Design Feeds

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds behavioral traits: default model (free), optional paid Anthropic probing, and return structure (per-model fields + combined). 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?

Four-sentence paragraph: purpose, default/API key usage, return format, use cases. Each sentence is informative with no redundancy or fluff.

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

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

Despite no output schema, description adequately explains the return structure. All parameters are described with added context. Covers key aspects 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 description coverage is 100% but description adds value: explains default for 'models', payment model for '_apiKey', and disambiguation role for 'context'. Exceeds baseline of 3.

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

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

The description explicitly states it probes LLMs for visibility scoring (0-100). It identifies the resource (LLMs), the action (probe), and the output format, clearly distinguishing it from sibling tools like '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 clear use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains default model and optional API key usage. Lacks explicit 'when not to use' but context is sufficient.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context about being a 'one fast call', working on every tier, and returning structured answers with citations. 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 detailed and well-structured with a clear front-loaded preference statement. It includes examples and sibling references. Slightly long but every sentence is 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 no output schema, the description explains the return format (structured answer with citation URIs). It covers usage context, routing logic, and examples, making it complete enough for an AI agent.

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

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

Schema covers all 6 parameters (question + 5 aliases) with descriptions, achieving 100% coverage. The description provides example values and explains aliases but does not add deep semantic value beyond what schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states that the tool routes factual questions to verified sources and returns structured answers with citations. It distinguishes itself from siblings like 'ask_pipeworx_grounded' and 'deep_research' by positioning as the default entry point.

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

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

Explicitly specifies when to use: for factual questions, current/historical data, and query types like 'what is', 'look up'. It also states when not to use: for broad/multi-part questions use deep_research, for hallucination-resistant answers use ask_pipeworx_grounded. Provides examples of appropriate queries.

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 behavioral details: costs one extra LLM call, refuses when data doesn't directly answer, returns specific response fields (answer, evidence, confidence, source, fetched_at, refusal_reason). 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 (~100 words), front-loaded with key purpose, then process, response format, usage guidance. Every sentence provides 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 the tool's complexity (routing among 4396 tools, multiple steps), the description covers purpose, process, response structure, refusal reasons, cost trade-off, and usage guidelines. No output schema exists, but return values are well described.

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 parameters, including aliases. Description does not add significant extra meaning beyond noting that the question is in natural language and listing aliases briefly.

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 is a hallucination-resistant answer mode for high-stakes reads, explains the process (same routing as ask_pipeworx but extracts only from tool result), and distinguishes from the sibling ask_pipeworx by noting cost and use cases.

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

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

Explicitly specifies when to use: when answers will be quoted, cited, or acted on, and the agent must not invent facts (financial, legal, medical, statements). Also says prefer ask_pipeworx for casual lookups.

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

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint false), the description discloses extensive behavioral details: low-confidence resolutions short-circuit with status:'low_confidence_match', closed markets return 'market_closed_or_inactive', wide-spread markets carry 'tradeability:"illiquid_wide_spread"', and it explains resolution-rule risk parsing and edge-case handling. No contradiction with annotations.

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

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

The description is well-structured with sections (RESPONSE SHAPES, RESOLVER CONTRACT, etc.) and front-loaded with the core purpose. However, it is quite long and could be more concise without losing essential detail. The structure helps readability, but brevity is sacrificed for completeness.

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

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

Given no output schema, the description thoroughly covers return shapes (market, analysis, evidence), critical fields (market_match_confidence, parent_event), error states (low_confidence_match, market_closed_or_inactive, illiquid_wide_spread), and resolution-rule risk. It feels complete for an agent to understand behavior and responses.

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?

Although the schema has 100% coverage, the description adds significant meaning: it explains that 'market' accepts slug, URL, or question text; describes 'depth' options as quick vs thorough; and details 'include_raw' with size implications and use cases. This enriches the schema beyond raw 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 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call,' specifying the verb (research), resource (Polymarket bet), and distinguishing it from sibling tools like polymarket_edges or ask_pipeworx by emphasizing a single-call, comprehensive data pull.

