Epo Ops

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

Annotations (readOnlyHint, idempotentHint, destructiveHint: false) indicate safe, read-only behavior. The description adds valuable context: free default model, BYO key for Anthropic, per-model scoring, and no side effects. 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 two sentences with no wasted words. It front-loads purpose and default model, then adds key detail on API key and return structure. Every sentence is essential.

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 4 parameters and no output schema, the description covers tool behavior (probing, scoring, return structure) sufficiently. It lacks details on 'signals' but overall is complete enough for an agent to understand 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 descriptions cover 100% of parameters. The description adds meaning beyond schema by explaining the default model, the role of '_apiKey', and using 'context' for disambiguation. This raises the score above baseline 3.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100) per model. It distinguishes from sibling tools like 'ask_pipeworx' or 'entity_profile' by focusing on AI visibility scoring across multiple models.

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

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

The description provides explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use the API key (to probe Anthropic). However, it does not explicitly state when not to use this tool or compare it to siblings like 'scan_competitor_ai_presence'.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds value by explaining the routing behavior and that it returns structured answers with stable citation URIs, which is helpful 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 dense but well-structured, starting with the key preference, then listing categories, explanation, usage patterns, and examples. Every sentence adds value, no waste.

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 of routing to many tools and no output schema, the description covers purpose, usage, behavior, and expected output with examples and mention of citation URIs. It is complete for the tool's needs.

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 being aliases for 'question'. The description explains that the input is a natural language question, provides examples, and clarifies the aliases, adding 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?

Description clearly states it answers factual questions using structured data from many sources, with specific examples and a preference over web search. It distinguishes itself from siblings by mentioning routing to 3,800 tools and providing citation URIs.

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 'PREFER OVER WEB SEARCH' and lists numerous query patterns and categories. It provides concrete examples of when to use, effectively guiding the agent on appropriate contexts.

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

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

The description adds significant behavioral context beyond the annotations: it explains the extraction logic (using only tool results), the return structure including refusal reasons, the extra LLM call cost, and the conditions that trigger refusal. 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?

Each sentence is informative and earns its place. The description is well-organized: starts with the core purpose, explains behavior, lists return fields, and ends with usage guidance. No fluff.

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

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

Despite no output schema, the description fully explains the return value structure for both success and failure, including specific refusal reasons. It also covers cost implications and use-case constraints, making the tool fully understandable.

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 description does not need to add much. It mentions the question parameter and its aliases, but this largely duplicates the schema. While sufficient, it does not add deeper semantic meaning beyond what the schema already conveys.

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 identifies this tool as a hallucination-resistant answer mode for high-stakes reads. It explicitly contrasts with the sibling ask_pipeworx, noting the extra cost and use case distinction, making the purpose highly specific and distinguishable.

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

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

The description provides explicit guidance on when to use this tool (high-stakes, when answers are quoted/cited/acted on) and when not to (casual lookups, preferring ask_pipeworx). It also lists example domains (financial verdicts, legal claims, medical lookups, public statements) and states the refusal behavior.

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 the annotations (readOnlyHint, idempotentHint, etc.). It details the resolution process, classification, fan-out examples, response shapes, error states (low-confidence matches, closed markets, wide spreads), resolution-rule risk, and blocking conditions. Every behavioral trait is disclosed, making the agent's decision process fully informed.

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 (multiple paragraphs) but well-structured with clear sections, examples, and warnings. It front-loads the main purpose and uses formatting (e.g., bullet-like lines) for readability. However, some details (like response shapes and resolution rules) could be shortened or moved to an output schema if one existed. It earns its length due to complexity but could be more concise.

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

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

Despite no output schema, the description thoroughly documents return values (result.market, result.analysis, result.evidence) and includes edge cases (low-confidence matches, closed markets, wide spreads, cancellation rules). It also explains parent_event extraction and news error handling. The description covers all necessary context for an agent to use the tool correctly.

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

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

Schema coverage is 100% and the description adds significant context beyond the schema. For 'market', it explains acceptable formats (slug, URL, text). For 'depth', it defines 'quick' vs 'thorough'. For 'include_raw', it explains the size implications and when to use true. This extra information helps the agent select appropriate parameter values.

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 the input types (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). The description distinguishes it from sibling tools by detailing its comprehensive data-fan-out and classification behavior.

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

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

The description 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 examples of inputs (e.g., 'will fed cut rates in june 2026'). However, it does not explicitly state when NOT to use the tool or suggest alternatives, though sibling tools like 'polymarket_edges' could be implied. This is still a minor gap.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds specifics: pulls latest 10-K data from SEC EDGAR/XBRL with off-calendar fiscal year handling, FAERS data for drugs, returns sorted results, and includes citation URIs. Does not mention limitations like missing data or rate limits.

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

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

The description is front-loaded with example queries, then explains behavior. It is packed with information but each sentence adds value. Slightly long but 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?

For a comparison tool with no output schema, the description covers input, behavior, data sources, and sorting. It doesn't mention output format details or potential error cases, but given the complexity and good annotations, it is reasonably 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% but the description adds significant meaning: for 'type' enum, it explains what data each value pulls; for 'values' array, it specifies expected formats (tickers/CIKs for company, names for drug). This goes beyond the schema's description.

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

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

The description clearly states the tool does side-by-side comparison of 2–5 companies or drugs in a single call. It provides example queries and explicitly distinguishes itself from sequential single-pack lookups, with sibling tools like 'entity_profile' serving a different 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?

The description explicitly advises to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear when-to-use guidance. It implies when not to use (for single entities) by contrasting with sequential 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 adds substantial behavioral context beyond annotations: it decomposes questions, runs parallel searches, returns structured findings with evidence, confidence, source, and explicit gaps array. Also mentions expected wait time (15-60s) and account requirements, which annotations lack.

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

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

The description is front-loaded with the core value proposition and all sentences add value. It is somewhat long but still efficient for the amount of detail conveyed. Could be slightly more concise without losing key points.

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 the return format (structured findings packet with evidence, gaps, etc.), prerequisites (Pipeworx account, paid plan for thorough), and expected latency. It leaves no major gaps for an agent to be uncertain about.

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 both params described. The description adds concrete meaning: depth enum values correspond to number of facets (quick=3, standard=5, thorough=8) and thorough requires a paid plan. This enriches the schema info.

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

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

The description clearly states the tool performs 'Grounded multi-source research in ONE call' by decomposing questions into sub-questions and routing them to many tools/sources. It distinguishes itself from sibling ask_pipeworx, which 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?

Explicit guidance: 'Use for broad or multi-part questions' and 'use ask_pipeworx for single lookups.' Also notes account requirement, paid plan for thorough depth, providing clear when-to-use and 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?

Adds behavioral context beyond annotations, describing that it returns top-N tools with schemas ready to call, and no second lookup needed. No contradictions.

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

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

Single paragraph, front-loaded with purpose, each sentence adds value. Slightly verbose but 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?

Completely covers purpose, input, output, and usage strategy. Sufficient for 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%, so baseline is 3. Description adds context about query aliases and examples but doesn't significantly augment schema semantics.

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/task, and lists extensive domains. It distinguishes itself from sibling tools by being a meta-tool for 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 advises 'Call this FIRST' when many tools are available, providing clear usage guidance and implied 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?

Beyond annotations (readOnly, idempotent), description details behavior: fans out across multiple sources, returns specific fields including URIs for filings, sorts fundamentals, notes patents API sunset with soft-fail, and mentions fallback mechanisms. Adds significant 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?

Description is thorough but somewhat verbose; it front-loads example queries then details outputs. Could be slightly more structured (e.g., bullet points), but every sentence adds value. Minimal waste.

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

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

Without output schema, description fully explains return fields (cik, filings with URIs, fundamentals sorted, patents status, news fallback, LEI). Covers edge cases like patents sunset and name resolution. Comprehensive 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?

Despite 100% schema coverage, description adds meaning: explains that type is only 'company' with future plans, and value must be ticker or zero-padded CIK (with examples), explicitly excludes names. Provides critical usage context beyond schema.

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

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

The description clearly states the tool provides a 'full cross-source profile of a US public company' and lists example queries. It distinguishes itself from siblings like resolve_entity by specifying when to use this tool versus others.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack lookups when user asks for holistic view' and notes that names are not supported, directing to resolve_entity. Provides clear when-to-use and when-not-to guidance.

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

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

Annotations indicate destructiveHint=true and idempotentHint=true. Description confirms deletion action and adds context about clearing sensitive data, but doesn't detail error handling or idempotency behavior.

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

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

Two sentences with no wasted words. First sentence states action, second provides usage context. Efficient and front-loaded.

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

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

For a simple tool with one required parameter and no output schema, the description fully covers purpose, usage, and pairing with siblings.

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

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

Schema coverage is 100% with clear property description 'Memory key to delete'. Description adds no additional 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?

Description clearly states 'Delete a previously stored memory by key' – specific verb and resource. Distinguishes from siblings by mentioning pairing with 'remember' and 'recall'.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also suggests pairing with remember and recall for context.

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

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

Annotations already indicate safety (readOnly, idempotent, non-destructive). The description adds behavioral details: fetches the page, extracts title/description/key links, and emits standard markdown. 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 plus a bullet list of use cases. Every sentence adds value, front-loaded with purpose. No wasted words.

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

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

No output schema, but the description clearly states the output is a single text blob in standard llms.txt markdown format. For a low-complexity tool with 2 simple params, this is complete and actionable.

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 description does not need to add parameter details. It provides context for max_links (default 25, max 50) which is already in the schema. No additional semantic 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 generates a production-ready llms.txt file for any URL, specifying the exact output format and use cases. It distinguishes well from siblings, which are mostly search and analysis tools unrelated to generating llms.txt.

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

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

The description explicitly provides use cases (indexing client site, drafting own llms.txt, auditing competitors), giving clear context for when to use. It lacks explicit 'don't use' guidance but the specificity makes it 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 true and destructiveHint false, so the safety profile is clear. The description adds no additional behavioral context beyond the purpose. This is adequate but not enhanced.

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

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

The description is a single, concise sentence with no wasted words. It is front-loaded and 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 the tool's low complexity, an output schema exists, and annotations are present, the description is minimally adequate. However, it lacks details about return values or behavior when an abstract does not exist.

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

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

Schema description coverage is 100% for the single parameter 'number', which is described as 'Patent number, epodoc format'. The description does not add further meaning, but the schema already sufficiently documents 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?

Description clearly states the tool retrieves 'Abstract text for a patent.' This is a specific verb+resource that distinguishes it from siblings like get_claims or get_biblio, though no explicit differentiation is made.

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

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

No guidance on when to use this tool versus alternatives. The description does not provide context or exclusion criteria, leaving the agent to infer usage from the name alone.

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

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

Annotations already indicate safe, idempotent operation. Description adds field list but no other behavioral traits (e.g., format, pagination).

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

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

Single sentence, no redundancy, front-loaded with purpose.

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

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

Output schema covers return values, so minimal description is acceptable. However, it omits details like response format or scope, but given annotations and schema, it's 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 has 100% coverage and describes 'number' well. Description adds no additional 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 it retrieves bibliographic data for a patent and lists specific fields (title, inventors, applicants, dates, classifications). Differentiates from sibling tools like get_claims and get_abstract.

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?

Implies usage for bibliographic retrieval but provides no explicit guidance on when to use vs siblings or when not to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds minimal behavioral context beyond the fact that it returns claims text. 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 extremely concise at four words, front-loading the essential information. Every word is meaningful and there is no extraneous 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?

Given the existence of an output schema, the return values are already documented. The tool is simple with one parameter and rich annotations. The description suffices for basic understanding, though it could briefly differentiate from similar tools like get_abstract or get_biblio.

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

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

The input schema covers the single parameter 'number' with a description ('Patent number, epodoc format'), and schema coverage is 100%. The tool description does not add any further parameter info, so baseline of 3 is appropriate.

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

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

Description clearly states it retrieves claims text for a patent. The verb 'get' is implied from the tool name. However, it does not differentiate from siblings like validate_claim or other get_* tools, which share similar naming patterns.

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

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

No guidance is provided on when to use this tool versus alternatives such as get_abstract, get_biblio, or validate_claim. The description lacks any explicit context for 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 and idempotentHint true, which the description does not contradict. The description adds the concept of 'INPADOC family' and worldwide scope, which provides some behavioral context beyond annotations, but does not disclose rate limits, error handling, or response details.

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

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

The description is a single, concise sentence (9 words) with no filler. It is appropriately front-loaded and every word adds value.

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

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

Given the tool's simplicity (1 parameter, existing output schema, and clear annotations), the description provides sufficient context. It explains the tool's purpose and the scope of results without needing further elaboration.

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

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

Schema coverage is 100%, and the description adds no additional meaning beyond the schema's parameter description ('Patent number, epodoc format'). 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?

The description clearly states the tool fetches an INPADOC family (related patent applications worldwide), which is a specific verb-resource pair. It distinguishes from siblings like get_abstract or get_claims by focusing on family relationships, though it could be more explicit about the output structure.

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 (when you need patent families) but no explicit guidance on when to use this versus alternatives like search_patents or get_biblio. Usage is implied rather than explicitly directed.

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=false. The description adds specific return fields and clarifies that only active subscriptions are returned by default, adding value beyond annotations. No contradictions.

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

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

The description is two sentences, front-loaded with the main purpose, and every sentence adds 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 the simple nature of the tool (list with one optional param) and no output schema, the description adequately covers return fields and usage context. Complete for the task.

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

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

Schema description coverage is 100% for the single parameter include_inactive. The description does not add new meaning beyond the schema, meeting the baseline expectation.

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

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

The description clearly states the tool lists the caller's active subscriptions, specifying the verb 'list' and resource 'subscriptions'. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on reading existing subscriptions.

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

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

The description explicitly suggests when to use the tool: before adding more subscriptions or to find an id to cancel. It does not mention when not to use, but the context is clear enough for an agent.

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 key behaviors beyond annotations: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, team reads digests daily. Annotations are not contradicted.

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?

Efficient one-paragraph description with front-loaded purpose and usage. Could be slightly more structured (e.g., bullet points for types), but no unnecessary content.

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

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

Covers all needed aspects: purpose, when to use, parameter roles, limitations, and outcome (team reads daily). No output schema, but the description sufficiently explains the tool's effect.

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?

Adds meaning beyond schema: explains 'type' enum values in detail, suggests message length (1-2 sentences, 2000 chars max), and describes optional context. Full schema coverage (100%) but description enriches usability.

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: sending feedback (bugs, feature requests, data gaps, praise) to the Pipeworx team. It differentiates from siblings, which are primarily query/information tools, by being a communication 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?

Explicitly describes when to use (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). Also provides rate limit and free usage info, giving clear decision support.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds that data is derived from CF analytics-engine, no PII, and cached 5min-1h. This provides useful behavioral context not captured in 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 at 4 sentences, front-loading the main action followed by use cases and technical notes. Could be slightly tighter, but no wasted words.

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

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

Given the tool's simplicity (1 optional param, no output schema, good annotations), the description is complete enough. It explains return values (top tools, top packs, volume) and ensures the agent knows what to expect.

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

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

Schema coverage is 100% with a single parameter. The description adds semantic meaning by explaining why different windows are useful (hot vs steady-state), which enhances understanding beyond the enum.

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 what the tool does: returns top tools, top packs, and total call volume over a window. It uses specific verbs and resources, and the listed use cases help distinguish 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 lists three use cases for when to use this tool, but does not mention when not to use it or provide direct alternatives. However, the use cases are clear and actionable.

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

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

The description provides extensive behavioral details: checks performed (monotonicity, partition_sum), thresholds (3pp, 0.30 Jaccard), fill check against live CLOB depth, and conditions to avoid trading. Annotations (readOnly, openWorld, idempotent) are consistent and description adds far more 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 comprehensive and well-structured with sections (REQUIRES, event mode, topic mode, SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). However, it is somewhat verbose; a few sentences could be trimmed 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's complexity and lack of output schema, the description comprehensively explains inputs, internal logic, response structure (opportunities array, partition_check, fill_check), and limitations. It provides enough information for an agent to use correctly.

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

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

Schema coverage is 100% but description adds significant meaning: explains event as a slug or full URL, topic as a seed question, and details how each parameter affects the tool's operation (single-event vs cross-event).

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 between single-event and cross-event modes, and differentiates from sibling tools like polymarket_fill_risk 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?

The description explicitly requires one of two parameters (event or topic) and explains when to use each: event for a specific market, topic for cross-event scanning to catch patterns missed by single-event mode. It also warns against calling with no args and references polymarket_fill_risk for custom sizing.

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

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

Annotations indicate readOnly, openWorld, idempotent, and not destructive. The description adds extensive behavioral context: caching strategy ('Cached 1h at the KV level'), model family details, edge calculation, diagnostics, and knob effects. No contradictions with annotations.

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

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

The description is verbose but well-structured: front-loaded with purpose, then segments, parameters, diagnostics, and caching. Every sentence adds value, though it could be slightly more concise for quick scanning. The structure aids comprehension for a complex tool.

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

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

No output schema, so description must fully explain return values. It details the three segments, diagnostics, fields like edge_pp_net, and Fed bets exclusion. For 9 parameters and no output schema, the description is exceptionally 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%, baseline 3. The description adds practical semantics beyond schema, e.g., for min_kelly: 'Skips opportunities that are too small to bet sensibly even if the edge is large.' It provides usage guidance and examples for multiple parameters, raising the score above baseline.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It distinguishes from sibling tools like polymarket_arbitrage by focusing on edges (disagreements) and uses specific verbs and resource scoping.

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

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

The description provides context for when to use ('what should I bet on today') and explains tradeable-edge knobs and diagnostics for understanding empty segments. It lacks explicit 'when not to use' but effectively guides usage through detailed parameter descriptions and segment explanations.

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

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

The description discloses key behaviors beyond annotations: history depth bounded by 60-day TTL, gaps due to cache-miss snapshot writing, and decay computed from daily closes not intraday. These details are not present in the annotations (readOnlyHint, openWorldHint, etc.) and add significant 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 purpose and response structure, then limits. It is dense but every sentence adds value. Slightly longer than necessary, but acceptable given the complexity.

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

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

Given no output schema, the description thoroughly explains the response fields (tracked, expired, snapshot_dates) and limits. It covers all necessary behavioral and output 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% and description repeats defaults and clamp values. It adds minor context like 'snapshot family' for window, but does not significantly enhance 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?

The description clearly states the tool provides 'edge persistence and decay telemetry' from daily snapshots, answering how long an edge has existed and whether it is shrinking. It distinguishes from the sibling 'polymarket_edges' by adding temporal context, and details the response structure (tracked, expired, snapshot_dates).

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 assessing edge age and decay, and contrasts fresh vs. old edges. However, it does not explicitly state when to use this tool over alternatives like 'polymarket_edges' or when not to use it.

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

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

The description goes beyond annotations by detailing exactly what the tool does: walks the order book ladder, returns verdict categories (clean|degraded|cannot_fill), forced_directional_risk, etc. It adds significant behavioral context about thin books and the risk of partial fills, all consistent with the readOnly, idempotent, non-destructive annotations.

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

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

The description is dense and well-structured, starting with core purpose then splitting into modes. While every sentence adds value, it is slightly verbose; however, it remains effective and front-loaded.

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

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

Given no output schema, the description comprehensively explains all return fields for both modes (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict for single-market; theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs, max_clean_notional_usd, forced_directional_risk for basket). It covers the entire scope of the tool.

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

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

Schema coverage is 100%, but the description adds extensive semantic context: it explains the dual meaning of 'side' in single vs basket mode, how 'size_usd' is interpreted differently for buys/sells and basket, default behavior for side in basket mode, and clamp range. This far exceeds what the schema provides.

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

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

The description clearly states it performs a realizable-vs-theoretical edge check against live CLOB order-book depth. It differentiates two modes (single-market and basket) and explicitly ties usage to sibling tools like polymarket_arbitrage and polymarket_edges, making the purpose very specific and distinguishable.

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 THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also warns about thin books and partial basket fills converting arb into unhedged directional positions, providing clear when-to-use and when-not-to-use guidance.

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

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

The description adds extensive behavioral context beyond annotations, including compatibility_warning conditions, temporal alignment, and skipped cross types. Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint; 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 moderately long but dense with information, front-loaded with purpose and modes. Every sentence adds value, though slightly verbose for some agents.

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 response structure (leg-by-leg prices, matched spread, safety fields) and covers edge cases (compatibility_warning conditions, temporal alignment). Complete for a complex tool.

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

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

All 3 parameters have schema descriptions, and the description adds meaning: lists the 10 topic shortcuts, explains that explicit tickers override the topic-mapped sides. This goes beyond the schema.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes: topic shortcuts and explicit tickers. This distinguishes it from sibling tools like polymarket_arbitrage.

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

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

The description explains when to use (comparing prices across venues) and provides warnings about when spreads are not tradeable (compatibility_warning). However, it does not explicitly list alternative tools for different scenarios.

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 show readOnly, idempotent, non-destructive. Description adds scoping by identifier and pairing info, which is valuable beyond annotations.

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

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

Three sentences, each adds value. Could be slightly more concise but not 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?

For a simple key-value retrieval without output schema, description covers purpose, usage, scoping, and pairing. Missing return format is acceptable.

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 one parameter. Description adds meaning: omitting key lists all, and provides usage context (e.g., user's target ticker).

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 retrieves a value saved via remember or lists all keys if omitted. Differentiates from siblings 'remember' and 'forget'.

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

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

Provides context for when to use (look up stored context without re-deriving), mentions pairing with remember/forget, and scoping. Lacks explicit when-not-to-use but implied.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, destructiveHint false. The description adds behavioral details: mark_read flags events as read, unread_only filters, and the feed is also at a REST endpoint. 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 and front-loaded with core purpose. Every sentence adds information, though slightly verbose. Could be slightly more concise without losing clarity, but overall good.

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

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

No output schema, but the description explains return fields (source, citation_uri, payload). Covers key behaviors and usage notes. Lacks explicit error handling or rate limits, but sufficient for a read-only 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?

Schema coverage is 100% but the description adds meaning by explaining mark_read toggles read status, unread_only filters null read_at, since is ISO timestamp, type filters by subscription type. It provides context beyond schema descriptions.

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

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

The description clearly states the tool pulls fired events from the subscription feed, listing specific fields (source, citation_uri, payload). It distinguishes from siblings by focusing on recent alerts from the feed, not subscription management or other tools.

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

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

Provides context for when to use (polls work fine, same feed available via REST for scripts/dashboards). Implicitly distinguishes from subscription management tools, but does not 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.

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable behavioral context: fans out to multiple sources, fallback logic, date format flexibility (ISO vs relative), structured return with grouping and citations, and PatentsView sunset soft-fail. 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 front-loaded with core purpose and query examples, then elaborates logically. Every sentence adds value, but could be slightly more concise (e.g., merging fallback details). Still well-structured for 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 complexity (multiple sources, no output schema), description covers fan-out, fallback, date formats, and return structure (grouped changes, count, citations). However, it lacks details on individual change item fields, which would improve completeness. Still high overall.

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 provides 100% coverage with parameter descriptions. Description enriches each: explains `since` format with examples and recommendation, clarifies `type` is limited to 'company', and shows `value` accepts ticker or CIK. Adds significant practical guidance 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?

Description clearly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call', specifying sources (SEC, GDELT/GNews, USPTO) and contrasting with sibling entity_profile tool. This makes purpose and distinction unambiguous.

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

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

Provides example queries and explicitly recommends entity_profile for static profiles instead. Mentions fallback behavior (GDELT→GNews) and USPTO soft-fail, but does not fully enumerate when not to use (e.g., if only stock price needed). Still clear context for typical 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?

Discloses persistence duration (24h anonymous, persistent for authenticated), scoping by identifier, and 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?

Structured with purpose first, usage guidance, then technical details; no redundant sentences.

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

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

For a simple tool with 2 required params and no output schema, description covers storage, scope, and pairing with recall/forget comprehensively.

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 usage context but no new parameter details beyond schema.

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

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

Explicit verb 'save data', clear resource 'key-value pair', distinguishes from sibling tools recall and forget by stating 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 (discover something worth carrying forward), not to use (maybe when not needed), and names recall and 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 read-only, idempotent, open-world, non-destructive. The description adds value by explaining that each call cascades through multiple internal lookup endpoints, replacing 2-3 manual steps. 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 starts with the core purpose. Every sentence adds value, covering usage, supported types, and internal behavior. Despite length, it is well-structured and 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 the complexity of two entity types with multiple input forms and detailed outputs (without an output schema), the description thoroughly explains what the tool does, what it returns, and how to use it. It also notes the cascading lookup process, providing complete 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?

The schema covers 100% of parameters, but the description adds detailed meaning: for company type, accepts ticker, CIK, or name; for drug, accepts brand or generic name. It also describes the output fields (ticker, CIK, URI for company; RxCUI, ingredient, URI for drug), which is not in the schema.

