Open Library

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

Annotations already declare read-only, idempotent, non-destructive. Description adds transparency about Anthropic dependency (BYO key) and per-model return structure, which is consistent 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?

Single, well-structured paragraph with front-loaded 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?

Explains return format (per-model object + combined view) despite no output schema. Covers complexity of multiple models and optional parameters. Schema coverage 100% helps completeness.

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

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

Schema covers 100% of parameters with descriptions. Description adds minor context (e.g., context helps disambiguate, _apiKey passed through) but does not significantly enhance beyond schema.

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

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

Description clearly states it probes LLMs for knowledge about an entity and scores visibility (0-100). Specifies default model and optional Anthropic, distinguishing it from sibling tools like scan_competitor_ai_presence or entity_profile.

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

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

Provides context on when to use (AI-marketing audits, pre-launch checks, competitive monitoring) and explains model selection and API key requirements. Lacks explicit when-not-to-use but clear usage scenario.

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?

Despite no annotations, the description explains that Pipeworx picks the right tool, fills arguments, and returns the result, covering the core behavior and listing over 300 sources. It lacks explicit mention of side effects but implies read-only operation.

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

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

The description is comprehensive but slightly verbose with the full list of sources and examples. However, it front-loads the purpose and usage, making it efficient for an agent to parse.

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

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

Given a single parameter and no output schema, the description sufficiently explains the tool's function, usage, and examples. It could mention error handling or ambiguity, but overall it is complete for this simple 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% for the single 'question' parameter. The description adds value by providing numerous example questions and clarifying the expected input format, going beyond the schema's short description.

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

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

The description clearly states it answers natural-language questions by routing to the right data source, with specific examples like 'What is the US trade deficit with China?' and 'