Cbioportal

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds that `_apiKey` is passed through to api.anthropic.com and that user pays Anthropic directly. Also explains scoring and raw response details. No contradictions.

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

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

Description is a single paragraph of 4-5 sentences, front-loaded with the core action and then details. No fluff, but could be slightly more compact. Still effective.

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

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

No output schema, but description clearly outlines return structure (per-model {score, confidence, signals, raw_response} + combined view). All 4 parameters are documented in schema. Could mention rate limits or pagination, but not necessary for this tool.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds context (default model is free, API key format example) but does not provide significant new meaning beyond the schema descriptions.

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

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

Clearly states it probes LLMs for what they know about an entity and scores visibility (0-100) per model. Specifies default model (Worker AI Llama-3.3-70b) and optional Anthropic. Distinguishes from sibling tools like 'ask_pipeworx' and 'scan_competitor_ai_presence'.

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

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

Provides use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use `_apiKey` (for Anthropic). Lacks explicit 'when not to use' or comparison to alternatives, but context is clear enough for an AI agent.

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

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

Description adds behavioral context beyond annotations: it routes to many tools (3,689 across 867 sources), returns structured answers with citation URIs. Annotations already declare readOnly, openWorld, idempotent. No contradictions.

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

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

Description is relatively long but each sentence is informative. Front-loaded with usage guidance. Could be slightly more concise, but all content is relevant and valuable.

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

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

Given the tool's complexity as a query router, the description fully covers its role, the types of questions it can handle, and the output format (structured answer with citations). No output schema, but description compensates adequately.

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 6 parameters (all aliases for question) with 100% coverage. Description adds examples and notes that the tool accepts natural language, but doesn't significantly enhance schema understanding beyond what schema already provides.

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

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

The description clearly states the tool's purpose: answering factual questions about current/historical data from authoritative sources, with a strong preference over web search. It lists specific domains and provides examples, distinguishing it from siblings like 'ask_pipeworx_grounded' and web search.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and gives when to use (factual, structured data) with examples. Also suggests using when user asks specific query types. Lacks explicit exclusions (e.g., opinions, creative tasks), but positive guidance 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?

The description adds significant behavioral context beyond annotations: explains the refusal mechanism with specific reasons, states it uses only tool result data, and notes the extra LLM call cost. No contradictions with annotations; all disclosures align.

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 key information front-loaded. While slightly long, every sentence provides useful context. Could be trimmed slightly but remains effective and readable.

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 specifies the output format (success and refusal objects with fields). It also covers the extra cost, routing process, and use cases. For a complex tool, the description leaves no major gaps in understanding what the tool returns and when to use it.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add parameter-level semantics beyond noting that arguments are filled, but the schema already documents the 'question' parameter and its aliases sufficiently. No additional value from 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 it is a hallucination-resistant answer mode for high-stakes reads, explicitly differentiates from the sibling 'ask_pipeworx' by noting the same routing but grounded extraction, and details the tool's function of picking and executing the right tool then extracting answers only from results.

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

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

Provides explicit when-to-use scenarios (answers to be quoted, cited, or acted on, and when the agent must not invent facts) and when-not-to-use (prefer 'ask_pipeworx' for casual lookups due to extra cost), with clear context for decision-making.

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

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

The description goes far beyond annotations, detailing fan-out behavior, response shapes, resolver contracts, parent event extraction, news fallback mechanisms, safety short-circuits, and liquidity warnings. No contradiction with annotations; all additional useful context.

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

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

The description is long but well-organized with clear sections (classifiers, fan-out examples, response shapes, safety). It is front-loaded with the main purpose. Minor redundancy could be trimmed, but structure is good for a complex tool.

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

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

Given no output schema, the description comprehensively covers all response fields, edge cases (closed markets, low confidence), and behavioral traits. It is complete enough for an agent to use without ambiguity.

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

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

Schema coverage is 100%, but the description adds value by explaining the effect of each parameter: 'quick' vs 'thorough' fan-out, and 'include_raw' summarizing vs full payloads. This enriches understanding beyond the schema definitions.

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

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

The description explicitly states it researches a Polymarket bet by pulling Pipeworx data in one call. It provides specific use cases (should I bet on X, what does data say, is there edge) and distinguishes from siblings like polymarket_edges by focusing on comprehensive evidence gathering.

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

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

The description gives clear usage examples ('Use for "should I bet on X"...') and implies when to use, but does not explicitly state when not to use or how it differs from alternatives. However, the context is clear enough for an agent to decide.

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

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

Adds specific behavioral details beyond annotations: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return of citation URIs. Consistent with readOnlyHint and openWorldHint.

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

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

While lengthy, every sentence adds value. Front-loaded with example queries and clear purpose. No redundancy.

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

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

Given sibling tools like entity_profile for single lookups, the description fully explains the comparison tool's purpose, data sources, constraints, and output format. No missing context.

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

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

Schema coverage is 100%, but description adds significant meaning: explains what each type returns and provides examples for values (tickers/CIKs for company, names for drug). Clarifies min/max items.

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 performs side-by-side comparison of 2-5 companies or drugs, with specific examples of queries. It distinguishes from sequential single-pack lookups and explains what data is returned for each entity type.

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 instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Also provides context on when to use company vs drug type.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe, non-destructive behavior. The description adds valuable behavioral context: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This clarifies the output format and readiness for invocation, going beyond the annotations. No contradictions.

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

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

The description is three sentences long, with the main purpose stated in the first sentence. Every sentence is informative and necessary: the first states the core function, the second lists examples of use cases, and the third explains the output and when to call it. 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?

The tool has no output schema, but the description compensates by explaining what is returned: 'names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly.' It also gives a clear usage context ('Call this FIRST'). This is complete enough for a discovery tool, though it could mention that the output includes examples, which it does. Lacks only a brief note on pagination or error handling, but that is minor.

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

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

The input schema has 100% description coverage, with each parameter (including aliases) documented. The description does not add significant new meaning beyond the schema, as it only briefly mentions 'top-N' and does not elaborate on parameter usage. With high schema coverage, a score of 3 is appropriate as the description adds minimal extra semantic value.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It uses specific verbs like 'find', 'browse', 'search', 'look up', and 'discover' to describe actions, and specifies the resource ('tools'). It also distinguishes itself from sibling tools by positioning itself as a starting point for exploring the tool set.

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

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

The description explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives clear guidance on when to use the tool and implies it is not meant for returning a single direct answer. However, it does not explicitly mention alternatives or when not to use it in all cases, so it is not a perfect 5.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context beyond annotations, such as the USPTO PatentsView API sunset and soft-fail behavior, and the fanned-out parallel call nature. No contradictions.

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

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

The description is a single dense paragraph that contains all necessary information but could be more structured (e.g., using bullet points). It is front-loaded with example queries, but its length reduces 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?

Despite having no output schema, the description explains the return structure in detail: CIK, company_name, recent_filings with URIs, fundamentals with data fields, patents, news, LEI. For a complex multi-source tool, this provides sufficient context for an agent to understand what to expect.

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

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

Schema coverage is 100%, so the schema already documents both parameters. The description reinforces the accepted formats (ticker or zero-padded CIK) but doesn't add 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 it provides a 'full cross-source profile of a US public company' and lists specific data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF) and return fields. It distinguishes itself from sibling tools like resolve_entity by explicitly noting that names are not supported.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides concrete negative guidance: 'Names not supported — use resolve_entity first if you only have a name.' Example queries are given.

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 destructiveHint=true and idempotentHint=true, covering safety and idempotency. The description adds 'delete' which aligns with destructiveHint but does not disclose additional behavioral traits beyond what annotations convey.

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

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

The description is two sentences, front-loaded with purpose and usage context, with no redundant words. Every sentence adds value.

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

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

For a simple tool with one parameter and no output schema, the description provides sufficient context: what it does, when to use, and related tools. No gaps.