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

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

The description clearly states the tool's purpose: resolving user-spoken names to canonical identifiers. It provides specific examples ('What's the ticker for...' / 'find the CIK for...') and distinguishes itself from sibling tools by focusing on identifier lookup rather than profiling or comparison.

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

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

The description explicitly advises 'Use FIRST whenever you have a name but need an ID.' It lists supported entity types and input formats, guiding when to use. However, it does not explicitly mention when not to use (e.g., when ID already known). Could be slightly more specific on exclusions.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds value by explaining the internal process (probing with ai_visibility_check, ranking, outputting scores/confidence/signal density) and the narrative treatment of the first entity. This goes beyond annotation basics.

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, each serving a purpose: purpose, process, and use case. No extraneous information. Front-loaded with the main action.

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

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

Given the tool has 4 parameters, no output schema, but rich annotations, the description covers purpose, usage, output structure, and key parameter constraints. It could address error handling or pagination if applicable, but overall it's complete for agent decision-making.

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

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

Schema coverage is 100%, but the description adds significant semantics: entity limit (2-8), first entity as subject, models supported with defaults and API key requirements. These details are not in the schema descriptions, enhancing agent understanding.

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

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

The description clearly states the tool's purpose: to compare AI visibility across multiple entities side-by-side. It uses a specific verb 'compare' and identifies the resource 'AI visibility'. It distinguishes itself from sibling tools like ai_visibility_check by noting it probes each entity with that tool, and from compare_entities by focusing on AI presence.

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

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

The description provides a clear use case: competitive AI-marketing audits. It implicitly contrasts with ai_visibility_check by mentioning it probes each entity with that tool, suggesting the single-entity alternative. However, it does not explicitly state when not to use it, leaving some room for improvement.

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

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

Annotations already declare readOnly, openWorld, idempotent, not destructive. The description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, sources_failed field lists timeouts, and the rest still returns. No contradictions with annotations.

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

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

The description is dense but front-loaded with the core purpose. Each subsequent sentence adds necessary detail (usage, return fields, ecosystem bounds, error handling). It is slightly long but all content earns its place; no redundancy. Could be slightly more structured, but 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 describes the return value: summary block with specific fields, per-advisory detail, links, and alternative versions. Also covers partial failures and timing. Complex tool is fully documented, so contextually 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% (both parameters described). The description adds extra semantic context: 'package' accepts scoped packages like '@types/node', and 'version' defaults to latest when omitted. These go beyond the schema definitions, justifying a slightly higher score than baseline 3.

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

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

The description clearly states the tool as a composite check for deciding whether to add an npm package, specifying the data sources (deps.dev, bundlephobia) and the types of information gathered. It distinguishes itself from siblings by explicitly limiting to npm in v1 and directing other ecosystems to deps.dev:version.

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 agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also provides exclusion guidance: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', helping agents avoid misuse.

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

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds that it returns a paginated list of publication numbers and uses CQL, enhancing transparency. No contradictions.

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

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

The description is two sentences plus example queries, efficiently conveying purpose, output format, and usage patterns. Every element is necessary and front-loaded.

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

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

With only two parameters, high schema coverage, and an output schema, the description covers purpose, query language, examples, and pagination. It is sufficient for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100% with well-described properties. The description's example queries provide practical semantics for the query parameter, showing syntax and typical usage, which 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 clearly states it searches published patents using CQL, and the resource and action are unambiguous. Sibling tools like get_abstract and get_claims are retrieval-focused, distinguishing this as a search tool.

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

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

The description explains the tool returns paginated results from CQL queries, and provides examples illustrating usage. However, it does not explicitly state when to use this tool versus alternatives like get_abstract or get_claims, though the context implies search vs. retrieval.

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

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

Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds significant behavioral details: embedding model (BGE-base-en), similarity method (cosine), window size (500-char overlapping), character cap (200K with truncation flag), and that results include offsets. 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 two sentences plus a technical detail sentence. It is front-loaded with purpose, then usage context, then technical specifics. No wasted words; 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?

