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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, indicating safe read behavior. The description adds valuable behavioral details: the default model is Workers AI Llama-3.3-70b (free), the _apiKey is passed through to Anthropic with direct payment, and the response structure includes per-model fields (score, confidence, signals, raw_response) plus a combined view. 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 (4-5 sentences) and front-loaded with the main action and outcome. Every sentence adds meaningful information: purpose, default model, API key note, return structure, and use cases. No redundant or vague phrasing.

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 covers purpose, return format (per-model scores with details), parameter semantics, and use cases. For a tool with 4 parameters and simple behavior, this provides sufficient context for an agent to select and invoke it correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond schema by explaining the default model, specifying that _apiKey is only needed for Anthropic and is passed directly, and clarifying the role of 'context' for disambiguation. This adds significant value.

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

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

The description clearly states the tool's purpose: to probe LLMs for knowledge about a business/brand/product/topic and score visibility from 0-100 per model. It uses a specific verb ('probe') and identifies the resource (LLMs). It distinguishes itself from siblings like 'scan_competitor_ai_presence' and 'compare_entities' by focusing on visibility scoring across multiple models.

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

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

The description explicitly mentions use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also provides guidance on specifying models and the optional API key for Anthropic. However, it does not explicitly mention when not to use the tool or list alternative tools for similar tasks.

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

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

Annotations already state readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context: routing to 4,774 tools, 1,242 sources, returning stable pipeworx:// URI citations, and being fast. No contradictions. Adds value beyond annotations.

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

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

The description is long (multiple paragraphs) but well-organized with front-loaded key advice and clear sections. It includes many examples and details that are helpful but could be trimmed for conciseness. 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?

For a complex tool with 6 parameters and no output schema, the description adequately covers purpose, usage, and behavior. It explains what the tool returns (structured answer with citations) and provides usage context. The lack of output schema is mitigated by describing return format.

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

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

Schema coverage is 100% with all aliases described. The description does not add much new parameter-level detail beyond what the schema provides. Baseline 3 is appropriate; the description includes examples that illustrate usage but no deeper semantic explanation.

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

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

The description clearly states that the tool routes questions to a vast set of verified sources and returns structured answers with citations. It uses specific verbs ('routes', 'returns') and identifies the resource (question answering via 4,774 tools). It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research.

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

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

Explicitly advises 'PREFER OVER WEB SEARCH' for many question types and provides explicit when-to-use (factual questions) and when-not-to-use (hallucination-resistant or broad questions, with alternatives named). Includes example queries and signals like 'what is', 'look up', etc.

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

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

Beyond annotations (readOnly, idempotent, etc.), the description discloses that it costs an extra LLM call, will refuse if data doesn't directly answer, and returns specific refusal reasons. 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 is well-structured with clear sections: purpose, mechanism, output format, use cases, cost tradeoff. Slightly verbose but each sentence adds value.

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

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

Given the tool's complexity (routing across 4,774 tools), the description covers purpose, usage, mechanism, output structure, failure modes, and alternatives. Without an output schema, it explicitly describes return shape. 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 covers all 6 parameters (all aliases for 'question') with descriptions. The description doesn't add new parameter information beyond what's in the schema, but it doesn't detract either. 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 states the tool is for 'hallucination-resistant answer mode for high-stakes reads,' clearly specifying the verb (answers) and the resource (tool results). It distinguishes from sibling 'ask_pipeworx' by highlighting the extra extraction step and refusal reasons.

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

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

Explicit guidance on when to use ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'), including rationale (extra cost).

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., covering safety. Description adds no extra behavioral context (e.g., character encoding, error handling). Minimal but not contradictory.

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 to the point of being under-specified. The single sentence does not justify its brevity by adding 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 parameter and an output schema, the description fails to mention what the output is or any edge cases (e.g., invalid input). Incomplete.

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

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

Schema coverage is 0% and description provides no explanation of the 's' parameter. Parameter meaning is left entirely to the agent's assumption.

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 is a tautology, restating the name 'Base64 decode' without adding any specificity. It does not differentiate from sibling tools like base64_encode.

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. Sibling tools include base64_encode, but no context for selection 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, and destructiveHint=false, which fully convey the safety and idempotency. The description adds no additional behavioral context beyond what is obvious from the name.

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

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

The description is a single sentence with no wasted words. It is front-loaded with the key action. However, it is too brief and could benefit from a bit more detail without harming conciseness.

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

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

Given the output schema exists and the tool is simple, the description is minimally adequate. However, it does not mention that the output is a base64-encoded string, which would be helpful for an agent unfamiliar with the tool.

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

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

The input schema has 0% parameter description coverage, and the description only implies that the parameter 's' is the string to encode. This is minimal added meaning; a more explicit statement like 'the string to base64 encode' would improve clarity.

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 'Base64 encode' clearly states the verb and resource. It is not a tautology because it adds the verb 'encode' to the name. However, it lacks any distinguishing detail from the sibling 'base64_decode', though the name alone distinguishes encoding vs decoding.

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 vs alternatives, such as when to encode vs decode. There is no mention of prerequisites or context, leaving the agent to infer from the name alone.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds tremendous value by detailing safety checks (low-confidence short-circuit, closed/dead market handling, wide-spread warnings), resolution-rule risk, and fan-out behaviors. This far exceeds what annotations alone provide. No contradictions.

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

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

The description is well-structured with clear sections (RESOLVER CONTRACT, PARENT_EVENT EXTRACTOR, etc.) and front-loads purpose and usage. However, it is lengthy and includes extensive enumerations (e.g., all classifiers, many fan-out examples) that could be condensed. Each sentence adds value, but the overall length slightly impacts conciseness.

