diagnostics, fed note), explains every key field (edge_pp_net, kelly, liquidity, spread), and covers all parameters. No gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value by explaining interaction of parameters (e.g., min_kelly vs min_partition_leg_kelly, tradeable-edge knobs) and providing example values and rationale (e.g., slippage_pp default of 0.3).

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

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

The description clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, using specific verbs like 'scan' and 'discover'. It explicitly distinguishes from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by detailing three distinct model families and the overall purpose of daily bet discovery.

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

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

The description provides extensive context on when to use the tool (daily bet discovery), how to use it (via tradeable-edge knobs), and what the output contains (segments, diagnostics). It mentions limitations (unreliable Fed signal without paid data) but does not explicitly contrast with specific sibling tools, though the detail given is sufficient for an agent to decide.

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

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

The description extensively details behavioral traits: response structure (tracked, expired, snapshot_dates), limitations (60-day TTL, cache-miss snapshotting), and computation details (decay from daily closes, not intraday). Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, etc., and the description adds substantial context without contradiction.

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

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

The description is lengthy but dense with value. It front-loads the purpose, then details output structure, then limitations. Every sentence serves a purpose. Slight room for brevity but justified by 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?

Despite no output schema, the description fully explains the response fields (tracked[], expired[], snapshot_dates[]) and their subfields. Parameters are covered, and limitations are disclosed. Complete for a telemetry tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining that 'days' is the lookback with default 14 and max 30, and 'window' is the snapshot family with default '1wk'. This context beyond the schema 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: 'Edge persistence and decay telemetry' answering 'how long has this edge existed and is it shrinking?' It distinguishes from sibling tools like polymarket_edges by focusing on time-series analysis rather than raw 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 usage context by contrasting a fresh wide edge and a 3-week-old wide edge, implying when to use this tool (to assess edge freshness). It doesn't explicitly state when not to use it or name alternatives, but the contrast and sibling list make differentiation 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 indicate readOnlyHint=true, idempotentHint=true, etc. The description adds behavioral context: walks order book ladder, returns verdict (clean|degraded|cannot_fill), warns about partial fills and stranded positions. 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 for each mode, uses bold for key terms, and every sentence adds value. It is dense but not overly verbose 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?

No output schema, but the description details all return fields (top_of_book, vwap_fill_price, slippage_pp, etc.) and covers edge cases (thin legs, forced directional risk). It is complete for the tool's complexity.

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

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

Schema coverage is 100% with descriptions. The description adds context beyond schema: explains mode selection (market vs event), default behavior for side and size_usd, and interpretation of size_usd in basket mode.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, and differentiates from 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?

Explicit usage guidance is provided: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' Also explains when not to use (partial basket fills convert arb to unhedged directional position).

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 provides extensive behavioral details beyond annotations: two modes, safety fields (compatibility_warning, temporal_alignment, skipped cross-type/subtype), and clear explanation of when spreads are meaningful. No contradiction with readOnlyHint=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 well-structured with front-loaded purpose, then modes, response details, and safety warnings. Every sentence adds value, though it is slightly verbose 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?

No output schema exists, but the description sufficiently explains the response structure (leg-by-leg prices, spread array, safety fields, temporal alignment, skip counters). It covers key aspects, though could be more explicit about the matching logic.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by explaining the topic values list and how explicit parameters override topic mappings, enhancing understanding beyond schema.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket, with two modes (topic shortcuts and explicit tickers). It distinguishes from sibling tools like polymarket_arbitrage by focusing on the same resolving question across venues.

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

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

The description explains when to use topic mode vs explicit mode, and warns that pre-mapped topics often return compatibility warnings, guiding users on realistic expectations. However, it does not explicitly compare to sibling tools or state when not to use entirely.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds scoping by identifier and the listing behavior, 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?

Two efficient sentences: first defines the action, second provides usage guidance. No redundant or vague language.

