ipinfo

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

The description adds behavioral context beyond annotations: it reveals the default model (Workers AI) is free, explains the optional Anthropic API key usage and associated cost, and outlines the return format (per-model score, confidence, signals, raw_response + combined view). Annotations already indicate read-only and idempotent behavior, so the description enriches the agent's understanding.

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, using three sentences that front-load the core action and output. Every sentence adds value: the first explains the function, the second details model options and cost, the third lists use cases. No redundant or unnecessary 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?

The description adequately covers the tool's purpose, parameters, use cases, and output structure. Given the absence of an output schema, listing the return fields (score, confidence, signals, raw_response) is helpful. However, it could mention what happens if no API key is provided or error handling, but the current level is sufficient for typical use.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add detailed parameter semantics beyond the schema; it groups usage context (e.g., 'pass _apiKey to also probe Anthropic') but does not elaborate on each parameter beyond what the schema already provides.

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

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

The description clearly states 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 specifies the verb 'probe', the resource 'LLMs', and the output 'score per model'. The tool's scope is distinct from siblings like 'scan_competitor_ai_presence' by focusing on general AI visibility scoring.

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: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' While it does not name alternative tools or provide when-not-to-use conditions, the context is clear and helps the agent decide when to invoke this tool.

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

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

Annotations already indicate read-only, idempotent, and open-world. The description adds context about routing and citations but does not contradict annotations. It provides acceptable extra 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 the key preference, explains functionality, and gives usage examples. It is well-structured but could be slightly more concise; nevertheless, 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 tool's complexity (3,745 tools) and no output schema, the description covers what it does, when to use it, and what output to expect (structured answer with citations). It is fairly complete for an AI agent to understand.

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 parameters described as aliases for 'question'. The description adds context about what constitutes a good question but does not add new semantic details 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 that the tool routes questions to 3,745 tools across 884 sources, returning structured answers with citations. It provides specific examples and distinguishes itself from web search, making its purpose very clear.

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 preferring this over web search and lists many query types (e.g., 'what is', 'look up', 'find'). Examples are provided, but it does not explicitly exclude cases where alternatives like 'ask_pipeworx_grounded' or 'deep_research' might be better.

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. Description adds valuable context: costs one extra LLM call, returns explicit refusal reasons (not_in_source, no_tool_match, etc.), and explains the extraction process. 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 but well-structured, starting with the core purpose and then detailing usage, cost, and return format. Could be slightly more structured with bullet points, but still concise and informative.

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

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

Despite the complexity of the tool (routing to 3,745 tools across 884 sources), the description covers purpose, usage, return format, refusal reasons, and cost. No output schema exists, but the description compensates by detailing the exact return structure and errors.

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 parameters documented including aliases. The description states that question is in natural language but adds no further semantic meaning beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose as a 'hallucination-resistant answer mode for high-stakes reads' and details its process: picks the right tool from many, fetches data, extracts answer only from tool result. It distinguishes itself from the sibling ask_pipeworx by being grounded and not inventing facts.

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: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and when to avoid: 'prefer ask_pipeworx for casual lookups'. Provides clear context and alternative tool.

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

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

The description adds substantial behavioral context beyond annotations, including low-confidence short-circuits, closed market handling, wide-spread illiquidity warnings, resolution-rule risk from cancellation rules, and specific fan-out behaviors. No contradictions with annotations (all readOnly, idempotent, 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?

The description is verbose but structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). It contains necessary details for a complex tool but could be tightened 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?

Despite no output schema, the description thoroughly explains return shapes: result.market, result.analysis, result.evidence, resolver contract, parent_event, news fields. It covers all edge cases (closed markets, low confidence, illiquid spreads, resolution-rule risk) and safety mechanisms.

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 meaning for each parameter: market parameter shows formats, depth explains quick vs thorough, include_raw describes size implications and use cases. Fan-out examples also provide context.

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 a Polymarket bet by pulling Pipeworx data. It specifies input formats (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). It distinguishes from siblings like polymarket_edges and polymarket_arbitrage by focusing on a specific bet evaluation.

