Ieee Standards

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false. The description adds context: probes multiple models, uses a default free model, requires BYO key for Anthropic, and returns per-model scores plus combined view. 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, both front-loaded with key information. The first sentence states action and output; the second adds default model and API key details. Every sentence earns its place with 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 tool's moderate complexity (4 parameters, no output schema), the description is complete. It explains return shape (per-model score, confidence, signals, raw_response, combined view), default model behavior, and parameter specifics, leaving no significant gaps.

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

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

Schema description coverage is 100%, so the schema already documents parameters. The description adds value by explaining the default model for the 'models' parameter, clarifying that '_apiKey' is only needed for Anthropic, and providing examples for 'entity' and 'context', which 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 probes LLMs for knowledge about a business/brand/product/topic and scores visibility, using a specific verb ('probe') and resource. It distinguishes from sibling tools by the unique function of AI visibility scoring, not just general Q&A or research.

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

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

The description explicitly lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains default model usage and when to provide an API key. It does not explicitly mention when not to use, but the context is clear enough for an agent to infer appropriate 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context: routes to 5,132 tools across 1350 sources, fills arguments, returns structured answer with citation URIs. 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?

Front-loaded with key directive ('PREFER OVER WEB SEARCH'). Structured with examples and guidance. A bit verbose 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?

Given the complexity (6 aliased parameters, no output schema), the description provides comprehensive context: tool's role, routing mechanism, citation URIs, and usage tiers. Covers all necessary aspects for an agent to use it correctly.

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

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

Schema has 100% coverage with descriptions for all 6 parameters (including aliases). The description mentions the main 'question' parameter and its aliases but doesn't add significant meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: answering factual questions about current/historical data from authoritative sources. It uses specific verbs ('routes', 'fills', 'returns') and distinguishes from sibling tools like ask_pipeworx_grounded and deep_research.

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

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

Provides explicit guidance: 'PREFER OVER WEB SEARCH' and 'START HERE for most questions'. Lists specific scenarios (SEC filings, FDA data, etc.) and directs when to use alternatives (grounded for verbatim evidence, deep_research for broad queries). Also lists example questions.

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

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

Adds significant behavioral context beyond annotations: describes beta status, source-preference ranking, fallback behavior, and comparison to stable router. Annotations already declare safety traits, and description complements them without contradiction.

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

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

Description is a single paragraph that front-loads key information (beta, identity, purpose). It is concise but could benefit from slightly more structure (e.g., bullet points) for readability.

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 adequately covers purpose, usage, behavioral differences, and relationship to stable router. It is complete for a beta tool with experimental features.

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 6 parameters as aliases for 'question'. Description mentions 'same arguments' but adds no additional meaning beyond the schema. 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?

Description clearly states it is a beta version of ask_pipeworx, an identical universal router with candidate routing improvements. It specifies the verb 'ask' and resource 'pipeworx', and distinguishes from siblings like ask_pipeworx (stable) and ask_pipeworx_grounded.

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

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

Explicitly says 'Use it exactly like ask_pipeworx when you want the newest routing', providing clear context for when to use it. Does not explicitly exclude other cases but implies it is for testing experimental routing.

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, open-world, idempotent, non-destructive. Description adds critical behavioral details: costs one extra LLM call, return format with fields (answer, evidence, confidence, source, fetched_at, refusal_reason), and enumeration of refusal reasons. No contradiction with annotations.

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

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

Four sentences pack purpose, mechanism, return format, usage guidance, and cost comparison. Front-loads core value proposition. Every sentence is necessary and informative.

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

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

Given complexity, description covers all essential aspects: input (question), output (structured refusal or answer), behavioral constraints (only from tool result), cost trade-off, and sibling differentiation. Absence of output schema is compensated by detailed return description.

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 alias documentation. Description adds only that question is 'natural language', which is implied. No additional semantic guidance beyond schema, meeting baseline for high coverage.

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

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

Description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' and explains the mechanism: same routing as ask_pipeworx but extracts answer only from tool results, returning explicit refusals. This distinctly differentiates it from sibling ask_pipeworx.

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

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

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples (financial verdicts, legal claims, medical lookups, public statements). Also advises preferring ask_pipeworx for casual lookups, providing clear exclusion.