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

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

Schema coverage is 100% and the parameter description 'Memory key to delete' is already in the schema. The tool description mentions 'by key' but adds no further meaning or constraints beyond the input schema.

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

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

The description uses the specific verb 'delete' and clearly identifies the resource 'a previously stored memory by key'. It distinguishes from sibling tools like 'remember' and 'recall' by stating the action and pairing suggestion.

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

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

The description explicitly states when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also directs to pair with remember and recall, but does not provide explicit exclusions or when not to use.

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true, etc. The description adds valuable context: fetching the page, extracting title/description/key links, output format. 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 three sentences: purpose, process, use cases. Every sentence earns its place with no wasted words. Front-loaded with key 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 tool with 2 parameters and no output schema, the description explains the output format (single text blob, standard llms.txt). It is self-contained but could mention error handling (e.g., invalid URL).

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 examples (e.g., 'Full URL of the site to summarize, e.g. https://example.com') but does not provide additional meaning beyond what the schema already states.

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

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

The description uses a specific verb ('generate') and resource ('llms.txt file'), and clearly states the action: fetches page, extracts info, emits markdown. It distinguishes itself from siblings like scan_competitor_ai_presence by focusing on generation rather than scanning.

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 explicit use cases (indexing client's site, drafting own llms.txt, auditing competitor). However, it does not mention when not to use it nor alternatives, so it falls short of the top score.

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

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

Annotations already indicate readOnlyHint, idempotentHint, openWorldHint, so behavioral safety is clear. The description adds context by specifying the source (cBioPortal) and the exact return fields, enhancing transparency 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 extremely concise with two sentences, front-loaded with the core purpose, and contains no unnecessary information.

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

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

Despite lacking an output schema, the description clearly states the return fields (entrez_gene_id, symbol, type). For a simple lookup tool, this is complete and sufficient.

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

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

The input schema covers all parameters with 100% description coverage, so the description adds no additional semantic value beyond what is already in 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?

The description uses a specific verb 'Resolve' and resource 'gene to its Entrez gene id and canonical info', clearly identifying the tool's function. It distinguishes from siblings like 'resolve_entity' by specifying the database (cBioPortal) and the output fields.

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 states acceptable inputs (HUGO symbol or Entrez gene id) but does not explicitly mention when to avoid this tool or provide alternative tools. Usage is implied by the description of its capabilities.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context on returned fields (description, cancer type, sample count, PMID, citation), enriching behavioral understanding 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?

Single sentence, front-loaded with purpose, no unnecessary words. Efficiently communicates the tool's function and expected input.

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 lists key return fields (description, cancer type, sample count, PMID, citation). It sufficiently covers the scope for a read operation, though could mention additional potential fields.

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

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

Schema coverage is 100% and describes study_id with example. The description reinforces with additional parameter examples (e.g., 'glioma_mskcc_2019'), adding semantic value beyond the schema alone.

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

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

The description explicitly states it gets full details for a cBioPortal cancer study by study id, using specific verb-resource combination. It provides example IDs like 'brca_tcga_pub', distinguishing it from sibling tools like search_studies.

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 usage context: when you need full details for a known study ID. It provides examples but does not explicitly state when not to use it or mention alternatives like search_studies for queries without a specific ID.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds transparency by stating the output includes id, display name, and parent type. 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 two concise sentences, front-loaded with the main action and output, followed by a usage hint. 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?

For a simple listing tool with no output schema, the description partially compensates by naming the output fields. It does not specify sorting or pagination details, but the tool's simplicity means this is likely 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?

The only parameter (limit) has 100% schema coverage with a clear description of its default and max. The tool description does not add extra semantics, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool lists cancer types with their id, display name, and parent type, and provides a specific use case (resolving IDs used by studies). This distinguishes it from all sibling tools, none of which list cancer types.

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

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

The description explicitly mentions the tool is useful for resolving cancer-type IDs used by studies, providing clear context. It does not exclude any scenarios or compare to alternatives, but the context is clear and sufficient for the agent.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds return fields and the include_inactive parameter behavior, but does not go beyond what annotations imply.

