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

Annotations already declare `readOnlyHint: true`, `openWorldHint: true`, `idempotentHint: true`, and `destructiveHint: false`. The description adds behavioral context beyond these: it explains the output format (per-model `{score, confidence, signals, raw_response}` plus a combined view), notes the cost implications of using `_apiKey`, and clarifies that the default model is free. 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 paragraph of four sentences. It front-loads the core purpose and output, then explains parameters and use cases. It is efficient but could be slightly more concise by merging some clauses. However, it earns its length with no redundant information.

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

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

Given the high schema coverage (100%), presence of annotations, and no output schema, the description fully explains the tool's behavior. It covers the input parameters, default model, optional Anthropic probing, return format (per-model and combined), and use cases. It is complete for an agent to decide 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 description coverage is 100%, so the baseline is 3. The description adds significant meaning: it explains that `models` can include 'workers-ai' (default) and 'anthropic', that `_apiKey` is required only when probing Anthropic and is passed directly to api.anthropic.com, and that `context` helps disambiguate common names. This adds value 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 clearly states the tool's purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It uses specific verbs ('probe', 'score') and identifies the resource ('LLMs'). The description distinguishes the tool from siblings by mentioning its focus on AI-marketing audits and brand checks, which are not covered by sibling tools.

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

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

The description provides clear usage guidance: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains the default model (Workers AI Llama-3.3-70b) and the optional use of `_apiKey` to probe Anthropic (with a note that the user pays directly). It does not explicitly state when not to use the tool or mention alternatives, but the context is clear enough.

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

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

Annotations already convey read-only, idempotent, open-world. Description adds value by mentioning structured answers with citation URIs and routing to 3,775 tools. No contradictions. Could mention potential unavailability if no source matches.

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

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

Front-loaded with purpose and usage guidelines. Concise overall, though examples could be more compact. Structure is clear.

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

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

Given no output schema, description fully explains return format (structured answer with citation URIs). Covers parameter semantics, use cases, and scope. Complete for a query tool with simple input.

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 documented. Description restates aliases and provides examples, but adds little beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool routes factual questions to authoritative sources. It specifies the verb 'ask' and resource 'Pipeworx', and distinguishes itself from web search with explicit preference. Examples further solidify purpose.

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

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

Provides explicit guidance: 'PREFER OVER WEB SEARCH' and lists cues like 'what is', 'look up', etc. However, it does not directly contrast with sibling tools like deep_research or entity_profile, which might be more suitable in specific cases.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant context: extracts answer using only tool result, returns structured response with evidence and explicit 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 concise (3-4 sentences), well-structured with front-loaded purpose, and every sentence adds value without redundancy.

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

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

Despite no output schema, the description fully details the return structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and possible refusal reasons. It provides sufficient context for the agent to understand behavior and outcomes.

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

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

Schema description coverage is 100%, so the schema already documents the parameters and aliases. The description mentions the question is in natural language, but adds minimal extra meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description explicitly states it is a 'hallucination-resistant answer mode for high-stakes reads' and details the process of extracting answers only from tool results. It distinguishes itself from the sibling tool 'ask_pipeworx' by noting the extra LLM call and use case differences.

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

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

Provides explicit guidance on when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts...' and contrasts with ask_pipeworx: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.'

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

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

The description goes far beyond annotations (readOnlyHint, idempotentHint, non-destructive) by detailing internal behavior: resolver contract with confidence scores, parent event extraction, news fallback mechanisms, safety short-circuits for low-confidence or closed markets, illiquid spread warnings, and resolution-rule risk. 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 very long and verbose, spanning multiple paragraphs with many examples and edge cases. While well-structured with section headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.), it could be more concise by omitting some examples or compressing explanation. The front-loading is good (purpose stated immediately), but every sentence is not strictly necessary.

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 (resolver, fan-out, multiple data sources, safety checks) and the absence of an output schema, the description comprehensively covers all aspects: response shapes, resolver contract, parent event extractor, news fields, safety features, and resolution-rule risk. It leaves no major gaps for an AI agent to understand what the tool returns and how it behaves under various conditions.