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

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

The description explicitly lists use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides context on when to inspect tradeability, resolution-rule risk, etc. However, it does not explicitly state when not to use this tool or name alternative tools, though the use cases are clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral details: for company type, it pulls latest 10-K data from SEC EDGAR/XBRL with off-calendar fiscal year handling; for drug type, it pulls FAERS counts. It also mentions sorting by primary metric and citation URIs.

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

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

The description is dense but front-loaded with example queries. Every sentence adds value, though it could be slightly trimmed without loss.

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, no output schema, and strong annotations, the description fully covers what the agent needs: entity types, data sources, sorting, and citation output. No gaps.

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

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

Schema coverage is 100% but the description adds examples ('AAPL','MSFT'; 'ozempic','mounjaro'), constraints (2–5 items), and explains what data each type returns. This goes beyond the schema descriptions.

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

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

The description starts with example queries and clearly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies the verb 'compare' and the resource 'entities,' and distinguishes itself from sibling tools like entity_profile and 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?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and notes it replaces 8–15 sequential lookups. It does not explicitly list when not to use it, but the context implies it is the optimal choice for comparisons.

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 substantial behavioral context beyond annotations: decomposes into facets, parallel routing to 4,396 tools, returns findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], never invents, and 15-60s expected time. 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 a single dense paragraph but front-loads critical information (account requirement, fallback tool). Every sentence adds value, though it is slightly long. Structure is logical: signup info, function, details, use cases, timing.

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

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

Given the tool's complexity and no output schema, the description covers the process, output format (findings packet with specific fields), limitations (gaps for non-catalog topics), and timing. It adequately informs the agent for decision-making.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra context: explains depth values (quick=3, standard=5 default, thorough=8) and clarifies that the question parameter accepts natural language and multi-part queries, reinforcing the tool's purpose.

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 ('Grounded multi-source research') and identifies the resource ('Pipeworx's 1102 STRUCTURED data sources'). It clearly distinguishes from siblings by stating it is NOT open-web search and contrasts with ask_pipeworx for single lookups.

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

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

Explicitly states when to use (broad/multi-part questions over structured data) and when not to (single lookups, breaking/current news). It provides fallback tool (ask_pipeworx) for unsigned users and for non-catalog topics, and notes depth plan requirements.

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, so the tool's safety profile is clear. The description adds behavioral detail about returning schemas and being ready to call directly, but nothing unexpected or 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 well-structured and concise. It starts with the core action, then provides usage guidance, and finally details the return value. 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?

Given there is no output schema, the description adequately explains what the tool returns ('top-N most relevant tools with names, descriptions, and full input schemas'). It covers the input parameter, examples, and usage context. Slightly missing details on error handling or edge cases, but still complete for the tool's purpose.

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 parameters. The description adds examples and clarifies that the query parameter accepts natural language, but does not add significant meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task' and lists specific domains (SEC filings, financials, etc.). It distinguishes itself from sibling tools by being a discovery/search tool for the ecosystem.

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 when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear guidance on when to use versus 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 readOnly, openWorld, idempotent, nondestructive. The description adds significant behavioral context: fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF; returns specific fields; mentions patent soft-fail due to API sunset; explains ticker vs CIK input. No contradictions.

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

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

The description is well-structured with examples upfront and a bullet-like list of returned data. It is somewhat verbose but still efficient, with each sentence adding information. Could be slightly trimmed but remains clear.

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

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

Given the tool's complexity and lack of output schema, the description covers return fields comprehensively (cik, recent_filings with URIs, fundamentals, patents, news, LEI). It notes limitations (patent sunset). However, it doesn't detail exact structure of all returned fields or pagination, but it is largely complete for a profiling 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 covers both parameters with descriptions (type enum, value string). The tool description adds that only 'company' type is supported, names are not accepted, and CIK should be zero-padded, adding value beyond schema. Schema coverage is 100%, so baseline is 3; the extra context raises it to 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 it provides a full cross-source profile of a US public company in one parallel call, using examples like 'tell me about X' and distinguishes from chaining individual lookups. It differentiates from sibling tools like compare_entities 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?