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

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

Given the tool's complexity and lack of output schema, the description is exceptionally complete. It covers input formats, fan-out logic per category, response shape details (market, analysis, evidence), resolver confidence handling, parent event extraction, news fallback behavior, safety mechanisms, and resolution-rule risk. All edge cases are addressed, making it comprehensive 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 detailed descriptions for each parameter. The description adds context about default values and recommendations (e.g., 'Default thorough' for depth, 'Default false' for include_raw), but these are already present in the schema. The baseline is appropriate as the description reinforces rather than extends parameter meaning.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data. It specifies input types (slug, URL, question text) and distinguishes its purpose from sibling tools like polymarket_edges by listing explicit use cases ('should I bet on X'). The verb 'Research' plus specific resource 'Polymarket bet' provides clear 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 provides explicit when-to-use guidance ('Use for...') and explains the tool's behavior for various scenarios (low confidence, closed markets, wide spreads). It does not explicitly mention when not to use or list alternatives, but the sibling tool context and detailed fan-out examples implicitly guide appropriate usage. The description could be more concise in this dimension.

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 the annotations (readOnlyHint, idempotentHint), the description details that for companies it pulls specific financial data from SEC EDGAR/XBRL with proper fiscal year handling, and for drugs it pulls adverse-event counts, FDA approvals, and trial counts. It also mentions results are sorted by primary metric and includes citation URIs. This adds meaningful context 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 front-loaded with example queries and the core purpose. While it contains abundant detail, most sentences are necessary for clarity and functionality. It is very informative without being overly verbose, earning a high score for its efficient packing of 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?

Given no output schema, the description adequately explains return values (paired data with citation URIs). It covers both entity types comprehensively, mentioning specific data sources and handling of fiscal years. It also addresses the tool's value proposition (replacing 8–15 sequential lookups), making it complete 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?

Both parameters are fully described in the schema (100% coverage). The description adds significant value by explaining the format for values (e.g., tickers/CIKs for companies, drug names for drugs), giving examples, and clarifying the max/min item count, which enhances understanding beyond the schema's basic 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 performs side-by-side comparison of 2–5 companies or drugs in a single call, providing example queries and distinguishing from sequential lookups. It effectively differentiates from sibling tools like entity_profile by advocating for preference 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 tells when to use this tool (e.g., when user says 'compare X and Y', 'which is bigger') and provides a clear preference over sequential lookups. It does not explicitly state when not to use it, but the schema constraints (2-5 items) implicitly cover that.

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, etc., but description adds no behavioral context (e.g., whether cookies are returned as headers or objects). Fails to enrich beyond structured fields.

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

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

Single sentence is too sparse and fails to convey necessary information. It does not earn its place as it adds no value.

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

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

Despite having an output schema, the description does not explain what the tool returns. Given the tool's simplicity, a minimal but complete description would include what 'cookies' refers to (e.g., HTTP cookies of the current request).

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 zero parameters and 100% schema coverage baseline is 3, but description 'Caller cookies.' adds no meaning beyond the name. Does not clarify what output or operation is implied.

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 'Caller cookies.' is a tautology, restating the name without a verb or clear purpose. It does not specify what the tool does (e.g., retrieve, set, or manage cookies).

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 or how it differs from siblings like 'get' or 'status'. No context or exclusions 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?

The description discloses behavioral traits well beyond annotations, including account requirements, depth-dependent plan restrictions, parallel decomposition, return format (findings packet with evidence, confidence, source, citation, gaps), latency (15-60s), and limitations (empty gaps for topics not in the catalog). This aligns with annotations and adds critical 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 relatively long but well-structured and front-loaded with critical prerequisites (account, alternative tool). Each sentence contributes useful information without redundancy, though it could be slightly more concise. It balances completeness with 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 and lack of output schema, the description is remarkably complete: it covers prerequisites, use cases, mechanism, output format, limitations, and expected latency. No critical information is missing for an AI agent to understand when and how to invoke the tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by specifying the number of facets per depth level (quick=3, standard=5, thorough=8) and clarifying that broad/multi-part questions are acceptable, slightly augmenting 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 function: 'Grounded multi-source research across Pipeworx's 1242 STRUCTURED data sources' and distinguishes it from siblings by noting it's not open-web search, not a single lookup, and not for breaking news. It specifies the verb 'research' and the resource 'structured data sources', making the purpose unmistakable.

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 when-to-use and when-not-to-use guidance: it directs unsigned users to ask_pipeworx, recommends ask_pipeworx for single lookups and breaking news, and explains the account/plan requirements. This clearly differentiates from sibling tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the tool's safety profile is clear. The description adds the core behavior (blocking sleep) but does not mention potential limits like maximum sleep duration or that it is a blocking call.

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, tightly focused sentence with no wasted words. It is front-loaded and immediately actionable.

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 rich annotations and an output schema, the description is nearly complete. It misses only minor details like confirmation that the tool returns after sleeping, but the output schema likely covers 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?

The parameter 'seconds' has no schema description (0% coverage), but the description states 'Sleep N seconds', directly linking the parameter to the action. This compensates well for the lack of schema-level docs.

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 'Sleep N seconds' clearly states the verb (sleep) and resource (time), making the tool's purpose obvious. It is distinct from sibling tools like get or uuid, all of which have different functions.

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 or alternatives is provided. The description lacks context such as 'use for introducing delays' or 'not for timeouts'. It assumes the agent already knows when to invoke 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, idempotentHint, destructiveHint. Description adds that it returns top-N relevant tools with full schemas and examples, ready to call directly, and mentions no side effects. Adds value beyond annotations.

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

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

Two sentences with a list of domains. Key information is front-loaded. The domain list is long but necessary for clarity. Not overly verbose.

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 meta-tool nature with one main param and no output schema, the description fully explains the return format (names, descriptions, schemas, examples) and usage context (call first). No missing critical information.