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 already describes all three parameters (market, depth, include_raw), but the description adds significant value beyond that: it gives concrete examples of market input (slug, URL, question text), explains the depth enum and default, and clarifies when to use include_raw (summarized vs full payloads with size estimates). Schema coverage is 100%, and the description richly supplements it.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies inputs (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). The provided classifiers and fan-out examples distinguish it from sibling tools like ask_pipeworx or polymarket_edges, making its unique role unambiguous.

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

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

The description explicitly lists use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides numerous fan-out examples for different bet types (crypto, fed, geopolitical, sports, etc.). However, it does not directly compare to sibling tools or explicitly state when not to use this tool, leaving some ambiguity.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant context: pulls latest data, handles off-calendar fiscal years, returns sorted, and includes citation URIs. No contradiction.

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

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

The description is somewhat lengthy but front-loaded with common query patterns and immediate purpose. Every sentence adds value; could be trimmed slightly but remains effective.

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

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

Given the simple input schema (2 parameters, no nested objects), the description fully covers both entity types, data sources, sorting behavior, and return format (paired data + URIs). No gaps.

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

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

Schema coverage is 100%, but description adds meaning beyond the enum and array definitions: for 'type' it explains data sources (SEC EDGAR/XBRL for company, FAERS/drug data), and for 'values' it specifies format (tickers/CIKs vs. drug names) and behavior (sorted). This adds 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 it performs side-by-side comparison of 2-5 companies or drugs in one call, specifying data sources (SEC 10-K for companies, FAERS counts for drugs) and sorting by primary metric. It is specific and distinguishes from sequential lookups.

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

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups', providing clear context for when to use. It does not explicitly list alternatives but implies entity_profile for single lookups. Overall helpful 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?

Description adds significant behavioral context beyond annotations: explains evidence, confidence, sources, citations, explicit gaps for unknowns, and expected duration (15-60s). 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?

Front-loaded with key action, but slightly lengthy; however all sentences add value and the structure is logical.

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

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

Comprehensive coverage of purpose, process, usage, prerequisites, and output format despite no output schema. Leaves no significant gaps.

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

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

Schema coverage is 100%, and description elaborates on depth values (quick=3, standard=5, thorough=8 paid) and question allowing broad/multi-part queries, adding value beyond schema.

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

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

The description clearly states the tool's purpose: grounded multi-source research in one call, with specific verbs like 'decomposes' and 'routes,' and distinguishes from siblings like ask_pipeworx for single 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?

Explicit guidance on when to use (broad/multi-part questions), when not (single lookups use ask_pipeworx), prerequisites (Pipeworx account), and depth tiers including paid plan requirement.

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 destructiveHint. The description adds behavioral details: results include full input schemas with curated examples, ready to call directly. No contradictions. Could mention rate limits or pagination, but sufficient.

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

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

Description is concise and front-loaded: first sentence states purpose. Each sentence adds value (usage guidance, return format, call strategy). 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 no output schema, the description fully explains what the tool returns: top-N most relevant tools with names, descriptions, and full input schemas with curated examples, each ready to call. Complete for a search/discovery 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%. Description adds context for aliases (task, q, description, search) and limit parameters (default 20, max 50). It also explains what the tool returns (names, descriptions, schemas with examples), adding value beyond the schema.

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

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

The description clearly states the tool finds tools by describing data or task. It uses specific verbs like 'browse, search, look up, discover' and lists many domains (SEC filings, financials, etc.), distinguishing it from sibling tools which are other specific tools.

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

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

Explicitly states when to use: 'Use when you need to browse, search, look up, or discover what tools exist for:' followed by a comprehensive list. Also says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer)', providing clear guidance and context for alternatives.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral details: it fans across multiple data sources, specifies return fields (CIK, filings, fundamentals, patents, news, LEI), notes the USPTO sunset soft-failure, and clarifies parameter constraints (ticker or CIK only). No contradictions.