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

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

The description explicitly says when to use: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. It also covers edge cases like closed markets and low-confidence resolutions. However, it lacks direct comparison to sibling tools.

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

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

Annotations already indicate read-only, idempotent, safe behavior. The description adds context about data sources (SEC EDGAR/XBRL, FAERS), handling of fiscal years, sorting by primary metric, and output format including citation URIs. 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 front-loaded with examples and key points, and each sentence contributes information. It is somewhat long but efficient for the content density.

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 mentions return format (paired data + citation URIs) and sorting. It covers data sources, use cases, and constraints (2-5 items). Slightly lacks detail on exact structure of 'paired data' but sufficient for a tool with two simple 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?

Schema coverage is 100% with descriptions for both parameters. The description adds meaning by explaining what each 'type' value retrieves (e.g., 'company' pulls revenue, net income, cash, debt from SEC; 'drug' pulls FAERS counts) and provides concrete examples for 'values' (tickers/CIKs for companies, names for drugs).

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, using specific verbs like 'compare' and specifying metrics for each type. It distinguishes from sequential single-pack lookups by saying 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.'

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

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

The description provides explicit examples of when to use (e.g., 'Compare X and Y', 'which is bigger', 'rank these companies') and advises preference over sequential lookups. It does not explicitly state when not to use, but the sibling tool 'entity_profile' implies an alternative for single entities.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds considerable behavioral detail: decomposes questions, routes to 3,745 tools in parallel, extracts grounded answers with verbatim evidence, confidence, source, fetched_at, citation, and explicit gaps. It also mentions expected duration (15-60s) and that facts are pre-verified, going well 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 comprehensive but not overly verbose. It front-loads the key value proposition ('Grounded multi-source research in ONE call.') and then provides necessary details. Every sentence earns its place, though could be slightly more concise without losing clarity.

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

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

Given the tool has two well-described parameters and no output schema, the description compensates by explaining the return value ('structured findings packet') and its contents (verbatim evidence, confidence, source, etc.). It also covers prerequisites, time expectations, and limitations (gaps[]), making it complete for the 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 description coverage is 100%, so baseline is 3. The description adds context by explaining the meanings of depth enum values ('quick=3, standard=5, thorough=8') and that 'thorough' requires paid plan. It also clarifies that broad questions are fine for the 'question' parameter. This enhancement 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 specifies the verb 'research' and the resource 'multi-source questions' explicitly. It distinguishes itself from sibling 'ask_pipeworx' by noting that this tool handles broad/multi-part questions with parallel decomposition, while the sibling is 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?

The description explicitly states when to use this tool ('broad or multi-part questions') and when not to ('use ask_pipeworx for single lookups'). It also provides prerequisites: requires Pipeworx account, and 'thorough' depth requires paid plan. This gives clear guidance.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds that it returns top-N relevant tools with full input schemas and curated examples, and that results are ready to call directly without a second lookup—significant behavioral detail 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?

Compact description with three front-loaded sentences: purpose, usage, return value. Every sentence adds distinct value without redundancy or fluff.

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

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

Despite no output schema, description fully explains what is returned (top-N tools with names, descriptions, schemas, examples) and how to use results, making the tool's behavior complete and clear 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%, so baseline of 3 applies. Description mentions 'query' and 'limit' indirectly (natural language description, top-N) but does not add substantial meaning beyond what the schema already provides for 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?

States 'Find tools by describing the data or task' with a specific verb and resource. Clearly differentiates itself from sibling tools by positioning as a meta-discovery tool to call first when exploring available options.

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

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

Explicitly advises to use when browsing or discovering tools across various domains, and instructs to call it first when many tools are available. Provides concrete examples and contrasts with getting a single answer.

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 data sources (SEC, XBRL, USPTO, news, GLEIF), return fields, and soft-fail behavior for USPTO patents. Annotations already indicate read-only, idempotent, non-destructive; description adds valuable 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?