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

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

Schema coverage is 100% with examples in the input schema itself. Description does not add new parameter meaning beyond what the schema provides (natural language query, aliases). 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 finds tools by describing data or task, listing specific domains (SEC filings, FDA drugs, etc.). It distinguishes from siblings as a meta-tool discovery mechanism, not a domain-specific tool.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for: [list]' and 'Call this FIRST when you have many tools available and want to see the option set'. Provides clear when-to-use and implies when not to.

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 discloses that it fans out across multiple sources in one parallel call, lists the sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), mentions a soft-fall for USPTO PatentsView after May 2025, and specifies input constraints (ticker or CIK, not names). This provides 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 examples and a summary statement ('full cross-source profile in ONE parallel call'). Each sentence provides necessary detail; no fluff. While slightly verbose for a tool description, the complexity of the tool justifies the 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 enumerates the return data: cik, company_name, recent_filings (with URI format), fundamentals (sorted), patents (with sunset note), news, and LEI. It also explains the fan-out behavior and fallback for news. This is sufficiently complete for an agent to understand 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 coverage is 100% and describes both parameters. The description adds examples (ticker 'AAPL', CIK '0000320193') and clarifies that only 'company' type is supported. It also reiterates the name restriction. This adds useful meaning beyond the schema, though the schema already covers the basics.

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

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

The description clearly states the tool's purpose: generating a full cross-source profile of a US public company. It lists specific data points returned (CIK, company name, filings, fundamentals, patents, news, LEI) and distinguishes it from chaining multiple single-source lookups. The resource (US public company) and action (profile generation) are explicit, with clear differentiation from sibling tools like resolve_entity.

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

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

Provides explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also advises using resolve_entity if only a name is available, as names are not supported. This clearly indicates when to use this tool and when to use an alternative.

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

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

Annotations indicate destructiveHint=true, and description confirms deletion. The description adds context about clearing sensitive data, which goes beyond the annotations without contradiction.

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

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

Two concise sentences, no redundancy. Every word contributes to clarity.

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

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

Given the simple nature (1 param, no output schema, annotations present), the description is sufficiently complete. It covers purpose, usage, and context. Could mention permanence of deletion, but not necessary given destructiveHint.

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 'key' parameter. The description does not add new semantics beyond the schema's 'Memory key to delete', so baseline score of 3.

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

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

The description clearly states the tool deletes a memory by key, using specific verb 'Delete' and resource 'memory'. It differentiates from siblings 'remember' and 'recall' which are for storing and retrieving.

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

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

Explicitly states when to use: when context is stale, task done, or clearing sensitive data. Also mentions pairing with remember and recall, providing clear guidance on 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 details the tool's behavior: it fetches the page, extracts title/description/key links, and emits a standard llms.txt markdown blob. This adds value beyond the annotations (which already indicate safety and idempotency) by explaining the extraction process and output 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?

The description is front-loaded with the core purpose and quickly moves to how it works and output. Every sentence serves a clear purpose: what, how, output format, and use cases. No redundant or filler 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 low complexity (2 params, no output schema), the description fully covers purpose, behavior, output format, and usage scenarios. It is complete for an agent to decide when and how to invoke this tool.

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

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

The input schema has 100% description coverage for both parameters (url and max_links), including default and max values. The description does not add any additional parameter-level meaning beyond what the schema already 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 specifies the tool's action (generate a production-ready llms.txt file), the target resource (any URL), and the intended consumers (AI crawlers like ChatGPT, Claude, Perplexity). It clearly distinguishes from siblings like robots_txt or scan_competitor_ai_presence by focusing on the standard llms.txt format.

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

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

The description lists concrete use cases: getting a client's site indexed, drafting for your own project, or auditing competitor presence. While it does not explicitly state when not to use the tool or name alternatives, the provided scenarios give clear guidance on appropriate contexts.

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, and destructiveHint. The description adds behavioral context by stating it sends a GET request to an external service and echoes request details. This complements the annotations without contradiction.

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

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

The description is two sentences long, front-loaded with the action and return value, followed by a usage tip. Every sentence is essential and there is 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?

Given the output schema exists (not shown), the description adequately covers the input parameters and return content. It mentions the key fields in the response, though it could briefly note that it's an external site or mention potential error responses for completeness.

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

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

Schema description coverage is 0% (no descriptions in schema), so the description carries the full burden. It explains that 'query' parameter sends query parameters and 'headers' sends custom headers, tying them to the echoed response. This adds meaning beyond the bare 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 (GET request to httpbin.org) and the resource (httpbin.org), plus lists the returned data (query params, headers, IP, User-Agent). It distinguishes from siblings implicitly by specifying 'GET' and 'httpbin.org', but does not explicitly differentiate from other HTTP tools like 'post' or 'headers'.

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 a usage context ('Use for HTTP debugging'), which is helpful but does not provide when-not-to-use or mention alternatives. With many sibling tools, explicit guidance would improve this score.

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, fully describing the behavioral traits. The description adds no new behavioral info beyond what the annotations provide, which is acceptable but not additive.

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

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

The description is a single sentence with no redundancy. It is front-loaded and earns its place without any 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 (0 parameters, output schema present, rich annotations), the description is sufficient. It could optionally state that it returns HTTP headers, but it's implied. No critical information 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 tool has zero parameters and schema coverage is 100%, so there is no need for description to explain parameters. Baseline for 0 parameters is 4, and the description adds no param info, which 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 'Echo caller's headers' clearly states the tool's purpose: it returns the HTTP headers from the caller's request. The verb 'echo' indicates reflection, and 'headers' specifies what is returned. This is distinct from siblings like 'cookies' or 'ip', which return different data.

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

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

No guidance is provided on when to use this tool versus alternatives. While the tool is self-explanatory, the description does not mention any context or exclusions, such as that it only works for HTTP requests or that it does not require any parameters.

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

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. Description adds that it fetches from httpbin.org, disclosing external dependency and sample nature, which is useful additional 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?

Two sentences with no wasted words. Action and purpose are front-loaded. Perfectly 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 zero parameters, good annotations, and existence of output schema, the description fully covers the tool's purpose, source, and intended use. 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?

No parameters exist, so schema coverage is 100%. Description adds no parameter info, but it is unnecessary; baseline score 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 clearly states the tool fetches a sample HTML response from httpbin.org, with specific verb and resource. It also provides use cases for verifying HTML content-type handling or as a test placeholder, which distinguishes it from sibling tools like json_anything.

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

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

Explicit use cases are given: verify HTML content-type handling or use as placeholder. However, no explicit when-not-to-use or comparison with alternatives, though 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 already indicate idempotent, read-only, non-destructive. Description adds that it returns a URL, but does not explain auth needs 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?

Extremely concise at one sentence, but omits critical parameter guidance, making it under-specified rather than efficiently 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 1-parameter tool with output schema, description is incomplete: does not describe the format parameter's role or acceptable values beyond the schema's examples.

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 does not explain the 'format' parameter or its values, failing to compensate.

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

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

States it returns sample image metadata (a URL), which is a clear verb+resource. However, it does not differentiate from sibling tools that may also return metadata.

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, e.g., when sample vs real data is 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 readOnlyHint, idempotentHint, and openWorldHint, so the description adds value by specifying 'gateway egress', clarifying it returns the external IP rather than local. 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: one phrase that fully communicates the tool's purpose. No wasted words.

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

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

For a simple, parameterless tool with annotations and output schema, the description is adequate. It could be slightly more explicit about the return format, but the output schema covers 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?

No parameters exist, so schema coverage is 100%. The description does not need to add parameter info. Baseline 4 for zero 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 'Caller IP (gateway egress)' clearly states the tool returns the caller's IP address, specifically the gateway egress IP. It distinguishes itself from sibling tools like user_agent or cookies.

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

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

No explicit guidance on when to use this tool versus alternatives. Usage is implied from the name and description, but no mention of alternatives or conditions.

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, covering safety and idempotency. The description adds specific behavioral context (fetching from httpbin.org) without contradicting annotations.

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

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

The description is very concise (two sentences, 20 words) and front-loaded with the primary action. Every word adds value.

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

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

Given the tool's simplicity (no parameters, static endpoint, output schema exists), the description fully covers what the agent needs: source, purpose, and use cases.

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?

No parameters exist, so schema_coverage is 100%. The description does not need to add parameter details; baseline 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 clearly states the action ('Fetch a sample JSON response'), specifies the exact source ('httpbin.org (/json)'), and distinguishes from siblings like html_anything and xml_anything by focusing on JSON and a specific endpoint.

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 use cases ('verify JSON content-type handling' and 'placeholder in tests'). It does not explicitly mention when not to use, but the context of sibling tools implies alternatives for non-JSON needs.

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, destructiveHint=false, and idempotentHint=true. Description adds that it lists active subscriptions (with optional include_inactive) and specifies return fields, which is adequate additional 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?

Description is two sentences: first states purpose and returned fields, second gives usage guidance. No unnecessary words.

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

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

For a simple list tool with no output schema, the description lists all returned fields, covers the single optional parameter via schema, and annotations handle behavioral safety. Complete and sufficient.

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

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

Schema coverage is 100%, and the single parameter 'include_inactive' has a clear description in the schema. The tool description does not add extra parameter details, meeting the 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 clearly states 'List the caller's active subscriptions' and lists returned fields, providing a specific verb and resource. It distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Description explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', giving specific contexts for tool use. While it doesn't mention when not to use it, no direct alternative exists 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?

The annotations provide no behavioral hints, so the description carries the full burden. It discloses that the tool is free, does not count against tool-call quota, and is rate-limited to 5 per identifier per day. It also explains how the feedback is used (daily digests, roadmap influence). 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 a single, well-structured paragraph that front-loads the core purpose and then provides usage conditions in a logical order. Every sentence adds essential information, 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?

For a simple feedback tool with no output schema, the description covers all necessary context: what the tool does, when to use it, how to structure feedback, rate limits, and that it is free. 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 coverage is 100%, with detailed descriptions for each parameter. The description adds extra value by advising users to describe issues in terms of Pipeworx tools/packs and not to paste prompts, which goes beyond the schema's basic explanations.

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: to send feedback to the Pipeworx team about bugs, feature requests, data gaps, or praise. It uses specific verbs and resource identification, and distinguishes itself from sibling tools like ask_pipeworx by being a feedback rather than a query tool.

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 when to use each feedback type (bug, feature, data_gap, praise) and provides a clear 'when-not' by instructing users not to paste the end-user's prompt. It also mentions rate limits and that the tool is free, offering comprehensive usage 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 indicate readOnly, openWorld, and idempotent behavior. The description adds valuable context: data is self-aggregated from CF analytics-engine, excludes PII, and is cached 5min–1h. No contradictions.

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

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

The description is two well-structured paragraphs with no filler. The first sentence gives the key outputs, and the second expands with use cases and technical 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 the simple input schema (1 optional parameter, no output schema), the description adequately covers purpose, usage, caching, and data source. The return structure is described in text, though explicit format details would improve completeness.