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

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

The description is front-loaded with concrete examples and a clear statement of purpose, but it is somewhat verbose (includes implementation details like GDELT→GNews fallback). While every sentence adds value, a tighter rewrite could improve readability without losing information.

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

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

Given the tool's complexity (multiple data sources, fallback mechanisms, partial sunset), the description is remarkably complete. It covers all major return components, limitations, and parameter constraints. Without an output schema, the description adequately explains the return structure and edge 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?

With 100% schema description coverage, the schema already documents both parameters. The description enriches meaning: type is limited to 'company' with future plans, value accepts ticker or zero-padded CIK, and explicitly excludes names, reinforcing the need for resolve_entity. This adds context beyond the enum and type constraints.

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

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

The description clearly states the tool's purpose: retrieving a full cross-source profile of a US public company in one parallel call. It uses example queries to illustrate intent, and the verb 'profile' combined with 'entity_profile' makes the resource explicit. It distinguishes from siblings like resolve_entity by noting name resolution is handled elsewhere.

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

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

The description explicitly advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views, providing clear when-to-use guidance. It also specifies that names are not supported and directs users to resolve_entity if only a name is available, effectively covering exclusions and alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true; description adds context about clearing sensitive data, but 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 concise sentences with no redundant information, effectively front-loaded.

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

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

For a simple delete operation with good annotations and schema, the description covers when and how to use, though it could mention behavior if key 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?

Schema coverage is 100% and parameter description is clear ('Memory key to delete'); description adds no additional semantics beyond the schema.

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

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

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

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

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

Explicitly provides usage context: 'when context is stale, the task is done, or you want to clear sensitive data', and suggests pairing with other 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, destructiveHint. Description adds details: fetches page, extracts title/description/key links, emits standard markdown. 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?

Three sentences, front-loaded with purpose, no wasted words, efficiently covers what the tool does and outputs.

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

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

Given no output schema, the description explains output format ('single text blob') and process. Annotations provide safety profile. Complete for a simple 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% and description doesn't add new parameter information beyond what's in schema. Baseline 3 is appropriate.

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

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

Clearly states the verb 'Generate', the resource 'llms.txt file', and the context 'for any URL'. Distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by focusing on generating the specific file format.

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

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

Provides explicit use cases (getting a client's site indexed, drafting for own project, auditing competitors) but does not explicitly mention when not to use or name alternatives, though 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 it as read-only, idempotent, non-destructive. Description adds value by specifying exactly what is returned (template name, image URL, dimensions, text box coordinates), which is beyond what annotations provide.

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

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

Two concise sentences. First sentence states action and return fields. Second sentence provides usage guidance. No wasted words.

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

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

For a tool with no parameters and a likely detailed output schema (indicated), the description fully covers what the tool does and how to use the results. 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 does not need to add parameter info; baseline per guidelines is 4.

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

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

Description clearly states 'Browse the 100 most popular meme templates' with specific verb and resource. It distinguishes from sibling tools by mentioning integration with 'caption_image', a related tool not in the sibling list but contextually relevant.

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

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

Description gives clear context (browsing popular templates) and hints at when to use the output (with caption_image). No explicit when-not or alternatives, but no competing tools exist in 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?

Annotations already indicate read-only and idempotent behavior. Description adds value by noting default active-only listing, inclusion of inactive via parameter, and listing specific returned fields.

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

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

Two sentences: first states purpose and return fields, second gives usage guidance. No wasted words, front-loaded with key information.

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

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

Given the tool's simplicity (one optional param, no output schema), the description covers purpose, return fields, usage, and default behavior adequately, though the structure of the 'params' field is not detailed.

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

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

Schema coverage is 100% with a well-described boolean parameter. Description does not add new semantic meaning beyond what schema provides, but is consistent.

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 states 'List the caller's active subscriptions' with specific verb and resource, and lists returned fields, clearly distinguishing 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?