Description is fairly long but well-structured with example queries upfront and detailed output breakdown. Every sentence adds value, though slightly 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?

Despite lacking output schema, the description thoroughly explains all return components (CIK, filings with URIs, fundamentals, patents, news, LEI) and includes caveats (soft-fail). Sufficient for an agent to understand the tool's output.

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 enriches it by detailing value format (ticker or zero-padded CIK), constraint on type enum (only company), and prerequisite for names.

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 full cross-source profile of US public companies in one parallel call. Differentiates from sibling 'resolve_entity' by requiring ticker/CIK, not names.

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 preferring this tool over chaining single-pack lookups for holistic views. Provides when-to-use and when-not-to-use guidance (names require resolve_entity first). Could explicitly mention sibling alternatives like 'deep_research'.

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

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

Annotations provide destructiveHint=true and idempotentHint=true. The description adds context about clearing data, but does not disclose additional behavioral traits beyond what annotations offer. 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?

Two sentences: first states purpose, second adds 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?

For a simple tool with one required parameter and no output schema, the description covers purpose, usage context, and sibling relationships. 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 3. The description adds no additional meaning beyond the schema's 'Memory key to delete'.

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

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

The description clearly states 'Delete a previously stored memory by key', specifying the action and resource. It distinguishes from sibling tools 'remember' and 'recall' 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 states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also mentions pairing with siblings, implying 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, idempotentHint, destructiveHint false. Description adds behavioral context: 'fetches the page, extracts title/description/key links, emits standard format, output is a single text blob'. 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?

Five sentences, no filler. Front-loaded with the core purpose, then usage examples. Every sentence contributes value. 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 2 params, full schema coverage, no output schema, and rich annotations, the description adequately covers purpose, process, and use cases. Lacks discussion of error handling or permission needs, but these are mitigated by annotations (readOnly, non-destructive).

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 for 'url' and 'max_links'. Description repeats the default/max for max_links but adds no new semantic meaning beyond what the schema already provides. 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 'generate a production-ready llms.txt file', specifying the verb ('generate'), resource ('llms.txt file'), and context ('for any URL, so AI crawlers can index the site cleanly'). Distinguishes from siblings like 'scan_competitor_ai_presence' which audit AI visibility but don't generate the file.

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 your own project, auditing competitor indexing'. Does not explicitly state when not to use, but context is clear enough given sibling tools like '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?

The description adds behavioral detail beyond annotations by specifying the return of geolocation data (city, region, country, etc.). Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so the description enhances transparency 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?

A single, concise sentence that front-loads the purpose and lists returned data. No extraneous words; every part is valuable.

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 and an output schema (though not shown, context indicates one exists), the description sufficiently explains what the tool returns. It covers all necessary context for a simple IP lookup 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?

There are zero parameters, which sets a baseline of 4. No parameter information is needed, and the description adds no param meaning beyond what the schema provides (empty).

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 gets the user's current IP address with geolocation data, listing specific fields. The verb 'Get' and resource 'my IP' are specific, and it distinguishes from sibling 'lookup_ip' which targets other IPs.

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 when-to-use or when-not-to-use guidance is provided. The tool's simplicity and zero parameters imply usage when needing one's own IP, but no mention of alternatives like 'lookup_ip' for other IPs.

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

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. Description adds value by specifying exact return fields (id, type, params, timestamps, fire_count) and indicating scope (caller's subscriptions), which is beyond annotation information.

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 only: first states purpose and return fields, second gives usage guidance. No fluff, front-loaded with 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 simple list tool with one optional parameter and no output schema, the description covers return fields, usage context, and scope. Sufficient for an agent to understand when and how to invoke it.

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

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

Schema coverage is 100% with a clear description for 'include_inactive'. The description reiterates the default (active only) and implies the parameter's effect, but does not add significant new meaning 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?

Clearly states verb (list), resource (subscriptions), scope (caller's active), and explicitly lists the fields returned, distinguishing it from sibling tools like 'subscribe' or '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?

Provides explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. Implicitly guides when not to use (when you want to add/modify/remove), but lacks explicit alternatives.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds value by listing specific return fields (city, region, country, etc.) and the example IP format, 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 a single sentence with no unnecessary words, front-loading the core action and resource. It is 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 the tool's simplicity (single parameter, clear annotations, and an output schema), the description is complete. It mentions the key return fields and provides an example, covering all necessary 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% with a clear description for the only parameter 'ip'. The description gives an example value and mentions the format, adding slight 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's purpose: 'Get geolocation and network info for any IP address', with an example '8.8.8.8'. It distinguishes from sibling 'get_my_ip' which focuses on the user's own IP.

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

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

The description implies use for any IP address and provides a clear example. It does not explicitly state when not to use or mention alternatives like 'get_my_ip' for self-lookup, but the context makes it reasonably 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 show non-read-only, non-idempotent, non-destructive. Description adds that it's free, doesn't count against quota, is rate-limited, and that the team reads digests daily. 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?

Well-structured: front-loaded with purpose, then guidelines, then details. Slightly long but each sentence adds value. Could be marginally more concise.

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

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

Complete for a feedback tool with no output schema: explains what happens (team reads, affects roadmap), covers all user concerns (rate limits, quota, content guidelines).

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

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

Schema coverage is 100%, and description adds context beyond schema: reinforces enum meanings, describes message content (be specific, 1-2 sentences, 2000 chars max), and advises against pasting prompts.

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's for sending feedback about Pipeworx tools, covering bugs, features, data gaps, and praise. It distinguishes from siblings (none are feedback tools), and uses specific verbs like 'tell' and examples.

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: bug, feature, data_gap, praise. Also gives negative guidance: don't paste end-user prompt, and mentions rate limits and free usage.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: data source ('CF analytics-engine'), no PII, and caching behavior (5min-1h depending on window). This goes beyond 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 with no wasted words. First sentence gives a clear one-liner purpose; second paragraph lists use cases as a bullet-like structure. Technical details (source, caching) are added efficiently. 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 tool with one parameter, the description covers purpose, output (top tools, packs, call volume), data source, caching, and usage context. No output schema exists, but the description explains what is returned. Fully complete for an agent to decide when 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?

Schema has 100% description coverage for the single 'window' parameter with enum. The description further explains the behavioral difference between short and long windows, providing semantic guidance not present in the schema. This fully compensates for any lack of explicit parameter details.

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: 'What other AI agents are calling on Pipeworx right now' and specifies it returns top tools, packs, and call volume over configurable windows. It contrasts with sibling 'discover_tools' by focusing on trending data rather than discovery.

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

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

Explicitly lists three concrete use cases (discovering hot data sources, confirming canonical tools, aligning use cases). Provides guidance on selecting window duration ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). Could mention when not to use (e.g., for historical analysis), but the clarity is high.

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 algorithmic details (monotonicity violations, partition-sum checks, Jaccard similarity for cross-event, placeholder filtering, fill check referencing live CLOB depth). Adds significant context beyond annotations (readOnlyHint, openWorldHint, etc.) 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 long but well-structured with clear sections (mode selection, semantic anchor, partition filter, response, fill check). Every sentence adds value for a complex tool. Slightly verbose but justified; loses a point for not being as concise as possible.

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, no output schema, and only two parameters, the description thoroughly covers behavior, modes, response structure, edge cases (placeholders, similarity thresholds), and cross-tool dependencies (fill check). Nothing essential 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?

Schema coverage is 100%, but the description adds crucial semantics: explains the mutual exclusivity requirement, provides example slugs, mentions full URLs accepted for event, and describes the behavior of topic mode in cross-event scanning. Each parameter's purpose is enriched well 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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks' and distinguishes between event and topic modes. It specifies the verb 'Find' and resource 'arbitrage opportunities', and the two modes differentiate it 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?