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 short sentences that immediately convey the tool's purpose and key details. No wasted words.

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

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

Given the simple tool with one optional parameter and no output schema, the description adequately covers returned fields and usage context.

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

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

Schema coverage is 100% for the single parameter. Description does not add extra meaning beyond listing fields, so baseline 3 applies.

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

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

The description clearly states the verb 'list' and the resource 'subscriptions' with scope 'caller's active subscriptions'. It also lists returned fields, making the purpose unambiguous.

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

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

Provides explicit use cases: review before adding more or to find an id to cancel. Does not mention alternatives but is sufficient for common 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?

The description reveals rate-limiting (5 per identifier per day) and that feedback is read daily, adding behavioral context beyond annotations. Annotations indicate non-read-only, non-idempotent, non-open-world, non-destructive, which aligns with a feedback submission tool. 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 concise, front-loaded with the purpose, and covers all essential aspects in a few sentences. Every sentence serves a purpose with no wasted words.

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

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

For a simple feedback tool with no output schema, the description covers purpose, usage, constraints, and parameter details. It mentions that feedback is read daily, which sets expectations. Missing explicit mention of what the agent can expect after submission (e.g., confirmation), but overall complete.

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

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

Schema description coverage is 100%, baseline 3. The description adds extra value by elaborating on enum values (bug, feature, data_gap, praise, other) and providing formatting guidelines for the message (be specific, 1-2 sentences, 2000 chars max). This goes beyond the schema.

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

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

The description clearly states the tool's purpose: to tell the Pipeworx team about bugs, missing features, data gaps, or praise. It provides specific examples and distinguishes from other tools by focusing on feedback. The verb 'tell' and resource 'Pipeworx team' are 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 explicitly lists when to use the tool (bug, feature/data_gap, praise) and provides guidance on how to describe the issue (in terms of Pipeworx tools/packs, not end-user prompt). It also mentions rate limits (5 per day) and states it's free and doesn't count against quota, implicitly saying when not to overuse.

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, non-destructive. Description adds valuable context: data source (CF analytics-engine), no PII, cached 5min-1h. 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?

Concise, well-structured: opening sentence, bullet-like list of use cases, then technical details. Every sentence adds value without redundancy. Appropriately sized for a simple tool.

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

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

Given low complexity (1 optional param, no output schema), description covers all aspects: what it returns, parameter behavior, caching, and data source. No missing information for correct invocation.

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

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

Schema coverage is 100% with one parameter and enum. Description adds interpretive guidance ('Shorter windows surface what's hot right now; longer windows show steady-state demand.'), 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?

The description clearly defines the verb (returns) and resource (top tools, top packs, total call volume) with specific scope (over recent window). It distinguishes itself from siblings by focusing on trending aggregation, not individual tool details.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical tool, seeing alignment). While it doesn't directly say when not to use, the context is clear. Minor improvement could include mention of alternatives like discover_tools or entity_profile.

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

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

Beyond annotations (readOnlyHint, etc.), the description details monotonicity violation checks, partition-sum calculations, similarity thresholds (Jaccard ≥0.30), placeholder filtering, and the response structure. This provides rich behavioral insight beyond what annotations convey.

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

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

The description is detailed but well-structured: opening requirement, mode explanations, technical details, and response format. It could be slightly more concise, but every sentence adds essential 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 the tool's complexity, the description covers all necessary aspects: required parameters, mode-specific behavior, algorithmic checks (similarity, partition filter), and response fields. Despite lacking an output schema, the description adequately documents the return structure.

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

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

With 100% schema coverage, the description adds value by explaining each parameter's role (single-event vs. cross-event mode) and providing examples of valid inputs (slugs, URLs). It clarifies that 'topic' is for scanning related events, which goes beyond the schema's brief 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 finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with specific examples, making the purpose precise and distinct from sibling tools.