Explicitly advises to prefer this tool over chaining single-source lookups for holistic views, and notes that names are not supported, directing to use resolve_entity first. This provides 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?

Beyond annotations (readOnly, idempotent, openWorld), the description adds that it fetches directly and falls back to a proxy if blocked. This is valuable behavioral context. 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?

Two efficient sentences, front-loaded with the core action. 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 covers purpose, usage, and behavioral detail adequately for most use cases. Minor omission of return format or error handling 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?

Schema coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema parameter descriptions, which already define url, limit, and query adequately.

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 'Fetch and normalize any RSS / Atom / RDF feed by URL', specifying the action (fetch and normalize) and the resource (any feed). It distinguishes from sibling tools like list_feeds and read_feed by focusing on any URL.

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 list_feeds first for curated sources', providing a clear alternative. The mention of 'CF-robust' with fallback proxy informs when to use this tool over others.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds no extra behavioral detail beyond stating 'Delete', which matches 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 action, then usage context. No unnecessary words.

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

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

For a simple one-parameter tool with no output schema and clear annotations, the description fully covers purpose, usage, and related 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?

Schema coverage is 100% with parameter 'key' described as 'Memory key to delete'. Description does not add additional semantics beyond the schema.

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

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

The description clearly states 'Delete a previously stored memory by key', providing a specific verb and resource. It distinguishes from siblings like remember and recall.

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

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

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

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 false. The description adds behavioral details: fetches the page, extracts title/description/key links, emits standard markdown. No contradictions.

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

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

Three sentences: purpose, process, use cases. No fluff, every sentence adds value. Front-loaded with purpose.

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

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

Given simple inputs, no output schema, and no nested objects, the description covers what, how, and when to use. Siblings are many but no overlap.

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

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

Schema coverage is 100% with clear parameter descriptions (url, max_links with default/max). Description does not add new semantics beyond schema, 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 generates a production-ready llms.txt file for any URL, specifying the verb (generate), resource (llms.txt file), and scope (any URL). It distinguishes from siblings by being the only tool for this 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 lists three use cases: indexing a client's site, drafting own project, auditing competitors. It does not state when not to use or alternatives, but the use cases are clear enough.

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

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds that it returns specific fields but does not uncover new behavioral traits beyond what annotations provide. No contradictions.

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

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

The description is two sentences, front-loaded with the core purpose, followed by optional usage and a sibling reference. Every sentence is useful and 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 list tool with no output schema, the description adequately covers what it returns, filter options, and how to proceed for a single feed. It feels complete for an agent to invoke correctly.

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

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

Schema coverage is 100% with detailed descriptions. The description reiterates the filter options (category and keyword) but adds little new semantic meaning beyond the schema. 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 tool lists curated design & photography feeds and specifies the returned fields (id, title, category, source). It distinguishes from sibling read_feed by suggesting to pass an id to read_feed for a single feed.

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 optional filtering by category or keyword and directs to read_feed for a specific feed. While it doesn't explicitly exclude other siblings like fetch_feed, it provides sufficient context for when to use this tool versus 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 context about return fields but does not reveal any behavioral traits beyond what annotations cover. No contradiction.

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

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

Two sentences: first states purpose and output, second provides usage context. No filler, 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 list tool, the description covers purpose, return fields, and usage context. No output schema exists, but the fields are listed. Sibling tools provide broader context.

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

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

The only parameter, include_inactive, is fully described in the schema (100% coverage). The description does not add additional meaning beyond the schema's own 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 'List the caller's active subscriptions' and enumerates the returned fields. It distinguishes itself from sibling tools like subscribe (create) and unsubscribe (cancel) by focusing on reading existing subscriptions.

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

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

Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use the tool, though it could be more explicit about when not to use it.

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

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

Annotations provide destructiveHint=false, readOnlyHint=false, etc. The description adds behavioral traits beyond annotations: it is rate-limited to 5 per identifier per day, free, does not count against tool-call quota, and the team reads digests daily with signal affecting the roadmap. This gives the agent a clear understanding of the tool's behavior and limitations.