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 extensive behavioral details: market resolution, classification, fan-out, response shapes (evidence packet, market-vs-model), safety checks (low-confidence, closed markets, wide spreads), resolution-rule risk parsing, and fallback mechanisms (e.g., GDELT 429s). It also explains when analysis fields are suppressed. There is 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 long but highly informative and well-structured. It uses labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) making it easy to scan. Every sentence adds value, though some redundancy exists (e.g., repeating 'closed/dead markets'). It is front-loaded with the core functionality.

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 (multi-step research, multiple data sources, classifiers, safety checks), the description covers all necessary aspects: input forms, classifier types, fan-out examples, response structure, edge cases (low confidence, closed markets, wide spreads), resolution rule risk, and fallback behavior. There is no output schema, but the description details what to expect in the response. It is thoroughly 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 beyond the schema. For example, it explains how 'market' accepts slug, URL, or question text. It clarifies 'depth' values (quick vs thorough) and their impact on fan-out. 'include_raw' is explained with guidance on size implications. Examples in the schema are also reinforced in the description. The description compensates fully for any ambiguity.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies the action (research) and the resource (Polymarket bet). The detailed explanation of resolving, classifying, and fanning out distinguishes it from siblings like polymarket_arbitrage or polymarket_edges. The verb+resource combination is specific and unambiguous.

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

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

The description provides explicit use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also lists classifiers and fan-out examples, giving context on when to invoke. It does not explicitly state when not to use it or compare to siblings, but the use cases are clear enough for an agent to select appropriately.

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, open-world, idempotent, and non-destructive behavior. Description adds value by detailing off-calendar fiscal year handling, citation URI retrieval, and sorting by primary metric. No contradiction.

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

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

Description is longer than minimal but well-structured with trigger phrases upfront and clear explanation of behavior. Could be slightly trimmed without losing value.

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

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

No output schema, but description explains return format (paired data + citation URIs) and sorting behavior. Covers purpose, usage, parameters, and results adequately for a comparison tool with two entity types.

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

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

Both parameters have 100% schema coverage. Description adds meaning by explaining data sources per type ('SEC EDGAR/XBRL' for company, 'FAERS' for drug), example values (tickers vs drug names), and constraints (min 2, max 5).

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 uses specific verbs ('compare', 'side-by-side comparison') and identifies the resources (companies via SEC EDGAR, drugs via FAERS). It distinguishes itself from siblings like 'entity_profile' by stating it replaces 8-15 sequential lookups.

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

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

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups' and provides example queries ('X vs Y', 'which is bigger'). Clearly communicates when to use this tool (comparison of 2-5 entities) versus alternatives (single entity 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?

Annotations already indicate safe read-only behavior (readOnlyHint, idempotentHint, non-destructive). The description adds rich behavioral context: account requirement, depth tiers, timing (15-90s), parallel decomposition, return packet structure (evidence, gaps, contradictions, citation_uri, hop field), excerpting behavior for large records, and second-hop iteration. 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 comprehensive but lengthy. However, it is well-structured with critical info front-loaded (account requirement, alternative tool) and subsequent paragraphs for details. While not maximally concise, 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?

Given no output schema, the description fully explains the return format (findings packet with evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], hop field). It also covers timing, excerpting behavior, and depth-specific behaviors. Complete for the tool's complexity.

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

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

Schema coverage is 100% with both parameters described. The description adds significant value beyond the schema: explains the enum depth options in terms of facets, hops, and paid tier; clarifies 'question' can be broad/multi-part. Complements the schema well.

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

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

The description explicitly states the tool performs 'grounded multi-source research across Pipeworx's 1350 STRUCTURED data sources' in one call, and distinguishes it from open-web search and sibling tools like ask_pipeworx. It clearly identifies the verb (research) and the resource (structured data sources).

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

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

The description provides explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data', and when-not-to: 'For a single lookup use ask_pipeworx' and for breaking news 'prefer ask_pipeworx'. It also notes account requirements and the alternative if not signed in. Alternatives are named clearly.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds that results include full schemas with curated examples, ready to call, no second lookup. Adds value beyond annotations.

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

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

Description is well-structured and front-loaded with main purpose. Slightly lengthy but each sentence adds value. Could be slightly more concise.

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

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

No output schema but description explains exactly what is returned (names, descriptions, schemas). Covers necessary context for agent to use tool effectively.

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

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

Schema coverage is 100%, so baseline is 3. Description mentions query accepts aliases but that is already in schema. No additional semantic 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?