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

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

The schema already describes the single parameter with an enum. The description adds meaning by explaining the trade-off between short and long windows (hot vs. steady-state demand), which aids selection.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a window. It uses specific verbs and distinguishes from sibling tools like 'ask_pipeworx' and 'discover_tools', 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 lists three explicit use cases and explains the trade-off between window sizes. It does not explicitly state when not to use the tool, but the provided context is sufficient for most agents.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds significant behavioral context: scanning logic, semantic anchor (Jaccard similarity ≥0.30), partition filter (drops placeholder slugs), and fill check (arbitrage.fill_check) that prevents trading if edge is only in last trade. 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 long but well-structured: high-level purpose, then mode explanations, then details on semantic anchor, partition filter, response format, and fill check. Every sentence adds necessary context; minor redundancy could be trimmed but overall efficient 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?

Given the complex arbitrage detection logic and no output schema, the description explains the response structure (opportunities[] with fields, partition_check in event mode) and the fill check. It covers edge cases (Jaccard similarity, placeholder filter). Missing explicit error conditions or rate limits, but sufficient for an agent to use effectively.

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

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

Schema description coverage is 100%. The description adds value by providing examples of event slugs (e.g., 'fed-decision-may-2026') and explaining that full URLs are accepted. For topic, it gives example seed questions and describes the cross-event scanning behavior. This enriches the parameter understanding beyond the schema.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between three modes (no args, event, topic) with specific verbs and resources. It differentiates itself from sibling tools like polymarket_edges and polymarket_fill_risk by focusing on arbitrage detection.

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

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

The description explicitly explains when to use each mode: no args for trending scan, event for a specific market, topic for cross-event scanning. It recommends event for specific markets and provides examples. However, it does not explicitly state when NOT to use this tool versus alternatives, which would strengthen the guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. Description adds extensive behavioral context: caching (1h at KV level), model family details, tradeable-edge knobs, diagnostics, and explanations of why Fed bets are excluded. 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 extremely verbose, containing detailed model family explanations, parameter knobs, and output structure. While front-loaded with the main purpose, it lacks conciseness and could be significantly shortened for an AI agent's 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 complexity (9 parameters, no output schema), the description thoroughly explains the output structure (by_segment, fed_candidates, _diagnostics), content of each opportunity (edge_pp_net, kelly_fraction, etc.), and covers edge cases. It is highly 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 description coverage is 100%, so baseline is 3. The description adds value by explaining how parameters like min_kelly and min_partition_leg_kelly interact with different segments, and provides context for their usage beyond the schema definitions.

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

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

Description clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with price, specifically for 'what should I bet on today'. The verb 'scan' and resource 'top Polymarket markets' are explicit. Sibling tools like 'polymarket_arbitrage' suggest a different focus, so this tool distinguishes itself.

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

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

The description explicitly says 'Built for "what should I bet on today"' and that agents can discover opportunities without paging hundreds of markets. It does not explicitly state when not to use it or name alternative tools, but the context of siblings implies alternatives exist. The guidance is clear but lacks 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 readOnlyHint and destructiveHint. The description adds important behavioral details: 60-day TTL, snapshot gaps, and decay computed from daily closes, not intraday.

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 dense paragraph that could be more concisely structured (e.g., bullet points). It contains all necessary info but lacks front-loading prioritization.

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 structure (tracked[], expired[], snapshot_dates[]) and limitations (TTL, snapshot gaps). 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 coverage is 100% (both parameters described). The description adds context (e.g., 'snapshot family' for window, defaults, constraints) but does not drastically extend schema 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 explicitly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots' and answers a clear question. It differentiates from sibling tools like polymarket_edges by focusing on persistence.

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 explains parameters and defaults, and implicitly suggests usage for tracking edge persistence rather than other analytics. However, it lacks explicit 'when to use' vs 'when not to use' or alternative tool references.

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=true, idempotentHint=true, destructiveHint=false. Description adds behavioral details: walks order book, returns fill feasibility and warnings about partial fills converting arb to directional position. 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 dense and comprehensive but relatively long, mixing mode details and warnings. Could be more structured with bullet points, but front-loads purpose. Slightly verbose 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?

No output schema, but description fully enumerates return fields for both modes (top_of_book, vwap, slippage, shares_filled, verdict, etc.) and explains forced_directional_risk. Sufficient for agent to understand what will be returned.

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

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

Schema coverage is 100% with descriptions. Description adds value by explaining interpretation of size_usd (spend vs settlement notional) and side defaults for basket mode, going beyond schema basics.

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 identifies tool as realizable-vs-theoretical edge check using live order-book depth, differentiating single-market and basket modes. 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?

Provides explicit when-to-use guidance: before executing sell/buy-every-leg signals or trades above $500. Explains why (theoretical overround not capturable, partial fills cause directional risk) and implies when not to use.

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

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

The description extensively discloses behavior beyond the annotations: it explains the two modes, the response structure (leg-by-leg prices, spread array), safety fields (compatibility_warning, temporal_alignment, skipped counters), and provides real-trade caveats. No conflict with annotations (readOnlyHint, etc.).

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, but the description is quite long. It is front-loaded with the purpose and modes, then details safety fields. Could be slightly more concise without losing essential information.

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

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

For a tool with no output schema and moderate complexity (two modes, multiple safety fields), the description is comprehensive. It covers the response fields, temporal alignment, skipped leg reasons, and limitations (pre-mapped ≠ tradeable).

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 value by listing the topic enum values, providing examples, and explaining how explicit tickers override topic-mapped sides. It also clarifies that topic shortcuts are pre-mapped and may not yield tradeable spreads.

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 spread between Kalshi and Polymarket for the same resolving question, with two modes (topic shortcuts and explicit tickers). This is specific and distinguishes it from sibling tools like polymarket_arbitrage (which focuses on a single venue's internal markets).

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

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

The description explains when to use the tool (to compare prices across venues), details both modes, and warns that pre-mapped topics often yield compatibility warnings. It implicitly advises caution but does not explicitly mention alternatives 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 declare readOnlyHint, idempotentHint, and non-destructive behavior, which align with a test service. The description adds that the tool echoes the request, providing additional 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 a single, well-structured sentence that front-loads the key action and result. Every word adds value.