Given the tool's complexity (semantic search, embeddings, truncation), the description covers purpose, appropriate use, pairing, technical behavior, and result format (offsets, scores). No output schema, but the description compensates adequately. Complete for 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% with all parameters described. The description adds value by providing concrete examples for 'text' and 'query' (e.g., 'SEC 10-K body', 'supply-chain risk'), which helps the agent understand usage. However, the 'limit' parameter details are already in the schema, so the addition is minor.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' using a specific verb and resource. It distinguishes itself from siblings by explaining the pairing with ask_pipeworx_grounded and the scenario of avoiding prompt cramming.

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: 'Use when the record is too big to cram into the prompt' and suggests pairing with ask_pipeworx_grounded. Also mentions truncation cap and flag, helping agent decide when to use.

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

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

The description adds useful behavioral details (delivery channels, rate limits, verification requirements) beyond annotations. However, it contradicts the idempotentHint=true annotation by stating 'Create a proactive monitoring subscription,' which implies a non-idempotent action, creating confusion.

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

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

The description is informative and front-loaded with the purpose. It efficiently conveys essential details without wasting sentences, though the structure is a single paragraph that could be slightly better organized.

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

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

Given the lack of output schema, the description explains the return value ('new subscription id') minimally but adequately. It covers all input aspects, delivery channels, and constraints well, making it sufficiently complete for agent 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?

With 100% schema coverage, the schema already documents all parameters. The description adds substantial value by providing concrete examples, required fields per type, and detailed delivery channel behavior that goes beyond the schema definitions.

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

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

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

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

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

The description provides clear context on when to use the tool (to get alerts on specific event types) and specifies requirements (Pipeworx OAuth account, no anonymous/BYO). It also hints at constraints like SMS rate limits and phone verification, though it doesn't explicitly exclude alternative tools.

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

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds that results are drawn from a live catalog, returns categorized examples with tool shapes, and describes behavior with/without topic parameter. 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 informative but slightly verbose, listing alternative phrasings upfront. However, it is well-structured with a clear lead and logical flow, making it easy for an agent to parse.

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

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

For a simple tool with one optional parameter and no output schema, the description covers all necessary context: purpose, usage, parameter behavior, and expected output. It provides a complete picture.

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 value by listing example topic values and explaining the effect of omitting vs providing the topic parameter. It clarifies the usage beyond the schema description.

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

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

Description clearly states it returns category-bucketed example questions with tool call information, serving as the onboarding entry point. It differentiates from sibling tools by specifying it's for when the agent doesn't know what Pipeworx can do.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides guidance on passing topic to focus. It also states when to call with no arguments. However, it does not explicitly exclude 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 provide non-destructive and idempotent hints; the description adds important details about deactivation (not deletion) and historical event availability, going beyond the annotations.

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

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

Two concise sentences with no fluff. The first sentence states the purpose, the second adds constraints. Highly efficient and front-loaded.

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

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

For a simple tool with one parameter and no output schema, the description fully covers what the tool does, its side effects, and constraints. No gaps.

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

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

The input schema has 100% coverage, describing the 'id' parameter as a UUID from subscribe. The description does not add new parameter details, so a baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Cancel a subscription by id.') and distinguishes the tool from siblings like 'subscribe' and 'list_subscriptions' by focusing on cancellation.

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 specifies ownership enforcement ('you can only cancel your own subscriptions'), providing clear context for when to use this tool. It does not explicitly list alternatives but the constraint is well communicated.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral context: returns a verdict (confirmed/approximately_correct/refuted/inconclusive/unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. It also explains the data sources (SEC EDGAR + XBRL). 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, front-loaded with example phrasings and purpose, and includes essential details like supported domain, return structure, and efficiency gains. 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 explains return values (verdict types, citation, delta). It explicitly states domain limitations (v1 supports company-financial claims) and the underlying data sources. This provides complete contextual information 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?

Only one parameter 'claim' with schema description coverage at 100%. The description provides natural-language examples of what the claim should look like, which adds marginal value beyond the schema's description. However, the schema already adequately documents the parameter's meaning.

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

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

The description uses specific verbs like 'fact check', 'verify', 'confirm or refute', clearly states the resource (natural-language claim verification against authoritative sources), and distinguishes itself from sibling tools by noting it replaces multiple sequential calls. It also specifies the domain (company-financial claims).

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 whenever the agent needs to check whether something a user said is factually correct.' It also specifies the supported domain (company-financial claims) and what it replaces. It does not explicitly mention when not to use or compare to alternatives, but the context from sibling tools provides some guidance.

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