Explicitly advises using this tool to review subscriptions before adding more or to find an ID to cancel, providing clear when-to-use context and implicitly referencing 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 indicate non-readOnly, non-idempotent, non-destructive. The description adds important behavioral context: rate-limited to 5 per identifier per day, free, no quota charge, and that the team reads digests daily, affecting roadmap. No contradiction with annotations.

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

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

Every sentence is useful and front-loaded. The description starts with the purpose, then usage cases, then constraints (rate limit, quota). No filler or 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 explains what happens with the feedback (digests, roadmap impact). It covers all parameters and constraints. Could mention whether confirmation is returned, but not necessary for this simple tool.

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

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

Schema coverage is 100%, but the description adds value by explaining the 'type' enum options in more detail, setting expectations for message length (2000 chars max), and advising to describe issues in terms of Pipeworx tools/packs. This goes beyond the schema's 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 a specific verb ('Tell') and resource ('the Pipeworx team') and clearly states the tool is for reporting broken, missing, or desired functionality. It distinguishes itself from sibling tools (which are mostly query/analysis tools) by being a dedicated feedback channel.

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

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

The description explicitly lists when to use the tool: for bugs, feature requests, data gaps, or praise. It also provides a clear 'don't' ('don't paste the end-user's prompt'). However, it does not explicitly state when not to use it (e.g., for general questions), but the sibling list includes other tools for 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 already declare readOnlyHint, openWorldHint, idempotentHint, and no destructive behavior. The description adds valuable context: self-aggregating signal, no PII, derived from CF analytics-engine, and caching window (5min-1h). 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: two sentences plus a bullet list of three use cases. Every sentence adds value, front-loaded with core functionality, and no redundant information.

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

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

While there is no output schema, the description states the return values (top tools, top packs, total call volume) and provides use-case context. It lacks details on response format or pagination, but for a simple read tool with good annotations, this is adequate.

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

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

Schema coverage is 100% with the 'window' parameter well-described in the schema. The description adds additional context by linking window length to use case (short for hot now, long for steady demand), which enhances understanding beyond the schema.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It specifies the resource ('Pipeworx trending data') and the action ('returns'), and distinguishes from siblings like 'discover_tools' by focusing on current popularity metrics.

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

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

The description lists three explicit use cases (discovering hot data sources, confirming canonical tool choices, aligning use cases) and explains how window parameter affects results (short vs long windows). It does not explicitly mention when not to use, 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?

Discloses all major behavioral traits: monotonicity checks, partition-sum verification, semantic anchor for cross-event pairs, partition filter, fill check against live CLOB depth. Annotations (readOnlyHint, etc.) are consistent and the description adds extensive context beyond 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 well-structured with labeled sections (REQUIRES, SEMANTIC ANCHOR, etc.) and front-loads the key requirement. While slightly lengthy, every sentence adds necessary detail, making it efficient for its 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 no output schema, the description thoroughly explains the response structure (opportunities, partition_check, fill check fields). It also references the related tool polymarket_fill_risk for custom sizing, ensuring the agent has full 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% but the description adds significant value: explains the difference between event and topic modes, provides example slugs/questions, and clarifies the requirement to choose one. This goes beyond the schema's brief 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 finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes two modes (event/topic) and differentiates from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

Explicitly states the tool requires one of 'event' or 'topic' and fails with no args. Recommends when to use each mode, explains cross-event mode catches patterns single-event misses, and warns against trading when fill check shows non-realizable edge.

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

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

The description goes far beyond annotations by detailing the three response segments (model_driven, structural_arbitrage, concentrated_longshot), caching behavior (1h at KV level), diagnostics for empty segments, and edge calculation details. No contradiction with annotations exists.

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

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

While the description is well-structured with sections (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, etc.), it is excessively verbose with technical jargon. It could be more concise to improve readability for an AI agent.

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

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

Given the tool's complexity (9 parameters, no output schema), the description thoroughly explains the response structure (by_segment, fed_candidates, diagnostics), edge calculation, and filtering knobs. It is complete and leaves no major gaps.