Description clearly states 'Find tools by describing the data or task', lists many domains, and explains it returns schemas ready to call. Distinguishes from siblings as a discovery tool.

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

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

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Clear when-to-use and implies when not to.

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

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

Annotations already declare safe, idempotent, read-only. Description adds important behavioral context: parallel call, soft-fail for patents, fallback for news. 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 with example queries upfront, then details. Slightly verbose but all information earns its place. Not bloated.

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 only 2 simple params and no output schema, description fully details return fields, sources, and behaviors. No gaps 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 covers both params with descriptions (100% coverage). Description reinforces with examples and edge cases, adding value beyond schema.

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

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

Description clearly states it is for holistic profiles of US public companies in one parallel call. Lists specific data sources and differentiators like 'ALWAYS PREFER over chaining...' Distinguishes from siblings like resolve_entity.

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

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

Explicitly says when to use (holistic view) and when not (only have a name, use resolve_entity). Provides examples and alternatives.

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

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

The description's 'Delete' aligns with the destructiveHint: true annotation. It adds context about use cases (stale context, task completion, clearing sensitive data) but does not contradict annotations.

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

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

The description is concise with two sentences, no redundant information, and front-loaded 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?

For a simple delete tool with one parameter, no output schema, and clear annotations, the description fully covers purpose, usage, and pairing, making it 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?

The input schema has 100% coverage with a single 'key' parameter described as 'Memory key to delete.' The description does not add further semantic detail beyond the schema, so the baseline of 3 is appropriate.

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

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

The description explicitly states 'Delete a previously stored memory by key,' providing a specific verb and resource. It distinguishes from sibling tools like 'remember' and 'recall' by focusing on deletion.

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

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

The description clearly states when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with 'remember' and 'recall,' providing context about alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint—all consistent with generating a file. The description adds context: it fetches the page, extracts information, and outputs a text blob. No contradictions; the extra detail about the process enhances transparency.

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 paragraph. It front-loads the main action and target audience, then provides brief details and use cases. Every sentence earns its place; no wasted words.

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

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

For a tool with 2 parameters and full schema coverage, the description explains the output format (standard llms.txt markdown) and lists use cases. No output schema exists, but the description compensates well. The agent has enough 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?

The input schema has 100% coverage with descriptions for both parameters (url with example, max_links with default and max). The description does not add additional semantics beyond restating the tool's purpose, so baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose with specific verbs: 'generate a llms.txt file for any URL', 'fetches the page, extracts title/description/key links, emits standard format'. It distinguishes itself from all sibling tools (none are similar), so the agent can select it correctly.

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

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

The description lists three specific use cases: getting a client's site indexed, drafting for own project, auditing competitors. This gives clear context for when to use. It doesn't explicitly say when not to use or mention alternatives, but the use-case specificity is strong.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, openWorldHint=true. The description adds context: that the tool returns only metadata and abstract (full text sold separately), requires an API key, and uses the IEEE Xplore Metadata API. This adds value beyond annotations.

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

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

The description is a single concise paragraph, front-loaded with the core action, then returned data, usage context, requirements, and examples. Every sentence adds value with no redundancy.

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

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

Despite no output schema, the description thoroughly explains what is returned (full list of metadata fields), prerequisites (API key), usage scenarios, and limitations (no full text). Annotations cover safety. The tool is simple and the description is fully adequate.

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

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

Schema coverage is 100% with descriptions. The description adds examples showing usage patterns and clarifies that either doi or article_number must be supplied. This reinforces the schema but does not add entirely new semantic information.

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

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

The description clearly states the tool fetches one IEEE Xplore record by article_number or DOI, listing the full metadata returned. It explicitly distinguishes from siblings (ieee_search, ieee_standard_search) by stating it is used to expand hits from those tools. Examples reinforce the 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 states when to use: to expand a hit from ieee_search or ieee_standard_search, or when an agent already holds a DOI or article number. It also notes that full text is not included, implying it should not be used when full text is needed. Could be improved by explicitly stating 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 mark it as read-only and idempotent. The description adds important behavioral context: requires an API key, returns only metadata+abstract (full text is sold), and explains the subscription process. 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 lengthy but front-loaded with the core purpose and returns. It includes examples and important caveats (full text purchase). Every sentence adds value, though it could be slightly more concise.

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

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

For a search tool with 11 parameters and no output schema, the description covers purpose, usage, authentication, filtering, and limitations. It does not detail pagination or sorting defaults but those are in the schema. Overall, it provides sufficient context for effective use.