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

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

For a simple retrieval tool with one optional parameter and no output schema, the description fully covers purpose, usage, scoping, and relationship to 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 coverage is 100% with clear parameter description. The tool description reinforces the behavior of omitting the key to list all saved keys, adding useful context 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 'Retrieve' and 'list' with clear resources ('value saved via remember', 'all saved keys'). It distinguishes from sibling tools like 'remember' and 'forget' by explicitly naming 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?

The description explains when to use the tool ('to look up context the agent stored earlier') and implies when not to ('without re-deriving it from scratch'). It also clarifies scope and pairing with 'remember' and 'forget'.

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 (read-only, idempotent, non-destructive), the description reveals that mark_read:true flags events as read for subsequent calls, and that the tool returns source, citation_uri, and raw payload. 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 coherent paragraph that fronts the main purpose and then details parameters and behavior. It could be slightly tighter, but no superfluous 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?

Covers purpose, parameters, return content, and alternative access method. Lacks explicit error handling or pagination details, but for a read-only tool with no output schema, it is sufficiently complete.

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

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

Schema descriptions cover 100% of parameters, but the tool description adds value by giving examples (e.g., 'sec_8k') and explaining the behavioral effect of mark_read, enhancing understanding beyond the schema.

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

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

The description clearly states it pulls fired events from the subscription feed, returns recent alerts with source, citation_uri, and raw payload. It distinguishes itself from sibling tools like list_subscriptions by focusing on events rather than subscription management.

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 usage guidance: filter by type and since, use mark_read to manage read state, and notes polling suitability. Mentions alternative REST endpoint for scripts/dashboards, but could more explicitly compare with sibling tools like list_subscriptions.

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

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

Adds significant behavioral details beyond annotations: fans out to three sources, fallback logic, soft-fail for USPTO, and structured output format. Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false) are consistent and complemented by the description.

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 usage examples first, then source details, parameter info, and closure with alternative tool. Although somewhat lengthy, every sentence adds value; no fluff.

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

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

Given the tool's complexity (multiple sources, fallbacks, soft-fail, structured return), the description covers all essential aspects: sources, parameter formats, output shape, and when to use an alternative. Complete enough for an AI agent to understand 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?

Input schema has 100% description coverage, but the description adds extra context: type is limited to 'company', since accepts relative shorthand, and value can be ticker or CIK. This enhances parameter 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 defines the tool's purpose: returning a change feed for a company over a specified window, aggregating data from multiple sources. It distinguishes itself from the sibling tool 'entity_profile' by specifying that static profiles should be handled separately.

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 usage examples ('What's new with X') and instructs when to use an alternative ('Use entity_profile instead when you want the static profile'). Also explains fallback behavior between GDELT and GNews and mentions the USPTO soft-fail due to API sunset.

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

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

Annotations indicate idempotent, non-destructive write. Description adds scoping by identifier, persistence for authenticated users vs 24-hour for anonymous, and pairing with recall/forget. 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?

Concise and well-structured: purpose first, then usage, then behavioral 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?

Given the tool's simplicity (2 required params, no output schema), the description is complete—covering purpose, usage, behavior, persistence, and sibling relationships.

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 for key and value. The description adds naming conventions and usage context, providing value beyond schema.

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

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

The description clearly states the tool saves data for reuse, provides concrete examples of usage (resolved ticker, target address, user preference), and distinguishes it from sibling tools like recall and forget.

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

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

The description gives clear use cases ('when you discover something worth carrying forward') and mentions pairing with recall/forget, but lacks explicit exclusions or when not to use it.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds that it cascades through several lookup endpoints internally, which is useful beyond annotations.

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

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

Two sentences that are well-structured and front-loaded with examples. Every sentence is informative and 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?

No output schema, but the description explains the return format for each type (ticker, CIK, name, citation URI for company; RxCUI, ingredient, brand, citation for drug). Complete enough for an agent.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning by detailing what each type returns and what formats the 'value' parameter accepts (ticker, CIK, name for company; brand/generic for drug).