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

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

It explicitly requires one of 'event' or 'topic' and describes when to use each: event for specific markets, topic for cross-event scanning. It notes that calling with no args fails, providing clear usage context, though it doesn't explicitly exclude other 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds rich behavioral details: cached 1h at KV level, edge calculation net of slippage, Kelly fractions, 24h-move warning, filter skips, and diagnostics. 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 lengthy and dense, with information packed into continuous paragraphs and inline bullet lists. It is front-loaded with the main purpose but could be more structured (e.g., clear sections) for quicker scanning. Every sentence adds value but not optimally 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?

Since no output schema is provided, the description fully compensates by detailing response structure: by_segment segments, fed_candidates/fed_note, _diagnostics with funnel counters and filter skips. It explains caching and per-opportunity fields, making the tool's behavior self-contained and complete.

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

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

Schema coverage is 100% with descriptions for all 9 parameters. The description adds practical usage notes beyond schema, e.g., slippage_pp suggests bumping/dropping based on fill model, and explains min_partition_leg_kelly interaction with partition_overround. This enriches understanding despite high schema 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?

The description clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, and specifies it's built for discovering what to bet on. It distinguishes from siblings like polymarket_arbitrage by its unique segmentation (model-driven, structural arbitrage, concentrated longshot) and inclusion of diagnostics.

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 extensive guidance: when to use (opportunity discovery without paging), knobs like min_liquidity and max_spread_pp, Fed bets caveat, and cache behavior. However, it lacks explicit when-not-to-use or comparisons with siblings like polymarket_arbitrage, relying on implied differentiation.

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

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

The description provides extensive behavioral details beyond annotations, including safety fields (compatibility_warning, temporal_alignment, skipped_cross_type/subtype) and explanations of when spreads are meaningful. It aligns with annotations (readOnlyHint, etc.) and adds critical context for interpretation.

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 and packed with technical details. While all information is relevant, it could be more concise. The front-loading is effective, but some redundancy exists (e.g., repeated warnings about compatibility).

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 comprehensively explains the response structure (leg-by-leg prices, spread field, safety fields) and caveats like temporal alignment and skipped comparisons. Without an output schema, this level of detail ensures the agent can correctly interpret and act on the results.

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

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

Input schema covers all 3 parameters with descriptions. The description adds value by explaining the two modes, the topic shortcuts list, and that explicit parameters override topic mapping. This exceeds the schema's basic definitions.

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

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

The description clearly states it calculates cross-venue spreads between Kalshi and Polymarket. It specifies two operational modes (topic shortcuts and explicit event pairing) and distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on venue comparison.

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

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

The description explains when to use the tool for comparing spreads across venues. It includes caution that most pre-mapped topics currently return compatibility warnings, guiding the agent to interpret results realistically. However, it does not explicitly list when not to use it or suggest alternative tools for single-venue analysis.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds scoping ('identifier') and pairing behavior, but does not reveal additional behavioral traits beyond what annotations imply.

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, no fluff, front-loaded with the action. Every 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 the simplicity of the tool, no output schema, and annotations covering safety, the description fully explains both use cases (retrieve by key, list all keys) and scoping.

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 key parameter. The description adds context that omitting the key lists all keys, enhancing understanding beyond the schema alone.

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

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

The description states 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument)', which gives a specific verb+resource and distinguishes 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?

The description explicitly says 'Use to look up context the agent stored earlier' and 'Pair with remember to save, forget to delete', providing clear when-to-use and alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: the mark_read parameter causes state mutation (flagging events as read), polling is fine, and the feed is mirrored externally. No contradictions with annotations.

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

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

The description is a single, well-structured paragraph. It front-loads the purpose, then details return fields, parameter usage, and additional notes in logical order. Every sentence adds value without redundancy.

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

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

For a tool with no output schema, the description compensates by explaining that returned events carry 'source, citation_uri, and raw event payload.' It also covers mark_read behavior and polling suitability. However, it lacks details on error handling, rate limits, or parameter constraints beyond schema defaults. Still, it provides sufficient context for an agent to use the tool correctly.