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

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

Schema description coverage is 100%, so the description adds limited value beyond examples and context (content_type values, API key registration). The examples improve usability but do not significantly enhance parameter understanding.

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

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

The description clearly states it searches the 'FULL IEEE Xplore corpus' and lists all content types (journals, conferences, standards, etc.). This distinguishes it from siblings like 'ieee_article' which likely retrieves a single article, and 'ieee_standard_search' which is standards-specific.

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 usage scenarios (paper search, literature review, tracking author's publications) and explains how to narrow by content_type. It does not directly contrast with sibling tools but gives enough context for appropriate 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 significant value beyond the annotations by detailing that the tool returns metadata and abstract but not normative text, which requires purchase. It also explains the API key setup process, including potential delays. These disclosures are beyond what annotations (readOnlyHint, idempotentHint, etc.) provide, enhancing transparency.

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

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

The description is somewhat lengthy but well-organized, starting with a clear purpose followed by examples and important details. Every sentence adds value, though it could be slightly more concise without losing information.

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

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

Given the lack of an output schema, the description adequately describes the returned fields (title, abstract, year, DOI, link). It also covers authentication constraints and result limits. However, it does not specify the response structure or pagination details, leaving slight gaps for a complex 5-parameter tool.

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

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

The schema covers all parameters with descriptions (100% coverage). The description provides example queries and default values for limit, but these are minor additions. Since the schema already does the heavy lifting, the description only adds moderate extra context.

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

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

The description clearly states the tool's purpose: search IEEE standards by number or topic. It provides specific examples of standards and query strings, making it unambiguous. Although it does not explicitly distinguish from sibling tools like ieee_article or ieee_search, the focus on 'standards' and the examples differentiate it effectively.

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 standard searching with examples but does not explicitly state when to use this tool over alternatives like ieee_search or ieee_article. There is no guidance on when not to use it or alternative tools, which would help agents make better decisions.

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, destructiveHint=false, idempotentHint=true. The description adds that it returns active subscriptions by default and optionally includes inactive ones, plus lists return fields. 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: first states purpose and return fields, second gives usage guidance. No wasted words.

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

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

For a simple read-only list tool with strong annotations, the description adequately covers purpose, usage, and return fields. Minor lack of error handling details, but sufficient.

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

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

Input schema has one parameter with 100% coverage. The description notes 'active subscriptions', implying the effect of include_inactive without explicit detail. Slight additional value beyond schema.

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

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

The description clearly states it lists the caller's active subscriptions and enumerates the return fields (id, type, params, etc.). It distinguishes from siblings like subscribe and unsubscribe.

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

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

Explicitly states when to use: 'to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and differentiation from subscribe/unsubscribe.

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 rate limit (5 per identifier per day), free usage, and that it doesn't count against tool-call quotas. These details are not present in the annotations, which only indicate non-readonly and non-destructive. No contradiction found.

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

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

The description is concise (about 100 words) and well-structured: it opens with purpose, then usage conditions, then instructions, and finally limitations. Every sentence adds necessary information 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?

Given that there is no output schema and the tool is straightforward (submitting feedback), the description covers all essential aspects: what it does, when to use, how to format input, and constraints. It also explains the impact on roadmap, which is valuable contextual info.

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 description provides additional guidance for the 'message' parameter (don't paste user prompt, describe in terms of tools/packs), which adds value beyond the schema's high coverage (100%). The 'type' parameter is already well-defined 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: to tell the Pipeworx team about bugs, missing features, or praise. It specifies the resource (Pipeworx team) and verb (tell), and distinguishes itself from sibling tools which are primarily search/research oriented.

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 when to use (bug, feature/data_gap, praise) and what to avoid (not pasting end-user prompt). Also mentions that feedback is read daily and affects roadmap, which helps agents decide when to use it.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds that data comes from CF analytics-engine, no PII, and is cached 5min-1h, providing valuable behavioral context beyond annotations.

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

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

The description is concise with a clear main action, bulleted use cases, and no wasted words. It front-loads the core functionality and is well-structured for quick parsing.

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

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

The description covers purpose, usage, and behavior well. However, since there is no output schema, it could specify the exact structure of the returned data (e.g., lists with counts). Still, the information is sufficient for an agent to use the tool.