Explicitly states when to use event mode ('recommended for a specific market') vs topic mode ('for cross-event scanning'), warns that calling with no args fails, and recommends polymarket_fill_risk for custom sizing. Provides clear alternative 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 provide readOnlyHint, openWorldHint, and idempotentHint. The description adds extensive behavioral details: caching at KV level (1h keyed on knobs), response structure (by_segment, fed_candidates, _diagnostics), the five model families with specific formulas, and tradeable-edge knob effects. This goes well beyond annotation coverage.

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 (around 300 words) and densely packed with details. While well-structured with numbered points and segments, it could be more concise for quick parsing by an AI agent. Front-loading is adequate but not optimal.

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 params, no output schema), the description is comprehensive, covering response structure, edge metrics, diagnostics, and caching. However, it lacks an explicit, structured overview of the complete return format, relying on inline mentions. An output schema would help, but the description fills most 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 description coverage is 100%, but the description adds significant context beyond the schema. For example, it explains min_partition_leg_kelly nuances (partition arbs ignore min_kelly) and why Fed bets are excluded. This adds value beyond the parameter 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 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' This specific verb+resource combination distinguishes it from siblings like polymarket_arbitrage. The title and name align perfectly.

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 'Built for "what should I bet on today"' and explains tradeable-edge knobs, but does not explicitly state when not to use this tool or name alternatives among siblings like polymarket_arbitrage or polymarket_kalshi_spread. Usage context is implied but not contrasted.

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 already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context beyond annotations: data comes from daily snapshots, history is bounded by 60-day TTL, decay is based on daily closes of edge_pp_net (net of default slippage), and gaps in snapshots indicate no scan that day. 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 sections for ARGS, RESPONSE, and LIMITS. It is dense but front-loaded with the core question. Slightly longer than necessary but each sentence adds value; no redundancy.

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

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

Given no output schema, the description thoroughly explains the response structure (tracked[], expired[], snapshot_dates[]) and key derived fields (first_seen, trend, decay_pp_per_day, lifespan_days). It also covers limitations (60-day TTL, daily closes, gap handling) and input defaults, making it self-contained 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 description coverage is 100%, so baseline is 3. The description adds context like 'lookback, default 14, max 30' (clamp 2-30) and 'snapshot family, default 1wk' but these largely duplicate schema. The description does not introduce major new semantics beyond what the schema already provides.

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

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

The description clearly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots' and answers the specific question 'how long has this edge existed and is it shrinking?'. This distinguishes it from sibling tools like polymarket_edges (which likely provides current edges) by focusing on temporal dynamics.

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 implicit usage guidance by contrasting 'a fresh wide edge' vs 'a 3-week-old wide edge', indicating when persistence matters. However, it does not explicitly list when not to use this tool (e.g., when current snapshot is sufficient) or name alternative tools like polymarket_edges.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds significant behavioral context: walks the ladder, returns fill details, verdict, and warns about forced directional risk from partial fills. No contradiction with annotations.

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

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

Well-structured with clear sections for single-market and basket modes. Front-loaded core purpose. Slightly lengthy but justified given the complexity of two modes and multiple return fields. 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 tool's complexity, multiple modes, and lack of output schema, the description is exhaustive. Enumerates all return fields, explains defaults and limits, and highlights risks (unhedged directional positions). No gaps.

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

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

Schema coverage is 100%, so baseline is 3. Description adds extra meaning by explaining default values and how size_usd is interpreted differently in single-market vs basket modes, compensating for the absence of an output 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 the tool performs a realizable-vs-theoretical edge check against live order-book depth. Differentiates two modes (single-market and basket) and explicitly distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Provides explicit guidance on when to use: before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500. Explains why (theoretical overround may not be capturable, partial fills cause directional risk). Does not explicitly list when not to use, but context implies it's for pre-trade validation.

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 detailed behavioral traits beyond annotations: compatibility_warning conditions (two cases), temporal alignment, skipped cross types/subtypes, and the caveat that real spreads are rare despite the shortcut list. Annotations already indicate read-only, idempotent, non-destructive nature, and the description adds substantial 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 lengthy but well-organized: purpose first, then modes, response, safety fields. Every sentence adds context, though some redundancy exists (e.g., repeating 'pre-mapped ≠ tradeable'). Appropriate for tool complexity, but could be slightly more concise.