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 packed with essential information. It front-loads the primary purpose, then provides usage guidelines, tips, and limitations. Every sentence adds value with no redundancy or fluff. It is well-structured for an AI agent to quickly parse.

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

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

Given that there is no output schema, the description does not explain return values, but that is acceptable for a feedback tool where the return is likely a simple success message. The description covers what to include in feedback, how it is used (roadmap influence), limitations (rate limit, quota), and formatting guidelines. This is complete for the tool's purpose.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how to describe issues ('in terms of Pipeworx tools/packs') and what not to include ('don't paste the end-user's prompt'). It also elaborates on the enum values for 'type' and provides guidance for 'message' (be specific, 1-2 sentences, max 2000 chars). This enhances the schema's 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's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the verb (tell/send feedback) and the resource (Pipeworx team). It also lists specific use cases (bug, feature, data_gap, praise) which helps distinguish it from sibling tools that perform other actions.

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 the tool: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also provides a negative guideline: 'don't paste the end-user's prompt.' Additionally, it mentions rate limits and quota usage, offering clear context for when to choose this tool over others.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' 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 with no fluff: first defines output, second lists use cases and adds details about source, privacy, and caching. 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 read-only trending tool with one optional parameter and no output schema, the description covers purpose, usage guidelines, behavioral traits, and parameter advice. No gaps given the tool's simplicity.

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 (window) is fully covered in the schema with enum values and descriptions for each. The description reinforces the advice ('Shorter windows surface what's hot right now; longer windows show steady-state demand'), adding marginal value beyond the schema's full 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 returns 'top tools, top packs, and total call volume' over a recent window. It specifies the resource (trending data on Pipeworx) and action (returns), and the unique angle ('what other AI agents are calling') distinguishes it from sibling tools like ask_pipeworx or discover_tools.

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

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

The description lists three explicit use cases: discovering hot data sources, confirming a popular tool, and seeing if use case aligns. It provides context on caching and window selection but does not explicitly state when not to use the tool or contrast with 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 declare readOnlyHint, openWorldHint, idempotentHint, and nondestructive behavior. The description adds extensive behavioral context: explains that event walks child markets, checks monotonicity and partition_sum, topic searches related events with a Jaccard similarity filter, and describes fill_check against CLOB depth. It also mentions skipped_low_similarity and placeholder filtering. 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 information-dense and front-loaded with the requirement and main purpose. It uses clear section-like phrasing for semantic anchor, partition filter, fill check, etc. However, it is lengthy and could be more structured with bullet points or line breaks to improve readability. Still, 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?

Despite having no output schema, the description thoroughly explains the response structure including opportunities[], partition_check, and fill_check details. It covers edge cases like skipped_low_similarity and placeholder filtering. The tool has only 2 parameters with no enums or nested objects, so the description provides complete guidance 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 descriptions, but the description adds significant value beyond the schema: it clarifies that event is for single-event mode using a slug, topic for cross-event mode using a seed question, and explains the implications of each. It also reinforces that at least one is required. This goes beyond the schema's concise descriptions.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It explains two distinct modes (event and topic) and provides examples. The purpose is specific and well-defined, though it doesn't explicitly differentiate from sibling tools like polymarket_edges or polymarket_fill_risk, but the uniqueness is clear from the description.

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

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

The description explicitly requires one of 'event' or 'topic' and warns that calling with no args fails. It recommends event mode for specific markets and topic for cross-event scanning. However, it does not mention when not to use this tool compared to siblings, so the guidance is good but lacks exclusions.