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

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

The single parameter 'window' has full schema coverage with an enum. The description adds semantic guidance: shorter windows surface hot trends, longer windows show steady-state demand, enriching the schema's basic 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 returns top tools, top packs, and total call volume over a window. It uses specific verbs and resources, and given sibling tools like discover_tools or search tools, this description effectively distinguishes the trending/aggregation 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?

Three explicit use cases are provided, helping an agent decide when to use this tool. However, it does not mention when not to use it or compare to siblings like discover_tools. The caching note is useful.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds details: scans top ~200 markets, walks child markets, checks ordering, computes partition_sum, signals at >3pp deviation, applies Jaccard similarity filter and placeholder fraction filter. Also describes fill check logic and output structure.

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-organized: starts with a summary, then details modes, parameter behavior, filtering, response structure, and fill check. Every sentence adds value. Front-loaded with purpose and usage.

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

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

No output schema, but description thoroughly explains output: opportunities[] with gap_pp, suggested_trade, reasoning, context; partition_check object with sum_yes_prices, gap_from_1, etc. Covers all relevant behavior and edge cases.

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

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

Schema covers 100% of parameters with brief descriptions. Description significantly expands: event accepts slug or URL, topic consumes seed question; explains each mode's behavior and limitations. Provides deeper meaning and usage context.

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

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

States 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks'. Clearly distinguishes from sibling tools like polymarket_edges and polymarket_fill_risk by specifying the method and output.

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

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

Explicit guidance on when to use no args (trending scan), event (single event), or topic (cross-event). Recommends event for specific markets, explains cross-event catches patterns single-event misses, and advises not to trade when realizable_edge ≤ 0, referring to 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 declare readOnly, idempotent, non-destructive. The description adds extensive behavioral context: caching at KV level, model families, segment structure, diagnostics, and risk warnings like 24h-move. No contradictions.

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

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

The description is dense and informative, using caps and structure. Though long, every sentence adds value for an AI agent to understand complex models and knobs. Slightly wordy but justified.

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 9 parameters and no output schema, the description thoroughly explains response structure (by_segment, diagnostics, fed_note) and how each segment works, including model formulas and filtering logic. 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?

Schema coverage is 100%, but the description adds significant meaning: explains slippage_pp assumptions, min_partition_leg_kelly rationale, tradeable-edge knobs like max_spread_pp and min_liquidity with practical guidance.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on model-driven and structural edges with specific segments.

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 targets 'what should I bet on today' and notes agents can discover opportunities without paging markets. It implies usage context but does not explicitly contrast with siblings; still, the detail provides adequate 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 (readOnly, openWorld, idempotent, not destructive) are complemented by detailed behavioral context: data sourced from daily snapshots, response structure, data gaps, and decay calculation limitations. No contradictions.

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

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

The description is dense but front-loaded with the core purpose. Every sentence adds value, though it is slightly lengthy. Structured logically with response details.

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

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

Despite no output schema, the description thoroughly explains the response structure (tracked, expired, snapshot_dates) and limitations. Complete for a complex telemetry tool.

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

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

Schema already has 100% coverage with descriptions. The description adds default/max for 'days' and enumerates 'window' options, providing extra 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 it provides edge persistence and decay telemetry, answering a specific question about edge duration and trend. It distinguishes from sibling polymarket_edges by focusing on time-series analysis.

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

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

Explicitly says when to use: to understand how long an edge has existed and if it's shrinking. Contrasts fresh vs old edges. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral context: walks the order book ladder, returns specific fields (top_of_book, vwap_fill_price, slippage_pp, etc.), and warns about forced directional risk. No contradiction with annotations.

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

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

The description is well-structured with clear sections for single-market and basket modes, front-loaded with purpose. Every sentence adds value, and the length is appropriate for the complexity.

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

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

Given the complexity (4 parameters, no output schema), the description is thorough. It explains both modes, inputs, outputs, and risk scenarios completely, enabling correct agent usage.

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

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

Schema coverage is 100%, but the description adds meaningful context: default values, mode selection logic, and interpretation of size_usd in basket mode. It goes beyond the schema's parameter descriptions.

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

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

The description clearly states 'Realizable-vs-theoretical edge check against live CLOB order-book depth', specifying two distinct modes (single-market and basket) with clear parameters and outputs. It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicit guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains when not to use (thin books, partial fills converting arb into directional risk).

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

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

Adds extensive behavioral context beyond annotations: explains safety fields (compatibility_warning, temporal_alignment, skipped counters), conditions for meaningful spreads, and that real spreads are rarer than the macro list suggests. 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?