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

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

The input schema already has 100% coverage with detailed descriptions for all 9 parameters. The tool description does not add significant parameter-specific meaning beyond what the schema provides, maintaining the baseline score.

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: scanning Polymarket markets for opportunities where Pipeworx data disagrees with market price. It uses specific verbs and resources, and distinguishes itself from siblings like 'polymarket_arbitrage' by focusing on Pipeworx disagreement and providing a 'what should I bet on today' use case.

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

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

The description provides context on when to use the tool (e.g., agents discover opportunities without paging hundreds of markets) and includes parameter guidance (tradeable-edge knobs). However, it does not explicitly compare to sibling tools or state when not to use it.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds substantial behavioral details: reads snapshots, returns tracked[] with time-series, first_seen, trend, decay; expired[] with lifespan; snapshot_dates; limits on history depth and decay granularity. No contradictions.

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

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

The description is dense but well-organized, starting with purpose and then detailing response fields and limits. It could be slightly more concise, but every sentence provides necessary information. Front-loading is effective.

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

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

Given no output schema, the description fully explains return structure (tracked, expired, snapshot_dates) with field semantics. It covers all relevant aspects: purpose, parameters, response, limitations, and data granularity. 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?

Schema description coverage is 100% with both days and window described. The description adds context: days default 14 max 30 (schema says clamp 2-30), window default '1wk', and links parameters to response structure (e.g., window determines snapshot family). This adds value beyond the schema.

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

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

The description states the tool provides 'edge persistence and decay telemetry' and explicitly answers 'how long has this edge existed and is it shrinking?' This is a specific verb-resource pair with clear differentiation from siblings like polymarket_edges, which likely provides raw edges without historical context.

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

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

The description explains the use case with an example: 'a fresh wide edge and a 3-week-old wide edge are different trades...', implying when to use this tool for historical persistence analysis. It also mentions limits (60-day TTL, from snapshot enablement). While it doesn't explicitly list alternatives, the context is sufficient for an agent to decide.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds context about return fields (e.g., top_of_book, vwap_fill_price), per-leg details, and the forced_directional_risk warning. No contradiction with annotations.

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

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

The description is well-structured with sections for single-market and basket modes, front-loading the purpose. While somewhat verbose, every sentence adds value. Could be slightly more concise, but overall efficient.

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

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

Despite no output schema, the description thoroughly explains return values for both modes, including fields like capture_ratio, thin_legs[], and profit_usd. It covers mode selection, defaults, and risk warnings, making it fully complete for an agent to understand behavior.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant context: default values (size_usd=1000, clamp), side behavior in basket mode (auto from partition sum), and interpretation of size_usd across modes. This extra depth justifies a 4.

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

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

The description clearly states the tool performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between two modes (single-market and basket) and explicitly positions itself as a prerequisite for using polymarket_arbitrage and polymarket_edges, differentiating from sibling tools.

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

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

The description explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains the risks of not using it, such as partial basket fills converting arb into directional positions.

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 readOnly, openWorld, idempotent, non-destructive. The description adds significant behavioral context: it explains when the delta is a real signal vs when compatibility_warning fires due to bet shape mismatch or temporal misalignment, and exposes counters for skipped leg pairs.

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

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

The description is thorough and well-structured with bolded key terms, but slightly verbose. Every sentence adds value, though some redundancy could be trimmed for tighter 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 no output schema, the description compensates by detailing the response structure, safety fields, and temporal alignment. It fully covers the three parameters and their interplay. The tool's complexity is well addressed.

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 meaning by listing the 10 topic shortcuts with examples and explaining how explicit parameters override the mapped side. It also clarifies the response structure (leg-by-leg prices, top_spreads_pp, safety fields).

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same question, specifying two modes (topic shortcuts and explicit pairing). It distinguishes itself from sibling tools like polymarket_arbitrage (intra-venue) and compare_entities (general).