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

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

Schema coverage is 100% with all parameters described. The description adds extra meaning by providing usage examples: 'Filter by type (e.g. 'sec_8k') and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones.' This enhances the schema descriptions, though limit and unread_only are not elaborated.

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 begins with a clear verb+resource: 'Pull fired events from your subscription feed.' It distinguishes itself from sibling tools like 'list_subscriptions' by focusing on events rather than subscription management. The specificity of 'recent alerts' and reference to 'subscription feed' makes the purpose unmistakable.

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

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

The description states when to use the tool: to retrieve recent alerts from the subscription feed. It provides a clear alternative for scripts/dashboards ('the same feed is also at GET registry.pipeworx.io/alerts.json'), implying when not to use the tool (for programmatic access outside the agent). However, it doesn't explicitly compare to other MCP tools like 'recent_changes', leaving some ambiguity.

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

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

Annotations indicate read-only, open-world, idempotent, and non-destructive. The description adds detailed behavioral context: fans out to multiple sources, GDELT→GNews fallback with rate-limit handling, USPTO patent soft-failure due to API sunset. This enriches the annotation signals without contradiction.

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

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

The description is comprehensive and front-loaded with example queries, but it is relatively long. However, every sentence serves a purpose—explaining scope, fallback behavior, parameter details, and alternatives—so it earns a 4 for being efficient 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 multi-source complexity and no output schema, the description fully covers the output structure (structured changes grouped by source, total_changes count, citation URIs) and constraints (USPTO soft-failure, GDELT preference). No gaps remain for an agent to select or invoke the tool correctly.

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

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

Schema coverage is 100%, and the description enhances parameter understanding: explains 'since' accepts ISO dates or relative shorthand ('7d', '30d', '3m', '1y') and recommends '30d' or '1m'. Clarifies that 'type' is limited to 'company' and 'value' can be ticker or zero-padded CIK.

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

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

The description uses multiple example queries to illustrate the tool's purpose ('What's new with X', 'latest on Y') and explicitly states it provides a change feed for a company aggregating SEC filings, news, and patents. It distinguishes from sibling 'entity_profile' by noting that tool is 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?

The description explicitly advises when to use this tool vs. 'entity_profile' ('Use entity_profile instead when you want the static profile...'). It also provides typical monitoring recommendation for the 'since' parameter ('Use "30d" or "1m" for typical monitoring.'), giving clear usage context.

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

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

The description adds value beyond annotations: it explains key-value scoping by identifier, persistence for authenticated (persistent) vs anonymous (24 hours) sessions. This complements the idempotentHint and destructiveHint annotations. No contradiction.

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

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

The description is efficient: 4 sentences front-loaded with purpose, no redundant information. Every sentence adds value.

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

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

Given the tool has only 2 parameters with full schema coverage and no output schema, the description is complete. It explains scoping, retention, and relationships with recall/forget.

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

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

Schema coverage is 100% (both key and value are described with examples). The description repeats 'key-value pair' but adds no new information about parameter semantics beyond the schema.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later' and distinguishes it from siblings like recall and forget by mentioning pairing. The verb 'save' and resource 'data' are 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?

Describes when to use: 'when you discover something worth carrying forward... so you don't have to look it up again.' It also mentions pairing with recall and forget, but does not explicitly state when not to use or provide alternatives beyond those siblings.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive. The description adds value by noting internal cascading, auto-disambiguation, and return of citation URIs, going 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 moderately long but well-structured with example hooks and clear sections. Every sentence adds value; no wasted words.

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

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

Despite no output schema, the description fully explains return values for both entity types, including citation URIs. It covers all necessary context for a tool with two parameters and clean 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 parameter descriptions. The description enriches by giving concrete examples for each type and detailing what outputs are returned (ticker, CIK, RxCUI, etc.), which is not in the schema.