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

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

The description adds extensive behavioral context beyond the annotations (readOnlyHint, idempotentHint). It details the three model families, edge computation, tradeable-edge filters, caching at 1h, diagnostics, and warning fields. 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 well-structured with numbered segments and bolded terms. It is front-loaded with the main action and every sentence provides useful information. Minor redundancy could be trimmed, but overall it is appropriately sized 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 9 parameters, no required, no output schema, the description is extremely thorough. It explains the response structure, segments, diagnostics, caching, and edge computation. The tool is complex, and the description covers all necessary details 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 coverage is 100% with good parameter descriptions. The tool description adds extra context for parameters like min_kelly, min_partition_leg_kelly, and tradeable-edge knobs, explaining their role in filtering opportunities. This exceeds the baseline of 3.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It uses a specific verb and resource, and the action is distinct from siblings like polymarket_arbitrage or polymarket_edge_tracker.

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 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' This provides clear usage context. However, it does not explicitly state when to avoid this tool in favor of alternatives like polymarket_arbitrage or polymarket_fill_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?

Annotations already indicate safe+idempotent. Description adds: snapshot gaps due to cache-misses, decay computed from daily closes, and response structure details (trend classification, lifespan_days). 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?

Contains essential info and is front-loaded, but somewhat verbose, especially the RESPONSE section mixed in the description. Could be more concise.

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

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

Covers all aspects: input, output structure (tracked, expired, snapshot_dates), limitations (TTL, intraday), and behavioral notes (cache misses). No gaps given 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?

Both parameters (days, window) are fully documented in schema; description adds defaults, clamp range, and explains window as 'snapshot family'. Also describes response fields, compensating for missing output schema.

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

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

Clearly states purpose: telemetry for edge persistence/decay from daily snapshots. Uses specific verbs (answers 'how long...'), distinguishes from sibling polymarket_edges by focusing on time-series rather than single snapshot.

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

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

Provides context on when to use (older edges are different trades) and limitations (60-day TTL, not intraday). Lacks explicit exclusion of alternatives but implies proper use cases.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the agent knows it's safe. The description adds valuable behavioral details: walks the ladder, explains basket mode logic, warns about thin legs and 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 detailed and well-structured, starting with purpose, then modes, return values, and usage guidance. Every sentence adds value, though it could be slightly more concise without losing clarity.

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

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

Given the complexity (two modes, four parameters, no output schema), the description is very complete. It covers behavior, edge cases, return values, and risks, compensating fully for the lack of an 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%, but description adds significant meaning: explains how size_usd is interpreted differently in single vs basket, side default logic, and market vs event distinction. This goes well beyond the schema descriptions.

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

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

The description clearly states the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It lists specific return fields, making the purpose unambiguous and distinct from siblings 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?

Explicitly states 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and explains why (partial fills convert arb to directional risk). Provides clear context for when to use and why alternatives may fail.

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?

Provides extensive behavioral context beyond annotations: details on modes, safety fields, compatibility warnings, temporal alignment, and leg-by-leg price matching. No contradiction with annotations.

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

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

Well-structured with clear sections (modes, response, safety fields), but slightly verbose. Every sentence earns its place, though some redundancy exists.

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 of cross-venue spread with multiple modes and safety fields, the description is thorough. Explains response structure even without 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%, but the description adds value by explaining the topic enum values and how explicit parameters override mapped sides. Could slightly benefit from more syntax details, but overall adds meaning.

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

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

Clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question. Distinguishes from siblings like polymarket_arbitrage by focusing on spread across two different prediction markets.

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

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

Explicitly describes two usage modes (topic shortcuts and explicit pairings) and explains when not to use via safety fields (compatibility_warning conditions). Warns that pre-mapped topics often yield nontradable results.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that it returns normalized items with specific fields, which is helpful but not essential beyond the annotations.

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

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

Two sentences efficiently convey the purpose, source, output, and optional filtering. No redundant information, and the key action is front-loaded.

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

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

For a read tool with complete schema and annotations, the description covers the return format and optional filtering. It could mention the limit default (already in schema) but is otherwise 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 parameter descriptions. The description reiterates the 'feed' id and 'keyword' filter but does not add new details beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'Read' and the resource 'curated design & photography feed', with a specific source (from list_feeds) and expected output (normalized items). This distinguishes it from siblings like list_feeds.