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

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

Given the existence of an output schema (not shown), the description adequately covers the tool's behavior and return value. It could mention that this is for testing purposes, but it is nearly complete.

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

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

Schema coverage is 0%, so the description must compensate. It mentions 'optional body and custom headers' but does not explain the content_type parameter or provide details on parameter types and formats. The description adds minimal value over 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 sends a POST request to httpbin.org with optional body and headers, and returns the echoed request. This clearly distinguishes it from sibling tools like get or headers.

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 what the tool does but does not explicitly state when to use it versus alternatives or when not to use it. The context of sending POST requests is implied but no exclusions are 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 provide readOnlyHint etc. Description adds scope detail (scoped to identifier) and confirms retrieval behavior. No contradiction, and adds value beyond annotations.

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

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

Three concise sentences with no redundancy. First sentence states core function, second gives examples, third adds scope and pairing. Every sentence earns its place.

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

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

For a simple one-param tool with no output schema, the description is complete: purpose, usage, parameter semantics, scope, and relationships to siblings are all covered.

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

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

Schema covers the key parameter 100%, but description adds crucial behavior: omitting key lists all keys. This clarifies usage beyond the schema's property 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?

Description clearly states the tool retrieves saved values or lists keys, with specific verb (retrieve/list) and resource (saved values/keys). It distinguishes from siblings 'remember' and 'forget' by naming them explicitly.

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: to look up previously stored context without re-deriving. Pairs with remember and forget, implying context. Does not explicitly state when not to use, but use case is clear.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds key behavioral detail: mark_read:true flags events as read, affecting subsequent calls. No contradictions.

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

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

Three sentences: purpose, field details and filtering, alternative access. Each sentence is necessary and front-loaded. 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?

Covers all aspects: return fields, filtering, mark_read side effect, polling suitability. With no output schema, describing returned fields is essential. The alternative endpoint is helpful for integration.

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

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

Schema covers 100% of parameters with descriptions. Description adds context: example type 'sec_8k', clarifies 'since' as ISO timestamp, explains effect of mark_read and unread_only. Adds 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?

Clearly states 'Pull fired events from your subscription feed' with specific verbs and resources. Distinguishes from sibling tools like list_subscriptions by focusing on recent alerts and mentioning the feed.

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 polling and an alternative endpoint for scripts/dashboards. Could explicitly state when not to use or compare with list_subscriptions, 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 already mark the tool as readOnly, idempotent, and non-destructive. The description adds significant behavioral context: it fans out to multiple sources (SEC EDGAR, GDELT, GNews, USPTO), explains fallback logic (GDELT preferred, GNews on rate limits/5xx), and notes that USPTO may soft-fail due to API sunset. This exceeds the annotation 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 paragraph that packs extensive information without redundancy. It is front-loaded with user-facing examples ('What's new with X') and quickly states the tool's core capability. While dense, every sentence adds value; a bullet list might improve scannability but the current structure is efficient.

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

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

Despite lacking an output schema, the description clearly specifies the return structure: 'Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' It also covers edge cases like fallback behavior and soft-fail for USPTO. Given only three parameters and no nested objects, the description feels complete.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by providing concrete examples for 'since' (ISO date vs. relative shorthand like '7d', '30d', '3m', '1y') and a recommended default ('Use "30d" or "1m" for typical monitoring'). For 'value', it clarifies that both ticker and zero-padded CIK are accepted.

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 numerous concrete query examples ('What's new with X', 'latest on Y') and immediately states the core function: 'change feed for a company in the last N days/weeks/months in ONE parallel call'. It distinguishes itself from the sibling tool 'entity_profile' by recommending that for static profiles, thereby clarifying 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 provides a conditional alternative: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It also gives typical parameter values (e.g., 'Use "30d" or "1m" for typical monitoring') and explains fallback behavior (GDELT to GNews) to guide 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 indicate idempotent and non-destructive write. Description adds persistence duration (24h for anonymous, persistent for authenticated) and scoping by identifier, which are valuable 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?

Five sentences, first states purpose, then usage, scope, persistence, and siblings. No redundancy, 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?

Adequate for a simple tool with 2 parameters and no output schema. Covers purpose, usage, persistence, and related tools. Could explicitly mention overwrite behavior, but still complete.

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

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

Schema coverage is 100% with descriptions. Description reinforces key-value pattern but does not add new semantic details beyond what schema 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 saves data for reuse later, specifies the resource as key-value pairs, and distinguishes from siblings (recall, forget) by mentioning pairing.

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: when discovering something worth carrying forward. Also notes scope and persistence details. Missing explicit when-not-to-use but context implies not for ephemeral data.

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 context beyond annotations, stating that 'each call cascades through several lookup endpoints internally' and that it replaces multiple manual lookups. No contradictions with annotations.

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

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

The description is front-loaded with examples and key purpose. All sentences are informative, but some details (e.g., internal lookup cascades) could be slightly more succinct. It earns a 4 for still being efficient without unnecessary redundancy.

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

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

Given no output schema, the description fully compensates by explaining the return structure for each entity type (returns ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand, citation for drug). It also specifies input formats and disambiguation for company. This leaves no ambiguity about what the tool produces.

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

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

Schema coverage is 100%, but the description significantly enriches parameter meaning. For the 'type' parameter, it details what each enum value returns (e.g., company returns ticker, CIK, company_name, citation URI). For 'value', it provides concrete examples and accepted formats (ticker, CIK, name for company; brand/generic for drug). This goes well beyond the schema descriptions.