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

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

The description clearly states the tool resolves user-spoken names to canonical/official identifiers, with example queries. It distinguishes from siblings like compare_entities and entity_profile by focusing on identifier 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?

The description advises 'Use FIRST whenever you have a name but need an ID,' providing a clear usage context. It doesn't explicitly state when not to use it, but the guidance is sufficient.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. No contradiction with annotations.

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

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

The description is two sentences with no wasted words. It front-loads the core purpose and then adds relevant context. Every sentence earns its place.

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

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

Given the complexity (4 parameters, no output schema), the description is complete. It explains the tool's mechanism (probes each entity with ai_visibility_check), what the output contains (ranked list with score, confidence, signal density), and the use case. No gaps remain.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds extra meaning: it clarifies that the first entity in 'entities' is treated as the 'subject' for narrative and the rest as competitors. This goes beyond the schema's description.

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

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

The description uses specific verbs like 'compare', 'probes', 'ranks', and 'surfaces' to clearly state the tool's function. It distinguishes itself from sibling tools like 'ai_visibility_check' by emphasizing side-by-side comparison across multiple entities.

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

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

The description explicitly states when to use this tool: for competitive AI-marketing audits. It provides a concrete example question: 'does Claude know about us as well as our competitors?' and implies not to use for single entity checks (use ai_visibility_check instead).

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

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

The description discloses behavioral traits beyond annotations: partial failures (bundlephobia timeout), graceful degradation with 'sources_failed', and composite nature. No contradiction with annotations (readOnlyHint, idempotentHint).

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

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

The description is detailed but each sentence adds value. It is somewhat lengthy but well-structured with front-loaded purpose and clear sections. Could be slightly more concise but still earns a 4.

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 format including fields, per-advisory details, links, and alternative versions. It also covers error handling and partial failures, making it complete.

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

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

Schema coverage is 100%, and description adds no new parameter info beyond what's already in schema descriptions. Baseline 3 is appropriate; no extra value added.

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: 'Composite should I add this npm package to my project check in ONE call'. It specifies the resources checked (deps.dev and bundlephobia) and distinguishes it from siblings (no direct competitors).

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 usage guidance is given: 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. It also notes limitations ('NPM ecosystem only in v1') and provides fallback instructions for other ecosystems.

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 specifying the return fields and emphasizing 'Keyless, open data', which provides additional behavioral context beyond annotations.

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

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

Two concise sentences, front-loaded with the purpose, no unnecessary words. Every sentence adds value.

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

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

For a simple two-parameter tool with no output schema, the description explains the return fields and behavior adequately. No gaps are apparent given 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 description coverage is 100%, so the schema already documents both parameters. The description adds meaning by explaining how the query parameter matches (study name, id, cancer type) and the default/max for limit, complementing the schema.

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

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

The description clearly states it searches public cancer genomics studies by keyword, matching study name, id, or cancer-type id. It distinguishes from siblings like get_study and list_cancer_types by specifically mentioning the search scope.

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

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

The description provides clear usage context (keyword search, case-insensitive, returns multiple fields) and implies it's for exploration. It does not explicitly state when not to use or mention alternatives, but the context signals are sufficient.

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

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

Beyond annotations (which already indicate read-only, idempotent, non-destructive), the description adds crucial behavioral details: uses BGE-base-en embeddings with cosine similarity, 500-char overlapping windows, and a 200K char limit with truncation flag. This fully informs the agent of the tool's internal 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?

The description is concise yet packs all necessary information: purpose, use case, pairing, algorithm, limits, and output details. Every sentence adds value, and it is front-loaded with the core 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?

Despite lacking an output schema, the description clearly states the return format: top-N passages with character offsets and similarity scores. It also explains the truncation behavior and the pairing pattern, making the tool fully understandable.

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

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

Schema coverage is 100%, but the description adds valuable context beyond the schema: it clarifies the 'text' max size (~200K chars), gives example queries for the 'query' parameter, and specifies the 'limit' default (5) and range (1-20). This enhances 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 performs semantic search inside a fetched record, with specific examples like 'search inside a SEC 10-K body or an article'. It distinguishes itself from sibling tools like ask_pipeworx_grounded by explaining the pairing and use case.