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

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

Despite lacking an output schema, the description fully explains the response format (leg prices, spreads, safety fields) and covers edge cases (temporal misalignment, non-equivalent bet shapes, semantic mismatches). Complete for a complex 3-parameter 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 parameter descriptions, but the description adds value by explaining the two-mode interplay, providing examples, and clarifying the interaction between topic and explicit overrides, which goes beyond schema details.

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, distinguishing it from sibling tools like polymarket_arbitrage which focus on single-venue arbitrage.

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

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

Two modes are explicitly described (topic shortcuts vs explicit ticker/slug) with guidance on when each is appropriate, and warnings that most pre-mapped topics currently return compatibility_warning. However, explicit comparison to similar sibling tools is absent.

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 scope to user identifier and listing behavior, enhancing transparency 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?

Concise three sentences, front-loaded with core action, 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?

Completeness is high given the simple tool (1 optional param, no output schema); covers functionality, scope, and relationships to siblings.

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

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

Schema coverage is 100% with a clear description of the 'key' parameter. The description adds no further semantic value beyond what the schema already provides.

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

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

The description clearly states it retrieves a value via 'remember' or lists all keys, with a specific verb and resource, and distinguishes from sibling tools 'remember' and 'forget'.

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

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

Explicitly says when to use (look up stored context), scope (user identifier), and pairing with remember/forget as 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 readOnlyHint, idempotentHint, etc. The description adds useful behavioral details like the effect of mark_read on subsequent calls and return field descriptions, 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 a single, well-structured paragraph that wastes no words. It starts with the main action, then lists features, and ends with a practical usage note.

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 covers return fields, parameter details, and practical usage (polling, alternative access). It is comprehensive for a retrieval tool with good annotations.

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

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

With 100% schema coverage, baseline is 3. The description explains each parameter's purpose (e.g., 'filter to one subscription type', 'ISO timestamp') and default limit, 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 uses strong verbs like 'Pull' and specifies the resource ('fired events from your subscription feed'). It details what is returned (source, citation_uri, raw payload) and filtering options, distinguishing it 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 notes that polling works fine and even provides an alternative access method for scripts/dashboards. While it doesn't explicitly state when not to use it, 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 fan-out to multiple sources, fallback from GDELT to GNews under rate limits/5xx, USPTO PatentsView API sunset causing soft-fail, and return format. Consistent with annotations (readOnlyHint, openWorldHint, idempotentHint).

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 front-loaded with query patterns, efficiently covering multiple sources, parameter options, fallback, and sibling differentiation without redundant language.

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

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

Despite no output schema, the description explains return format (changes[] grouped by source, total_changes count, citation URIs). Covers all essential behavioral and usage information for a complex multi-source 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?

All three parameters have schema descriptions (100% coverage). The description adds clarity: 'type' limited to 'company', 'since' accepts relative shorthand, 'value' accepts ticker or CIK. 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?

The description clearly states the tool returns a change feed for a company covering SEC filings, news (with GDELT→GNews fallback), and patents. It distinguishes from sibling 'entity_profile' which provides static profile.

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

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

Explicit guidance on when to use this tool vs 'entity_profile', plus examples of parameter usage (ISO date vs relative shorthand) and behavior across data sources with fallback logic.

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 idempotentHint=true and destructiveHint=false. Description adds scoping by identifier, persistence details (authenticated vs anonymous), and storage mechanism (key-value pair), which go beyond annotations. 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?

Four sentences that front-load purpose, then usage, then storage details. 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?

For a simple two-parameter tool with no output schema, the description covers what, when, how, scoping, persistence, and companion tools. Adequate for the agent to judge usability.