Description is well-structured with sections, bold key terms, and front-loaded first sentence. Every sentence adds value; no wasted words despite length.

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

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

Given the tool's complexity (cross-venue spread with safety flags), the description is complete. Explains output fields, modes, and warning conditions. No output schema but description compensates with detailed behavioral info.

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%, giving baseline of 3. Description adds meaning by explaining the two modes, the 10 macro shortcuts, and how explicit parameters override. Provides concrete examples.

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 computes the cross-venue spread between Kalshi and Polymarket for the same resolving question. Uses specific verb 'spread' and resources 'Kalshi' and 'Polymarket'. Distinguishes from sibling 'polymarket_arbitrage' which is venue-internal 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?

Explains two modes (topic shortcuts vs explicit parameters) and warns that most pre-mapped topics return compatility_warning, guiding when results are meaningful. Lacks explicit when to prefer this over siblings but context is clear.

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

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

Adds scoping detail ('anonymous IP, BYO key hash, or account ID') beyond readOnly and idempotent 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?

Two concise, front-loaded sentences with no wasted words; each sentence serves a 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?

Given simple schema and clear annotations, the description fully covers retrieval behavior, scoping, and relationship to sibling tools.

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, description adds clarity by explaining the effect of omitting key ('list all keys'), which aids parameter understanding.

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

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

The description explicitly states the tool retrieves a saved value or lists all keys, with clear distinction from siblings like '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 clear use cases ('look up context the agent stored earlier') and mentions pairing with remember/forget, though no explicit when-not-to-use.

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

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

The description contradicts annotations: annotations set readOnlyHint=true, implying no state changes, but the description states mark_read:true flags events as read, which modifies state. Despite other behavioral details being clear, this contradiction violates the scoring rule, resulting in a score of 1.

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 long, front-loaded with the main action and return fields, followed by filter parameters and a note on polling. Every sentence adds value with no redundancy or filler.

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

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

The description covers return fields, filtering, and alternative access. Without an output schema, it provides enough detail for basic use. However, it does not mention pagination behavior beyond the limit parameter or describe the default ordering, which could be improved for full completeness.

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

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

Schema coverage is 100%, providing a baseline of 3. The description adds significant value by explaining each parameter with examples (e.g., 'sec_8k' for type, ISO timestamp for since) and clarifying the effect of mark_read and unread_only. This fully compensates for the baseline and exceeds expectations.

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

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

The description clearly states it pulls fired events from a subscription feed, listing specific fields (source, citation_uri, payload). It uses specific verbs and mentions filtering options, making its purpose distinct. However, it does not explicitly differentiate from sibling tools like list_subscriptions or recent_changes, which slightly reduces clarity.

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

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

The description explains when to use the tool (e.g., for polling alerts, filtering by type and timestamp, marking as read) and provides an alternative access method (GET /alerts.json). It lacks explicit 'when not to use' guidance or comparison to alternative tools, but the provided context is sufficient for typical 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?

Beyond annotations (readOnly, idempotent), description discloses data sources (SEC, GDELT→GNews fallback, USPTO), date formats, output structure, and known issues like PatentsView sunset. 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?

Front-loaded with natural language query examples, followed by concise technical details. Every sentence adds necessary information; no redundancy or filler.

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

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

Despite no output schema, description explains return structure (grouped by source, total_changes count, citation URIs). Thorough for a multi-source tool with complex behavior and no output schema.

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

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

Schema coverage is 100%, so baseline 3. Description adds value by specifying examples for 'since' ('30d', '1m'), explaining 'value' accepts ticker or CIK, and noting 'type' only supports 'company'. Compensates well 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 explicitly states the tool provides a 'change feed' combining SEC filings, news, and patents in one parallel call, with example queries like 'What's new with X'. It clearly distinguishes from sibling tool 'entity_profile' for static profiles.

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 multiple example use cases ('What's new with X', 'latest on Y') and explicitly instructs to use 'entity_profile instead when you want the static profile', giving 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?

Annotations provide idempotentHint, readOnlyHint, destructiveHint. Description adds key context: scoping by identifier, persistence differences for authenticated vs. anonymous (24h), which is beyond annotation info.

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

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

Concise paragraph, front-loaded with primary purpose, efficient use of sentences – each adds value. No fluff.