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

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

The description explicitly advises using this tool when the record is too big to fit in the prompt, saving context. It also mentions pairing with ask_pipeworx_grounded. However, it does not explicitly state when not to use it, which would strengthen guidelines further.

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 rich behavioral context beyond annotations: OAuth requirement, delivery channels, rate limits (SMS 10/day), webhook signing, auto-disable. No contradiction with annotations.

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

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

Well-structured and front-loaded, but slightly verbose; every sentence adds value but 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?

Covers requirements, types, channels, and return value. Minor gap: no mention of idempotent behavior or exact return format beyond 'subscription id'.

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

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

Schema coverage is 100%, and description enriches each param with concrete examples and constraints (e.g., type-specific filter format, delivery verification).

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

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

The description clearly states it creates a subscription, returns an ID, and differs from siblings like unsubscribe 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?

The description implies when to use, but does not explicitly exclude cases or compare directly with siblings like recent_alerts or 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?

The description adds significant behavioral context beyond annotations, such as being the onboarding entry point, returning categorized examples, and that calling with no arguments gives a full spread. It does not contradict any 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 thorough but slightly verbose. It front-loads common user queries, which is helpful, but could be tightened slightly without losing meaning.

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

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

Given the simple parameter schema and no output schema, the description is complete. It covers purpose, usage, parameter options, and output nature adequately.

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

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

Schema coverage is 100% and the description adds practical guidance: 'Call with no arguments for the full spread, or pass `topic` to focus.' This clarifies parameter usage beyond the schema.

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

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

The description clearly states the tool's purpose: it returns category-bucketed example questions with exact tool+argument shapes. It distinguishes itself from siblings by being the explicit onboarding entry point.

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

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

The description explicitly states when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains the optional topic argument but does not mention alternatives or when not to use.

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

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

Description adds significant value beyond annotations: ownership enforced, row deactivated not deleted, historical events remain via recent_alerts. No contradiction with annotations (idempotentHint true, destructiveHint false).

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

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

Two sentences with zero waste, front-loaded with purpose. Every sentence adds value: action, ownership constraint, and side effect.

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?

Tool is simple with one parameter and no output schema. Description covers behavior, constraints, and side effects completely, sufficient for 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 100% of parameters (id) with description matching the tool's purpose. Description doesn't add new meaning beyond schema, achieving 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?

The description clearly states it cancels a subscription by id, with a specific verb 'cancel' and resource 'subscription'. It distinguishes from sibling tools like 'subscribe' (create) and 'list_subscriptions' (list) by including ownership and deactivation details.

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

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

The description explicitly says when to use (cancel a subscription by id) and includes ownership enforcement. While it doesn't explicitly say when not to use, the context is clear for a simple tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false appropriately. The description adds valuable behavioral context: it returns a verdict with four possible outcomes, a structured form, actual value with citation, percent delta, and notes the underlying data sources. It also mentions v1-specific limitations, which is transparent.

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 (three sentences) and front-loaded with example phrasings. Every sentence serves a purpose: examples, usage context, domain specification, and output description. 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?

Despite lacking an output schema, the description fully explains what the tool returns (verdict, extracted form, value, citation, delta). It covers purpose, domain, usage, and even quantifies the efficiency gain (replaces 4-6 calls). Complete for the tool's complexity level.

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

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

The single parameter 'claim' has 100% schema description coverage. The description adds meaning by providing concrete examples and clarifying that it accepts natural-language factual claims, reinforcing the schema's intent.

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 (verify/validate), resource (natural-language claims), and scope (company-financial claims for public US companies via SEC EDGAR + XBRL). It provides multiple example phrasings and distinguishes the tool as a replacement for multiple sequential calls, though not explicitly differentiating from siblings.

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' and specifies the domain. However, it does not explicitly state when not to use or name alternative tools, leaving some room for ambiguity.

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