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 resolves user-spoken names to canonical identifiers, gives concrete examples (ticker, CIK, RxCUI), and distinguishes from sibling tools by noting it replaces 2-3 manual lookups.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID' and provides examples. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds context about the probing and aggregation process, output format (score, confidence, signal density), which is consistent with annotations.

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

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

Three sentences, front-loaded with main action, no wasted words. Every sentence adds value.

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

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

No output schema, but description explains return values (ranked list with score, confidence, signal density per entity). Covers input constraints (2-8 entities) and behavioral details adequately given moderate complexity.

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

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

Schema coverage is 100%, and the description adds meaningful context beyond schema: entities first entry treated as subject, context disambiguates common names, models default to workers-ai, _apiKey needed for anthropic.

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

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

The description clearly states the tool compares AI visibility across multiple entities, probes with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general comparison).

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

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

Explicitly mentions usefulness for competitive AI-marketing audits with an example query. Names the underlying tool ai_visibility_check as a sibling, but does not explicitly state when not to use or list alternatives beyond the example.

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, openWorldHint true. The description adds critical behavioral details: the composite fan-out, partial failures, bundlephobia timeout (5-30s), and graceful degradation with sources_failed. No contradictions with annotations.

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

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

The description is relatively long but every sentence adds value. It is front-loaded with the core purpose and flows logically. While slightly verbose, it remains efficient for 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?

With no output schema, the description fully details the return contents: summary block fields (is_latest, license, published_at, advisory_count, bundle_kb_min, etc.), per-advisory detail, links, and alternative versions. It also covers edge cases like partial failures and timeouts, making it complete for the tool's complexity.

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

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

Schema coverage is 100%, so schema already documents both parameters. The description adds meaningful context: 'package' accepts scoped names like '@types/node', and 'version' defaults to the latest published version when omitted. This goes beyond the schema but is not exhaustive, justifying 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 it is a composite check for evaluating whether to add an npm package, fanning out to deps.dev and bundlephobia. It specifies the verb 'scan dependency' and the resource 'npm package', effectively distinguishing it from sibling tools like deep_research or entity_profile.

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

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

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also delineates scope: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This provides clear when-to-use and when-not-to-use with 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?

Description adds behavioral details beyond annotations: character offsets, similarity scores, BGE-base-en embeddings, 500-char windows, 200K char cap with truncation flag. No contradiction with annotations (readOnly, idempotent).

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

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

Concise and well-structured: first sentence states purpose, followed by usage, pairing, and technical details. Every sentence contributes meaning, and key information is front-loaded.

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

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

Given no output schema, description fully explains return format (passages with offsets and scores), truncation behavior, and pairing with sibling tool. Covers all necessary context for effective use.

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

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

Schema coverage is 100%, but description adds value with examples for query and clarifies text max size. Improves usability 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 it performs semantic search inside a fetched record, with examples of text types (SEC 10-K, article) and natural-language queries. It distinguishes its scope by specifying use cases where the record is too large for the prompt, setting it apart from siblings like ask_pipeworx_grounded.

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

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

Explicitly advises to use when the record is too big for the prompt, and pairs with ask_pipeworx_grounded for grounded answers. Lacks explicit 'when not to use' statements, but the context provides clear guidance.

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

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

Annotations indicate readOnlyHint=false and destructiveHint=false, so the description adds value by detailing account requirements, subscription type behaviors, delivery limits (10/day SMS), and webhook auto-disable after 10 failures. However, it does not clarify idempotentHint=true (e.g., what happens if subscription already exists). Overall, it provides substantial behavioral context beyond annotations.

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

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

The description is well-organized and front-loaded with the main purpose. Every sentence contributes useful information. While a bit lengthy, the complexity of the tool justifies the length. No redundant or wasted sentences.