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

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

For a simple 2-param tool with no output schema, description covers all necessary context: purpose, usage, persistence details, and relation to sibling tools. 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 100% with clear descriptions of key and value. Description reiterates key-value pair but adds no new parameter-level details beyond examples.

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 'Save data the agent will need to reuse later' with specific verb (save) and resource (key-value pair). Distinguishes from siblings recall and forget by name.

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

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

Explicitly says when to use: 'when you discover something worth carrying forward... so you don't have to look it up again.' References related tools (recall, forget) but lacks explicit when-not.

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 safe, idempotent, non-destructive behavior. Description adds value by disclosing internal cascading lookups and detailed return structure (e.g., ticker+CIK+citation URI for company).

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

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

Well-structured with examples first, then usage instruction, then type details. Slightly lengthy but each sentence adds value; could trim some 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?

No output schema, but description adequately explains return values for both entity types, including citation URIs. Could mention additional fields or edge cases, but sufficient for a lookup tool.

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

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

Input schema has 100% description coverage with clear enum and string descriptions. The description adds further context for each type (e.g., 'accepts ticker, CIK, or company name') enriching the schema's 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 resolves names to canonical identifiers with specific examples ('What's the ticker for…'). It distinguishes from sibling tools like entity_profile by emphasizing its role as a first-step lookup.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID' and mentions it replaces manual lookups. Could improve by stating when not to use or naming alternatives, but current guidance is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that it probes via ai_visibility_check, ranks by score, and returns score/confidence/signal density per entity, providing behavioral context beyond annotations.

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

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

Two sentences plus a parenthetical use case. Front-loaded purpose, followed by mechanism and example. Every sentence contributes meaning without 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?

Covers input semantics, internal mechanism, and output format despite no output schema. Lacks details about score/signal density metrics but is sufficient for agent decision to invoke.

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

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

Schema coverage is 100% with good descriptions. Description enriches semantics by noting that the first entity is treated as the 'subject' for narrative and that context disambiguates common names, adding value beyond schema.

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

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

Explicitly states 'Compare AI visibility across multiple entities side-by-side' with specific verb-resource pair. Distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic).

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

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

Provides clear use case example ('competitive AI-marketing audits') but does not specify when not to use or contrast with alternatives like compare_entities. Implicit context from siblings is not explicit.

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

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

Annotations already declare readOnlyHint and idempotentHint, so the tool is safe. The description adds crucial behavioral context: partial failures, bundlephobia's first measurement taking 5-30 seconds, and that sources_failed lists timeouts. 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?

Description is well-structured and front-loaded with the core purpose. While it is somewhat long, every sentence adds value. Minor brevity opportunities exist (e.g., 'Returns a summary block...' could be shorter), but overall efficient.

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

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

All essential aspects are covered: purpose, use cases, ecosystem scope, expected output fields, handling of partial failures, and absence of output schema is compensated by describing return structure. No gaps given the tool's complexity and available annotations.

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

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

Schema coverage is 100% with clear descriptions for both 'package' and 'version'. The description adds no new parameter meaning beyond what the schema already provides (scoped packages, defaulting). Thus baseline 3 is appropriate.

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

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

The description clearly identifies the tool as a composite check for deciding whether to add an npm package, covering license, advisories, bundle size, etc. It distinguishes itself from sibling tools by its specific domain (npm dependencies) and composite nature.

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

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

Explicitly states when to use ('is X safe / popular / small' queries) and provides exclusion ('NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly'). Also explains graceful degradation for bundlephobia timeouts.

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 details beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and that passages include character offsets. Annotations already declare readOnlyHint and idempotentHint, which are consistent.

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

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

The description is a single paragraph that front-loads the core idea. It is concise yet comprehensive, covering purpose, usage, behavioral details, and limitations. Every sentence adds information 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?

Although there is no output schema, the description explains return values: top-N passages with character offsets and similarity scores. It also mentions pairing with another tool and provides a clear cap on input size. This is sufficient for an agent to understand behavior and output.

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

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

Schema has 100% description coverage, so baseline is 3. The description adds value by explaining that 'text' is already pulled by the agent, giving example queries for 'query', and clarifying 'limit' as 'top-N passages'. This enriches understanding beyond the schema.

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

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

The description clearly states that the tool performs semantic search inside a fetched record, using specific examples like SEC 10-K and articles. It also distinguishes itself from siblings by mentioning pairing with ask_pipeworx_grounded and implying a separate fetch step.