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 for two modes and warns that pre-mapped topics often return compatibility_warning, reducing false expectations. It also describes safety fields that help decide if the spread is meaningful.

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, destructiveHint. Description adds useful behavioral context: scoped to identifier (anonymous IP, etc.), and pairing with remember/forget. No contradictions.

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

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

Two sentences, no fluff, front-loaded with core purpose. Every sentence adds value: first states action, second gives usage context, third adds scoping and pairing.

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 tool (1 optional param, no output schema, rich annotations), the description covers all necessary aspects: retrieval vs listing, scoping, and pairing with sibling tools. 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?

Input schema has 100% coverage with description for parameter 'key'. Description adds the critical nuance: 'omit to list all keys', which goes beyond schema's description of the parameter.

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

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

The description clearly states the tool retrieves a saved value or lists all keys, using specific verbs (retrieve, list) and resource (memory). It distinguishes from sibling tools like remember and forget by naming them and describing 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?

Provides explicit context for when to use: 'look up context the agent stored earlier... without re-deriving it from scratch.' Mentions pairing with remember and forget, which implies alternatives. Lacks explicit 'do not use when' statements, but clear enough.

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

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint. The description adds value by explaining that returned alerts carry specific fields, that mark_read affects subsequent calls, and that the feed is persisted. 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 three sentences, front-loaded with the core purpose, then details return structure and filtering, and finally mark_read and alternative access. Every sentence adds value with 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?

There is no output schema, but the description explains the key return fields (source, citation_uri, raw event payload). It covers filtering, mark_read behavior, polling suitability, and provides an alternative endpoint. For a simple retrieval tool with full annotations, this 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 coverage is 100% with descriptions for all 5 parameters. The description adds semantics by giving a concrete example for type ('sec_8k'), explaining the effect of since (ISO timestamp), and detailing mark_read behavior. This goes beyond the schema alone.

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

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

The description starts with 'Pull fired events from your subscription feed,' clearly stating the action and resource. It specifies the return fields (source, citation_uri, raw event payload), distinguishing it from sibling tools like subscribe or list_subscriptions.

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

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

The description explains filtering by type and since with an example ('sec_8k'), and how mark_read works. It also mentions that polling is fine and provides an alternative URL for scripts/dashboards, but does not explicitly compare with siblings 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 indicate read-only, idempotent, open-world hints. Description adds significant context: fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail, and return structure with grouped changes and citation URIs.

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?

A single dense paragraph with no superfluous words. Front-loaded with examples and structured logically from queries to sources to parameters to return format. 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?

Despite no output schema, the description explains the return structure (changes grouped by source, total_changes, pipeworx:// URIs). Given the tool's complexity (multiple data sources), the description provides sufficient context for correct invocation.

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

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

Schema descriptions cover all three parameters. Description adds meaning: for 'since' it details acceptable formats (ISO, relative) and recommended values; for 'value' it clarifies ticker vs CIK formats.

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 provides a change feed for a company over a time window, with example queries. It distinguishes itself from the sibling 'entity_profile' tool by specifying when to use each.

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

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

Explicitly lists example queries and provides guidance on when to use 'entity_profile' instead. Also notes supported entity type (company) and that the tool fans out to multiple sources.

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

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

Adds context beyond annotations: scope by identifier, persistence for authenticated users, 24-hour retention for anonymous sessions. Does not contradict annotations; aligns with idempotentHint and destructiveHint.

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

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

Concise three-sentence structure: purpose, usage scenario, persistence details, then pairing with siblings. Front-loaded and every sentence adds value.

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

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

Given the simplicity of the tool (key-value store with two parameters), the description covers purpose, usage, persistence, and relationships with siblings. No output schema needed; complete for effective selection and invocation.

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

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

Schema coverage is 100% with descriptions for both parameters. The description provides additional examples of key formats and value types, enhancing understanding beyond the schema.

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

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

Description clearly states the tool's function: 'Save data the agent will need to reuse later'. It uses a specific verb ('save') and resource ('data' as key-value pairs). It distinguishes from sibling tools by mentioning pairing with 'recall' and 'forget'.