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 mentions the prerequisite 'from list_feeds' and optional filtering by keyword, providing some context. However, it does not explicitly state when to avoid using this tool or compare it to alternatives like fetch_feed.

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, destructiveHint=false, idempotentHint=true. The description adds context about scoping to user identifier, which annotations do not cover, and confirms the read-only 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?

Three sentences, each providing essential information: operation, use case, scoping, and pairing. No redundant words. Front-loaded with the core action.

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

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

For a simple tool with one optional parameter, no output schema, and clear annotations, the description covers all necessary context: retrieval, listing, scope, and relationships to other 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?

Schema provides 100% coverage for the single parameter 'key' with description. Description adds important semantic detail: omitting key lists all keys, and the key must have been saved via remember. This adds value beyond the schema.

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

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

The description states the exact action: retrieve a value previously saved via remember, or list all saved keys. It clearly distinguishes from siblings (remember, forget) by specifying the retrieve/list function.

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

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

Explicitly says when to use (look up context stored earlier) and when not to (avoid re-deriving). Mentions pairing with remember and forget, and scoping. Provides clear guidance.

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

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

Annotations already declare readOnly, openWorld, idempotent. Description adds valuable context: alerts are written by evaluator to persisted feed, mark_read causes next call to show only newer events, and returns raw event payload. No contradictions.

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

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

Three sentences: first states purpose, second details returned fields, third covers filtering and polling. No fluff, 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?

No output schema, but description explains return format (source, citation_uri, raw event payload). Covers filtering, polling, and HTTP alternative. Adequate for a read-only tool with good annotations.

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

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

Schema has 100% coverage with descriptions. Description adds meaningful details: type example 'sec_8k', explains mark_read behavior ('flag returned events read so the next call only shows newer ones'), and clarifies ISO timestamp usage.

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 the subscription feed, returns most recent alerts with specific fields (source, citation_uri, raw payload). It distinguishes from siblings like fetch_feed and read_feed by focusing on recent alerts from 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?

Provides clear context: filter by type and/or since, use mark_read to manage read status, mention polling works fine, and gives HTTP alternative. Lacks explicit when-not-to-use or direct sibling comparisons, but overall 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?

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds valuable behavioral context about fan-out to multiple sources, fallback logic, and soft-fail for USPTO. 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 a single paragraph that is front-loaded with example queries and covers all necessary details. While somewhat lengthy, it is efficient and every sentence adds value.

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

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

Despite lacking an output schema, the description thoroughly explains the return format (changes[] grouped by source + total_changes + citation URIs) and covers all aspects of the tool's behavior, making it complete 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?

The schema covers 100% of parameters with descriptions, but the description adds extra value by explaining the 'since' format (ISO or relative shorthand) and typical usage ('30d'), as well as clarifying 'value' accepts ticker or 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 clearly states the tool's purpose as a change feed for a company over a recent date window, listing specific data sources (SEC EDGAR, GDELT/GNews, USPTO) and differentiating itself from sibling tool 'entity_profile' which provides static profiles regardless of window.

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 example queries like 'What's new with X' and clearly states when to use this tool vs. 'entity_profile' for static profile. It also explains fallback behavior (GDELT→GNews) and notes about USPTO soft-fail.

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?

Disclosures beyond annotations: authenticated users get persistent memory; anonymous sessions retain for 24 hours. No contradiction with annotations (idempotentHint true, destructiveHint false). Adds context about scoping and lifetime.

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, well-structured paragraph with front-loaded main purpose. Every sentence adds value. No redundancy.

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

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

Complete for a simple memory tool: explains purpose, usage, persistence, pairing with siblings. No output schema needed. Covers all essential context.

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

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

Schema coverage is 100%, and the description reinforces the key-value concept with examples like 'subject_property', 'target_ticker'. The schema already has clear descriptions, but the description adds contextual meaning (e.g., what types of values to store). Not much new beyond schema, hence 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 saves data for reuse across conversations or sessions, using specific verbs like 'save data' and specifying the resource as key-value pairs. It distinguishes from siblings by referencing recall and forget.