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 3 parameters (one nested), no output schema, and annotations present, the description covers the creation flow, input structure, return value (subscription id), and important behavioral notes (auth, limits, webhook handling). It provides enough context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant enrichment: for 'type' it lists supported values with brief explanations, for 'params' it gives detailed examples and required fields, and for 'delivery' it explains signing secrets and verification steps. This moves the score above baseline.

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 'Create a proactive monitoring subscription to a live-data event stream' with a specific verb ('Create') and resource ('subscription'). It distinguishes from sibling tools like 'list_subscriptions' and 'unsubscribe' by focusing on creation and detailing return of a new subscription id.

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 (creating a subscription) and lists prerequisites (Pipeworx OAuth account). It explains supported types and delivery channels, but does not explicitly state when not to use it or mention alternatives beyond the sibling list. This is a minor gap.

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

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

Annotations already cover read-only, idempotent, non-destructive, open-world hints. The description adds behavioral details: returns example questions with exact tool/argument shapes from a live catalog, and that topic focuses the output.

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

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

The description is thorough but somewhat long. However, every sentence adds value and it is front-loaded with the purpose. Could be slightly tighter, but still effective.

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

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

Given one optional parameter, no output schema, and rich annotations, the description fully covers what the tool does, how to use it, and what it returns. 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?

The schema has 100% description coverage, but the tool description adds extra meaning by listing example topic values (finance, pharma, etc.) and explaining that omitting topic gives a cross-category spread.

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

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

The description clearly states it is the onboarding entry point for a new agent, returning category-bucketed example questions. It distinguishes itself from siblings like ask_pipeworx and discover_tools by being the first tool to use when unfamiliar with Pipeworx's capabilities.

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

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

Explicitly instructs to use this tool first when the agent does not know what Pipeworx can do, and mentions alternatives (meta-tools). Also provides guidance on passing a topic or omitting it for full spread.

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

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

Annotations already indicate the tool is not read-only, idempotent, and not destructive. The description adds valuable context: ownership enforcement, deactivation vs deletion, and preservation of historical events in recent_alerts. 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 extremely concise with two sentences, front-loading the action and providing key constraints 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?

For a simple one-parameter tool with no output schema, the description, combined with annotations, provides complete context including ownership, idempotency, and side effects.

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

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

The input schema has 100% coverage for the single parameter with a clear description. The tool description does not add additional semantic information beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Cancel' and resource 'subscription'. It distinguishes itself from siblings like 'subscribe' and 'list_subscriptions' by specifying ownership enforcement and deactivation behavior.

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

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

The description provides clear guidance on when to use the tool (to cancel own subscriptions) and mentions that it deactivates rather than deletes, but does not explicitly state 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral details: it returns a verdict with specific types, extracted structured form, actual value with citations, and percent delta. It also notes it replaces multiple sequential calls, providing transparency about its integrated nature.

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

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

The description is moderately concise with a front-loaded structure of examples and usage intent. It contains multiple sentences but each adds value (examples, domain, return types, efficiency). Some redundancy in the examples could be trimmed, but overall it is well-organized.

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

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

Given there is no output schema, the description completely explains the return values: verdict types, extracted structured form, actual value, citation, and percent delta. It also notes tool limitations (v1, company-financial only). This is fully sufficient for the agent to understand inputs, outputs, and boundaries.

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

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

The input schema covers the single parameter 'claim' with 100% description coverage, including examples. The tool description does not add significant new meaning beyond what the schema already provides. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: verifying natural-language claims against authoritative sources. It uses example queries and specifies the domain (company-financial claims). However, it does not explicitly differentiate from sibling tools, so it loses a point for not meeting the 'distinguishes from siblings' criterion.

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

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

The description explicitly says to use it when checking factual correctness of user statements. It implies the domain limitation (company-financial, US public companies) but does not provide explicit exclusions or alternative tool recommendations. Clear context for usage is present.

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