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

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

The description starts with concrete example queries ('What's the ticker for...') and explicitly states the core purpose: resolving a user-spoken name to a canonical identifier. It distinguishes itself from sibling tools by detailing the exact return types for 'company' and 'drug', which no sibling tool duplicates.

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 directive: 'Use FIRST whenever you have a name but need an ID.' This tells the agent when to invoke the tool. It also implies it replaces 2-3 manual lookups. However, it lacks explicit guidance on when not to use it or mention of alternatives, slightly reducing the score.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) already disclose safety and behavior. Description adds specific source URL and purpose, providing additional 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?

Two sentences, no wasted words. Purpose is front-loaded: first sentence states action and source, second sentence gives use cases.

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?

Complete for a tool with no parameters. Includes source URL and intended use cases. Output schema exists but description doesn't need to explain returns.

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

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

No parameters to describe. Schema coverage is 100% by default. Description does not need to add param info, and the purpose is clearly communicated.

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 'Fetch a sample robots.txt from httpbin.org (/robots.txt)', which is a specific verb and resource. It distinguishes itself from generic fetch tools like 'get' by specifying the exact URL and sample 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?

Explicitly says 'Use to test robots.txt parsing or as a content-type placeholder.' This provides clear usage context. Lacks explicit when-not-to-use, but the intent is well-understood.

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

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

Annotations already declare readOnly, openWorld, idempotent, and not destructive. The description adds valuable behavioral info: it internally uses ai_visibility_check, aggregates results, treats first entity as subject, and returns ranked list with score, confidence, signal density. 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?

Three sentences, front-loaded with purpose, no fluff. Every sentence adds value: function, method, use case. Highly concise and 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?

Given 4 parameters (1 required) and no output schema, the description adequately explains the output format (ranked list with score, confidence, signal density). It also covers the array length constraint and first-entity treatment. Complete for a comparison tool with rich annotations.

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

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

Schema coverage is 100% with detailed descriptions for each parameter, including that entities first entry is subject. The main description does not add significant new meaning beyond the schema, so baseline of 3 is appropriate.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single probe) and compare_entities (general comparison) by specifying AI visibility focus and output details.

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

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

The description provides context for competitive AI-marketing audits and poses a concrete question. It implicitly suggests using the sibling ai_visibility_check for single entities, but does not explicitly state when not to use this tool or provide alternative names.

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 read-only, idempotent, non-destructive nature. Description adds critical behavioral details: partial failure handling (bundlephobia may timeout, sources_failed listed), graceful degradation, and measurement latency (5-30s). Contradiction is 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 concise yet comprehensive. Front-loaded with core purpose, then efficiently covers ecosystem, timing, and output format. 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 absence of an output schema, the description fully explains the return value structure (summary block fields, per-advisory detail, links, alternative versions) and edge cases (timeout, partial failures). This makes the tool's behavior predictable and complete.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minimal param detail beyond schema: mentions that scoped packages are accepted and that version defaults to latest. This is adequate but not extensive; extra context about return value shape compensates slightly, but is not about 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?

Description clearly defines the tool as a composite check for npm packages, combining deps.dev and bundlephobia data. It distinguishes from siblings by specifying the ecosystem and multi-source aggregation, leaving no ambiguity about what resource is being scanned.

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

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

Explicitly states when to use: 'when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also provides exclusion guidance for non-npm ecosystems, directing to deps.dev:version directly, which helps avoid misuse.

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

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

Discloses technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flag. Output includes character offsets and similarity scores. No contradiction with annotations (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?

Description is front-loaded with core purpose, then usage, then technical details. Every sentence adds value; no wasted words. Appropriate length for a moderately complex 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?

Even without output schema, the description sufficiently explains output format (passages with offsets and scores) and input constraints (200K char limit with truncation). Covers edge cases and pairing context.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value with concrete examples for query and text, explains default and range for limit, enhancing understanding beyond schema descriptions.

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

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

The description clearly states the tool does semantic search inside a fetched record, providing concrete examples (SEC 10-K, article) and detailing output (top-N passages with offsets and scores). It distinguishes from siblings by mentioning pairing with 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 tells when to use: when the record is too big for the prompt. Mentions pairing with ask_pipeworx_grounded for grounding over relevant passages, providing clear guidance on 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 and idempotentHint true, so the description does not contradict. However, it adds no behavioral context beyond stating the action. For a simple tool returning status, this is minimal but not harmful.

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

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

The description is extremely concise (3 words), sacrificing necessary detail. While brevity is valued, here it undermines clarity and usefulness.

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 only one parameter and an output schema (not shown), the description fails to explain what the tool returns or how to use it. The agent would need to guess the function's behavior from examples in the 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 no description coverage for the required 'code' parameter. The description 'Return given HTTP status' only implies the parameter is a status code but does not explain its role, format, or acceptable values. The schema examples provide some clues but are not part of the 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 'Return given HTTP status' clearly states the verb (Return) and resource (HTTP status). It distinguishes from siblings like get or post, which are for actual requests, while status simulates a response. However, it could be more specific about what 'returning a status' entails.

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 like get or post. The description does not specify context or exclusions, leaving the agent to infer usage from the tool name alone.

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

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

The description discloses behavioral traits beyond annotations: it explains that a new subscription is created (readOnlyHint=false), is idempotent (idempotentHint=true), and does not destroy data (destructiveHint=false). It also details delivery channel behaviors, such as SMS caps (10/day) and webhook auto-disable after 10 failures, which are not covered by 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 the core purpose, followed by requirements, types, and delivery details. It is slightly lengthy but well-organized with clear sections. Each sentence adds relevant information, though minor trimming could improve conciseness.

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

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

Given the tool's complexity (nested parameters, no output schema), the description is complete: it covers return value (subscription ID), all supported types with examples, authentication requirements, and delivery channel rules. No crucial aspect 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?