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

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

Explicit guidance on when to use: 'Use when you discover something worth carrying forward' and provides alternatives: 'Pair with recall to retrieve later, forget to delete.' Also clarifies persistence behavior based on authentication.

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 (readOnlyHint, etc.) are provided. The description adds that each call cascades through several internal endpoints and details the return fields (ticker, CIK, RxCUI, citation URIs). 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 moderately concise with front-loaded examples and structured details. Each sentence contributes value, though slightly verbose in listing supported types.

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 rich annotations and full schema coverage, the description is complete. It explains what is returned for each type, when to use, and how it simplifies workflow. No output schema needed.

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

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

Schema coverage is 100%. The description expands on the 'value' parameter by giving examples per type and mentioning auto-disambiguation for company, adding meaningful context 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 resolves names to canonical identifiers, provides concrete examples (ticker, CIK, RxCUI), and lists supported entity types with specific outputs. It implicitly distinguishes from siblings by being the first tool for name-to-ID 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 advises 'Use FIRST whenever you have a name but need an ID' and notes it replaces multiple manual lookups. However, it does not explicitly contrast with sibling tools that operate on already-resolved entities.

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

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

The description adds valuable behavioral context beyond annotations: it reveals the internal probe mechanism using ai_visibility_check, ranking by score, and returning a ranked list with score, confidence, and signal density. This aligns with the readOnlyHint, idempotentHint, and destructiveHint 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: first states purpose, second explains the process, third gives use case and return format. Every sentence is informative and 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?

The description covers the tool's functionality well: it explains parameters implicitly, describes the return format (ranked list with score, confidence, signal density), and provides a concrete use case. Missing details like error handling or pagination are not critical 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 coverage is 100%, so the baseline is 3. The description adds meaning by noting that the first entity is treated as the 'subject' for narrative purposes and that the default model is 'workers-ai'. This extra context justifies a 4.

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

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

The description clearly states the tool's purpose: comparing AI visibility across multiple entities side-by-side. It uses specific verbs like 'compare', 'probes', 'ranks', and distinguishes from the sibling tool 'ai_visibility_check' by focusing on 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 provides a clear use case ('competitive AI-marketing audits') and an example question. It implies when to use this tool over single-entity alternatives, but does not explicitly state when not to use or list specific exclusions.

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

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

Discloses partial failure behavior ('sources_failed will list it'), timing caveat for bundlephobia first measurement (5-30s), and graceful degradation. Annotations already indicate read-only/idempotent, but description adds valuable runtime 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?

While somewhat verbose (single paragraph), the description is front-loaded with purpose and every sentence adds value. Could be slightly more concise but well-structured.

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

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

Without an output schema, the description thoroughly explains return values (summary block with all fields, per-advisory detail, links, alternative versions). Also covers edge cases (scoped packages, default version, partial failures).

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% with clear descriptions for both parameters. Description adds minimal extra beyond schema (e.g., version defaults to latest, scoped packages accepted). Baseline 3 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 is a composite check for adding npm packages, listing specific sources (deps.dev, bundlephobia) and return fields. It differentiates from siblings by focusing on npm ecosystem and being a one-call aggregation.

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 tells when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes ecosystem limitation (npm only) and suggests alternatives for other ecosystems (deps.dev:version).

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 beyond: embedding model (BGE-base-en), window size (500-char overlapping), max text length (200K chars with truncation flag), and offset feature for verification. No contradiction.

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

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

Description is a single paragraph but front-loaded with purpose. Every sentence provides distinct information. Could be slightly more structured but remains effective without excess.

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

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

Despite no output schema, description fully explains return format (top-N passages with offsets and similarity scores), mentions verification, and pairs with a sibling tool. Complete for a well-annotated tool.

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

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

Schema has 100% coverage with descriptions for all 3 parameters. Description reinforces but adds value with query examples and embedding context, helping the agent understand how parameters are used together.

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

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

The description clearly states the tool does 'semantic search INSIDE a fetched record', provides concrete examples (SEC 10-K, article), and distinguishes its use case from siblings by specifying when the record is too large for prompts.