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

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

Schema covers both parameters with descriptions (100% coverage). Description does not add new semantic information for parameters beyond examples already in schema, so baseline score of 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 the tool saves data for reuse, uses specific verbs like 'save' and 'store', and distinguishes from siblings like recall and forget by explicitly mentioning them as companions.

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

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

Explicitly says when to use ('when you discover something worth carrying forward') and provides examples. Also directs to pair with recall and forget, giving clear alternative usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by mentioning internal cascading of lookups, which explains the tool's complexity beyond a simple one-to-one mapping.

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

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

The description is well-structured with a clear opening, usage guideline, and bullet-like listing of supported types. It is not overly verbose, with each sentence contributing to understanding. Slight redundancy in examples could be trimmed, but overall 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?

Despite the absence of an output schema, the description thoroughly explains return values for each entity type, including specific identifiers and citation URIs. This makes the tool self-contained and easy to use without external documentation.

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 enriches both parameters significantly. For 'type', it lists specific entity types; for 'value', it provides accepted input formats (ticker, CIK, name for company; brand/generic for drug) and notes auto-disambiguation, which goes well 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 resolves names to canonical identifiers, using specific verbs like 'resolve' and 'look up'. It provides concrete usage examples and distinguishes itself from siblings like 'entity_profile' by focusing on ID resolution. The supported types are explicitly listed with details on what is returned.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID', providing clear when-to-use guidance. However, it does not explicitly state when not to use it or mention alternative tools, but the context is sufficient for an agent to make a reasonable decision.

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?

Goes well beyond annotations (readOnly, openWorld, idempotent) by describing the internal logic: probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, signal density. 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?

Four sentences with 84 words; front-loaded with the core purpose, then mechanism, usage example, and return fields. No wasted words, efficient structure.

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 (4 parameters, no output schema), the description adequately covers what the tool does, how it works, what it returns, and provides context for usage. The return fields (score, confidence, signal density) are listed, which compensates for the lack of an explicit output schema.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by stating that the first entity is treated as the 'subject' for narrative purposes, and clarifies that 'workers-ai' is the free default model. These details are not in the schema.

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

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

Clearly states the tool compares AI visibility across multiple entities side-by-side, which distinguishes it from the sibling 'ai_visibility_check' that probes a single entity, and from 'compare_entities' which is generic. Specific verb 'compare' and resource 'AI visibility across entities'.

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

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

Explicitly positions the tool for competitive AI-marketing audits with a concrete example. Implicitly suggests using 'ai_visibility_check' for single entity checks, but does not provide explicit when-not-to-use guidance or alternative comparisons beyond these two 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 indicate readOnly, openWorld, idempotent, non-destructive. Description adds that partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed lists timeouts. Provides detailed return structure including summary block, per-advisory details, links, and recent alternative versions.

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 but densely packed with useful information. Every sentence adds value. Could be slightly more structured with bullets, but front-loads the main purpose well.

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

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

No output schema, but the description comprehensively explains return structure (summary block, advisory details, links, alternative versions), failure modes, and ecosystem scope. Very complete for a composite 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 clear parameter descriptions. The description adds value by stating scoped packages are accepted for 'package' and that 'version' defaults to latest. This extra context justifies above baseline.

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

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

The description clearly states it's a composite check for npm packages covering safety, popularity, and size, with specific output fields listed. It distinguishes from sibling tools by specifying NPM ecosystem only; none of the sibling tools overlap.

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 specifies when to use: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes ecosystem scope and alternative for other ecosystems (deps.dev:version directly).

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 safety (readOnlyHint, idempotentHint, etc.). Description adds critical behavioral details: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, character offsets, and similarity scores. Goes well 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?

Description is concise: 4 sentences, each carrying essential information. Front-loaded with purpose and usage, followed by technical details. 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 complexity (semantic search, truncation, embedding details) and no output schema, the description sufficiently explains what the tool does, how it works, and what the agent receives (passages with offsets and scores). Covers all necessary aspects for an AI to select and invoke correctly.