With 100% schema coverage, the description still adds significant value: it provides rich examples for each subscription type, explains required vs optional fields in params, and adds operational details for delivery (e.g., phone verification, webhook HMAC signing). This goes well beyond the schema's basic 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 creates a proactive monitoring subscription and returns a subscription ID. It specifies the verb 'Create' and the resource 'subscription to a live-data event stream', distinguishing it from sibling tools like list_subscriptions, unsubscribe, and recent_alerts.

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 usage context: requires a Pipeworx OAuth account (anonymous and BYO cannot persist), lists supported subscription types with examples, and mentions delivery channel limits. It implicitly guides when to use (e.g., for persistent subscriptions) but does not explicitly contrast with alternative 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, openWorldHint, idempotentHint as true, and destructiveHint false. Description adds value beyond annotations: it explains the return format (category-bucketed example questions with exact tool+argument shape drawn from live catalog) and that it's a safe read-only operation. 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?

Description is 3-4 sentences, front-loaded with common user queries. Each sentence adds value: what it does, what it returns, how to call, and when to use. 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 (1 optional param, no output schema), the description is complete. It explains what it returns, how to call it, and its purpose. No missing information for an agent to use it correctly.

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

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

Schema coverage is 100%. Parameter 'topic' is described with optional focus areas. Description adds context: 'Call with no arguments for the full spread, or pass topic to focus'. This is additional useful guidance 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?

Description clearly states it's an onboarding entry point that suggests category-bucketed example questions, specifying the verb 'suggest' and resource 'questions'. It distinguishes from siblings like ask_pipeworx, entity_profile, etc. by being the first tool to use when the agent doesn't know what Pipeworx can do.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Provides guidance on when to call with or without topic argument. No explicit when-not, 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?

Discloses soft-delete behavior (deactivated not deleted) and references historical events, supplementing 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, front-loaded with purpose, 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?

Adequate for a simple tool; mentions outcome and side effect. Could detail return value, but overall complete.

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

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

Schema coverage is 100%, description adds 'by id' and 'uuid' but adds little 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?

Clear verb+resource: 'Cancel a subscription by id.' Distinguishes from siblings like subscribe and list_subscriptions.

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

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

Specifies ownership enforcement but does not explicitly compare to alternatives or state when not to use.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description does not need to repeat these. The description adds no further behavioral context, but it does not contradict annotations. A score of 3 is appropriate as the description is neutral.

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 (two words) and front-loaded. Every word is necessary; 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 (no parameters, annotations covering safety, and presence of an output schema), the description is minimally but adequately complete. It could clarify what 'caller' means, but the output schema likely covers the return value sufficiently.

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

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

There are no parameters (0 params), so the schema coverage is 100%. According to guidelines, 0 params = baseline 4. The description does not need to add parameter information.

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 'Caller User-Agent' clearly states the tool retrieves the caller's user agent. It uses a specific verb-resource pair ('get user agent') and distinguishes from siblings which are unrelated (e.g., search, encode, 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?

No guidance on when to use this tool versus alternatives. While the tool is simple, the description does not provide any context for usage, leaving the agent to infer from the name alone.

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

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

Annotations already provide safety and idempotency hints. Description merely says 'Random UUID,' adding no behavioral context beyond what structured fields convey.

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 2 words, front-loaded, no wasted text.

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?

Output schema presumably defines return format, so description doesn't need to. Minimal but adequate for such a simple tool with zero parameters.

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

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

No parameters exist; description isn't expected to add param info. Baseline 4 appropriate as schema covers everything.

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

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

Clearly states the tool generates a random UUID. Could be more precise about version (e.g., v4), but it's 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?

No guidance on when to use this tool vs alternatives. Among many sibling tools, no mention of use cases or exclusions.

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

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

The description adds significant value beyond annotations by detailing the return format (verdict, extracted form, actual value with citation, percent delta), its efficiency (replaces multiple calls), and its version limitations (v1 supports only company-financial claims). Annotations already indicate readOnly and idempotent, and the description complements them.

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

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

The description is front-loaded with key phrases and logically structured, but includes some redundancy with multiple example triggers. It is slightly verbose but still effective and well-organized.

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

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

Given the single parameter and no output schema, the description comprehensively explains the tool's behavior, output structure, supported domain, and efficiency gains. It leaves no significant gaps for an agent to understand the tool's capabilities.

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 'claim' has a schema description that explains its natural-language nature. The description provides concrete examples and clarifies the expected format, adding meaning beyond the schema's basic type definition.

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

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

The description clearly defines the tool as a natural-language claim verification system against authoritative sources, using specific verbs like 'validate' and 'verify'. It lists example trigger phrases, distinguishing it from sibling tools that do not perform fact-checking.

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 when to use: 'whenever the agent needs to check whether something a user said is factually correct.' It also specifies the supported scope (company-financial claims), but lacks explicit guidance on when not to use or alternatives for other domains.

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?

Given that annotations already provide readOnly, idempotent, and non-destructive hints, the description adds value by specifying the source URL (httpbin.org/xml) and purpose. No further behavioral details are needed for this simple tool.

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

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

The description is two sentences, direct and front-loaded with the action and resource. Every word is necessary and no extraneous information.

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

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

For a simple tool with no parameters and an output schema, the description is complete: it states the source, purpose, and typical use cases, leaving no ambiguity.

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?

No parameters exist, so schema coverage is 100%. The description does not need to add parameter semantics; it correctly focuses on tool functionality.

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 fetches a sample XML response from httpbin.org (/xml), with specific use cases for verifying XML content-type handling or as a test placeholder. It distinguishes itself from sibling tools like json_anything and html_anything.

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 (testing XML handling, placeholder), but does not explicitly exclude alternatives or mention when not to use it, which would improve the score.

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