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

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

Provides explicit guidance on when to use: 'when you discover something worth carrying forward...' and gives examples. It does not explicitly state when not to use, but the context and sibling references are clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds value by disclosing that each call cascades through several lookup endpoints internally, replacing 2-3 manual lookups. This provides behavioral context beyond the annotations.

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

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

The description is well-structured and front-loaded with the core purpose and usage guidance. It then details supported types in a clear bullet-like format. While it is somewhat lengthy, every sentence contributes value, and it avoids redundancy.

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

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

Despite lacking an output schema, the description fully explains what each type returns (ticker + CIK + company_name + citation URI for company; RxCUI + ingredient + brand + citation for drug). It also mentions internal cascading behavior. For a tool with this complexity, the description is thorough and leaves no major gaps.

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

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

Schema coverage is 100%, so the parameters are fully documented in the schema. The description repeats and slightly elaborates on the parameter values with examples (e.g., 'ticker (AAPL), CIK, or name'), but this adds marginal meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: resolving a user-spoken name to a canonical/official identifier. It provides specific query examples like 'What's the ticker for...' and explicitly differentiates from siblings by saying 'Use FIRST whenever you have a name but need an ID.' This is a specific verb+resource combination with 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 gives explicit guidance on when to use ('Use FIRST whenever you have a name but need an ID') and lists supported types with detailed explanations. It does not explicitly state when not to use, but the 'FIRST' instruction implies priority over other tools, and the sibling tools context provides alternatives.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds that it returns a ranked list with score, confidence, and signal density per entity, and that it probes each entity. 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 brief, front-loaded with the core action, and every sentence provides essential context. 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 complexity, the description explains the output format and use case. It does not cover error handling or edge cases, but for a simple aggregate tool this is 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%, but the description adds value by clarifying that the first entity is treated as the subject and the rest as competitors, and notes that 'workers-ai' is the free default. This goes beyond the schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check to probe each, ranking by score. It differentiates from siblings by focusing on competitive AI-marketing audits and mentions specific use cases.

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: for competitive AI-marketing audits, and gives a concrete example: 'does Claude know about us as well as our competitors?'. It implies not to use for single-entity queries, which are handled by sibling ai_visibility_check.

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

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

Adds significant transparency beyond annotations: describes composite calling pattern, partial failure degradation, timing caveat for bundlephobia first measurement (5-30s), and sources_failed listing.

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?

Efficiently packed with key info in first sentence, then usage, return fields, limitations. No wasted words, each sentence adds value.

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

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

No output schema exists, but description fully covers return fields, includes edge cases (partial failures, timing), and ecosystem limitations. Complete for a two-parameter, no-nested-objects 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 good descriptions. Description does not add meaning beyond what schema provides (e.g., package name and optional version). Baseline 3 applies.

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

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

Description clearly states composite npm package check using deps.dev and bundlephobia, with specific use cases. Distinguishes from sibling tools by focusing on npm and combining safety/popularity/size metrics.

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

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

Explicitly says when to use: for questions about safety, popularity, size, cost. Also specifies not for other ecosystems (PyPI, Maven, etc.) and directs to deps.dev:version for those.

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), description provides technical details: BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char cap with truncation and flagging, and character offsets for verification. 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?

Single paragraph that efficiently front-loads purpose, then provides usage guidance, technical details, and constraints. Every sentence adds value; 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?

Despite no output schema, description covers return values (passages with offsets and similarity scores), internal algorithm, and constraints. Complete for a read-only search tool with clear 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%, but description adds meaningful context: explains text as 'already pulled', query as natural-language with examples, and limit via 'top-N'. Enhances schema without redundancy.

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

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

The description clearly states it performs semantic search inside a fetched record, using specific examples like SEC 10-K. It distinguishes from siblings by focusing on intra-document search and hinting at 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 says 'use when the record is too big to cram into the prompt' and pairs with ask_pipeworx_grounded for grounding. Does not explicitly state when not to use, 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?