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 cram into the prompt'), what it achieves (saves context, returns relevant passages), and pairs with sibling tool ask_pipeworx_grounded for grounding.

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=false, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds meaningful context: requires OAuth, subscription creation is mutating, delivery channel constraints (SMS cap, webhook auto-disable), and return of webhook secret once. 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 covers many details in a single paragraph. While informative, it lacks clear structure or bullet points that would improve readability. It could be more concise by separating type explanations and delivery options.

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, no output schema), the description fully covers return value (subscription id), explains all types and delivery channels, and addresses constraints (phone verification, SMS cap, webhook failure handling). It leaves no significant gaps.

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

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

Schema coverage is 100% (all parameters documented), and the description adds significant value beyond schema: explains each type's semantics (e.g., 'sec_8k' for 8-K filings with ticker and item codes), provides example params for each type, and details delivery channel behaviors like webhook HMAC signing and auto-disable.

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

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

The description clearly states the verb 'Create' and resource 'proactive monitoring subscription', and specifies the return value 'Returns the new subscription id.' It distinguishes from siblings like 'list_subscriptions' and 'unsubscribe' by focusing on 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 prerequisites (Pipeworx OAuth account) and examples for each supported type, guiding when to use each. However, it does not explicitly state when NOT to use this tool or mention alternative sibling tools for different scenarios.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive. The description adds value by detailing the return format and that it draws from a live catalog. 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 relatively long but front-loaded with key phrases and includes all necessary information. Every sentence serves a purpose, though a slight trim 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?

With no output schema, the description fully explains the return value: category-bucketed questions with exact tool/argument shapes. It also covers the live catalog aspect and use cases, making it 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 has 100% coverage for the single optional parameter 'topic', but the description adds practical context: listing example topics and explaining behavior when parameter is omitted. This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions. It specifies the exact output and distinguishes itself from similar tools by emphasizing it is the first tool to use when learning what Pipeworx can do.

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

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

The description provides explicit guidance: call with no arguments for full spread or pass topic to focus. It also advises using this tool first when unsure of Pipeworx capabilities. However, it does not explicitly mention when not to use it or list alternatives.

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

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

Annotations (readOnlyHint: false, destructiveHint: false, idempotentHint: true) are supplemented by description: 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts.' This adds crucial 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, front-loaded with purpose, 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?

Simple tool with one parameter and no output schema. Description covers purpose, ownership, and behavioral effect. Could mention success return type, but not necessary given 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 covers 100% of parameters with description of 'id'. Description adds 'returned by subscribe' hint, providing extra context on where to obtain the parameter 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?

Clearly states 'Cancel a subscription by id.' Specific verb (cancel) and resource (subscription). Distinguishes from sibling tools 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?

Explicit ownership constraint: 'you can only cancel your own subscriptions.' Does not explicitly state when not to use or name alternative tools, but the constraint provides clear guidance.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds rich behavioral context: returns verdict types, structured form, actual value with citation, percent delta, and states domain limitation. 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 well-structured, front-loaded with key examples and purpose, then domain and return details. It is fairly concise but includes substantial information; minor trimming possible, but overall effective.

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

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

Given the tool's complexity (claim verification with structured output) and absence of output schema, the description fully explains return values (verdict, extracted form, actual value, citation, delta) and covers purpose, domain, and efficiency benefits, leaving no gaps for agent decision-making.

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

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

Only one parameter 'claim' with schema coverage 100%. Description adds meaningful context with examples of natural-language claims (e.g., 'Apple's FY2024 revenue was $400 billion'), clarifying expected input format beyond the schema.

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

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

Description starts with natural-language examples (‘Is it true that…’, ‘fact check’) and clearly states the tool's purpose: natural-language claim verification against authoritative sources, specifically for company-financial claims, distinguishing it from sibling tools.

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

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

Explicitly states ‘Use whenever the agent needs to check whether something a user said is factually correct’ and specifies the domain (company-financial claims for public US companies), implying when not to use and highlighting efficiency gains over alternatives.

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