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 the record is too big to fit in the prompt. It explains the benefit (saves context, returns only relevant passages with offsets) and suggests pairing with ask_pipeworx_grounded, providing clear decision guidance.

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

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

Annotations already indicate non-read-only, idempotent, non-destructive behavior. The description adds critical behavioral details: OAuth requirement, returned subscription ID, delivery mechanisms (feed always on, optional email/sms/webhook), SMS rate limit (10/day), webhook HMAC signing and one-time secret, and auto-disable after 10 failures. 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 comprehensive but slightly long; however, every sentence provides necessary detail. It is well-structured with clear sections for purpose, return, requirements, types, and delivery. A minor reduction in verbosity could improve conciseness.

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

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

Given the tool's complexity (3 parameters, nested objects, multiple types and delivery options), the description covers all necessary context: return value (subscription ID), prerequisites, all subscription types with examples, delivery channels with constraints, and security details. No output schema exists, but return value is described. Annotations complement with idempotency and open-world hints.

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 enriches each parameter with examples and constraints. The 'type' enum is explained with examples; 'params' gets type-specific structures; 'delivery' details each channel's behavior, including phone verification and webhook signing. This adds significant 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 verb-resource action: 'Create a proactive monitoring subscription to a live-data event stream.' It distinguishes itself from siblings like list_subscriptions, unsubscribe, and recent_alerts by focusing on subscription creation.

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 instructions on when to use the tool, including prerequisites ('Requires a Pipeworx OAuth account'), supported subscription types with examples, delivery channel options, and constraints like SMS caps and webhook auto-disable. It also implicitly differentiates from alternative tools for listing or deleting subscriptions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing the output: 'Returns category-bucketed example questions... each with the exact tool + argument shape'. 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 somewhat verbose but well-structured with a front-loaded list of synonyms and clear explanations. Every sentence adds value, though could be slightly more concise.

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

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

Despite no output schema, the description fully explains the return value format and content. It covers the tool's role, usage, and expected results comprehensively for a single-parameter, non-destructive tool.

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

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

Schema coverage is 100% with a description for the single optional parameter. The description adds significant meaning by listing possible values and explaining the parameter's purpose as a 'focus area', which 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 it is an onboarding entry point for a new agent, returning category-bucketed example questions. It distinguishes from sibling tools by being the first tool to use when unfamiliar with Pipeworx.

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

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

Explicitly advises 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains how to use the optional topic parameter for focus. Provides clear context for when to use this tool.

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

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

Discloses ownership enforcement and deactivation vs. deletion beyond annotations, which already indicate non-destructive and idempotent 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?

Three sentences, each serving a purpose: action, ownership, and behavioral nuance. No redundancy.

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

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

Complete for a single-parameter tool with annotations; covers purpose, constraint, and behavioral impact without needing output schema.

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

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

Schema covers id with 100% description; description adds ownership context about the id parameter, enhancing understanding beyond schema.

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

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

Description clearly states 'Cancel a subscription by id,' specifying verb and resource, and distinguishes from siblings like subscribe and list_subscriptions.

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

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

Explicitly states ownership enforcement ('you can only cancel your own subscriptions') and explains the deactivation behavior, 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?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds rich behavioral context: the two processing paths (SEC EDGAR for financial, grounded pipeline for others), exact percent-delta math, and return structure (verdict, actual value, citation, reasoning). 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 comprehensive but somewhat verbose, with multiple clauses and embedded examples. It is front-loaded with usage phrases, but could be more concise. The bullet-like list for verdict values helps structure, but overall it could be tightened.

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

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

Given the absence of an output schema, the description thoroughly explains the return values (verdict, actual value, citation, reasoning) and the internal routing logic. With only 2 parameters and no nested objects, the description covers all necessary context for an agent to use the tool effectively.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value beyond schema by explaining the interplay between claim wording and tolerance_pct, and by providing concrete examples of claim strings. This extra context justifies a score of 4.

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

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

The description clearly states the tool performs natural-language claim verification against authoritative sources, with verb 'verify' and resource 'claim'. It provides strong usage hints via example phrases. However, it does not explicitly distinguish this tool from sibling tools like ask_pipeworx_grounded or deep_research, which may also handle factual queries.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also outlines two internal paths for company-financial vs. other claims. However, it lacks explicit 'when not to use' guidance or direct comparison to sibling alternatives.

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