Adds extensive behavioral details beyond annotations: returns new subscription id, requires OAuth, supports various types with specific parameters, delivery channels with limitations (SMS cap, phone verification, webhook auto-disable). The idempotentHint annotation is not contradicted and is contextualized by potential deduplication, though not explicitly stated.

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 thorough and well-structured, starting with the main purpose and then detailing types and delivery. While lengthy, each sentence adds value and the use of examples enhances clarity without being 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 complexity (nested objects, multiple types, no output schema), the description covers return value, prerequisites, type-specific filters, delivery options with limitations, and error conditions (auto-disable). Fully equips an agent to invoke correctly.

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

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

Schema coverage is 100%, but description adds significant value with concrete examples for each type and delivery channel, constraints (SMS cap, phone verification, webhook signing secret). Provides more practical guidance than the schema alone.

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

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

The description clearly states the verb 'Create' and the resource 'proactive monitoring subscription to a live-data event stream'. It distinguishes from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by focusing on creating a new subscription.

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

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

Provides clear context on when to use (create a subscription) and prerequisites (requires Pipeworx OAuth account, not for anonymous/BYO). Does not explicitly state when not to use or list alternatives, but the account restriction serves as a usage boundary.

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 flags (readOnly, idempotent). The description adds the structure of the output (bucketed examples with tool calls) and the effect of the parameter (focus vs. full spread). 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 a single paragraph with no wasted words. It is front-loaded with alternative phrasings. Slightly verbose but all content is relevant.

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

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

Given no output schema, the description adequately explains the return type (example questions with tool calls). The parameter is fully documented, and usage context is clear. The tool's role as onboarding entry point is well established.

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% (topic has a description). The description adds meaning by listing example values and explaining the effect of omitting vs providing the parameter, going beyond the schema.

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

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

The description uses a specific verb ('returns') and clearly identifies the resource ('category-bucketed example questions'). It distinguishes the tool from siblings like ask_pipeworx (which answers) and discover_tools (which lists tools), making it the onboarding entry point.

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

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

Explicitly states 'Use this FIRST' and explains when to omit or provide the topic parameter. Does not list alternatives, but the context makes it clear this is for initial exploration before using other 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 indicate mutation is non-destructive and idempotent. Description adds key detail: 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts', aligning with annotations.

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

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

Two sentences with no superfluous information. Critical details are front-loaded, making it efficient.

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

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

Given single parameter, no output schema, and present annotations, the description fully covers ownership, behavior, and impact, making it 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 the schema already documents the id parameter. Description mentions 'by id' but does not add substantial meaning beyond schema.

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

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

Clearly states 'Cancel a subscription by id' with specific verb and resource. Distinguishes from siblings like 'subscribe' and 'list_subscriptions'.

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

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

Explicitly states ownership enforcement ('you can only cancel your own subscriptions') and provides context on row deactivation vs deletion, aiding decision-making.

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, openWorldHint, idempotentHint, destructiveHint. The description adds value by listing the return types (verdict, extracted form, actual value with citation, percent delta) and explaining it replaces 4-6 sequential calls. No contradiction with annotations.

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

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

The description is well-structured, front-loaded with purpose and examples, and covers return types efficiently. Slightly verbose but earns its length.

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

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

Given the single parameter (100% coverage) and no output schema, the description compensates by detailing the output format. It is complete enough for an agent to understand input and expected results.

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%. The description adds examples and domain context to the 'claim' parameter but does not add new semantic information beyond the schema's description. Baseline 3 is appropriate.

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

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

The description clearly states the tool verifies natural-language claims against authoritative sources, provides example phrasings, and specifies the domain (company-financial claims via SEC EDGAR+XBRL). It distinguishes itself from siblings by noting it replaces multiple sequential calls.

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

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

The description says 'Use whenever the agent needs to check whether something a user said is factually correct' and specifies the domain, providing clear usage context. However, it does not explicitly state when not to use it or mention 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.