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

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

Schema description coverage is 100%, so parameters are already well-documented. Description adds extra value with example queries for the 'query' parameter and clarifies the maximum text length for 'text'. Minor gap: no mention of default limit value, but schema covers default. Score 4 because description enhances understanding without being essential.

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

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

Clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article, long tool result). Distinguishes from sibling ask_pipeworx_grounded by mentioning their pairing, making the purpose distinct.

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 when to use ('when the record is too big to cram into the prompt') and hints at alternatives ('Pairs with ask_pipeworx_grounded'). Provides clear context for choosing this tool over others.

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

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

Annotations indicate readOnlyHint=false, idempotentHint=true. Description aligns by detailing the write operation, OAuth requirement, and rate limits (10 SMS/day). Adds behavioral context beyond 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?

Description is detailed but front-loaded with core purpose. Every sentence adds value; could be slightly more concise but justified by complexity.

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

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

Covers prerequisites, all subscription types, parameter details, delivery options, and constraints. No output schema but states returns subscription id. Complete for the tool's complexity.

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

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

Schema coverage is 100%. Description adds meaning for each type enum with examples, and explains delivery channels with constraints (SMS verification, webhook HMAC signing). Provides significant value beyond schema.

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

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

The description clearly states it creates a proactive monitoring subscription to a live-data event stream and returns the new subscription id. It distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

Provides explicit context on when to use (creating subscriptions), including prerequisites like a Pipeworx OAuth account. Does not explicitly state when not to use, but the context is sufficient.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral detail: returns category-bucketed examples with live catalog, no side effects. 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 verbose but all sentences earn their place, front-loaded with common user questions. Slightly redundant in listing categories that are in schema; still 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?

No output schema, but description fully explains return format (category-bucketed examples with tool+argument shape). Covers when, how, and what, making it complete for a 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 has 100% coverage with one parameter described. Description adds usage nuance: 'omit for cross-category spread' and lists example topic values beyond schema enum, providing practical guidance.

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

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

Description explicitly states it is the onboarding entry point, returning category-bucketed example questions with exact tool+argument shape. Clearly distinguishes from siblings like ask_pipeworx or compare_entities.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides guidance on no arguments vs topic. Lacks explicit mention of alternatives, but context makes it 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?

The description goes beyond annotations by explaining that the row is deactivated, not deleted, and that historical events remain via recent_alerts. Annotations already indicate non-destructive and non-readOnly, but description adds concrete 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?

Two concise sentences, each adding value: first states purpose and constraint, second explains the behavioral nuance. 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 single-parameter tool without output schema, the description covers the core functionality, ownership constraint, and the side-effect of deactivation. This is complete for effective usage.

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

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

Schema coverage is 100% with a clear description for 'id'. The tool description adds minimal further meaning (e.g., referencing 'returned by subscribe'), which meets the baseline for well-documented parameters.

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

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

The description clearly states the action ('Cancel a subscription by id'), identifies the resource (subscription), and adds the scope ('ownership enforced'), distinguishing it from siblings 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?

It explicitly says ownership is enforced ('you can only cancel your own subscriptions'), providing a clear when-to-use condition. It doesn't explicitly list alternatives, but the sibling context is sufficient.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral details: it returns a verdict with types, extracted structured form, actual value with citation, and percent delta. It also notes the domain limitation, enriching 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 paragraph that front-loads trigger phrases, then states usage, domain, and return details. It is concise but could be slightly tighter (e.g., merging some examples). Still well-structured and information-dense.

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 return value (verdict, structured form, citation, delta). It covers the single parameter fully and provides sufficient context for an agent to use the tool correctly.

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

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

The single parameter 'claim' has a schema description. The description adds context about natural-language claims and supported financial claims, enhancing understanding beyond the schema.

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

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

The description uses explicit trigger phrases like 'Is it true that...' and 'fact check', clearly states it verifies natural-language claims against authoritative sources, and specifies the domain (company-financial claims). It distinguishes 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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also specifies the supported domain, giving clear context for when to use, though it doesn't explicitly state when not to use or list alternatives.

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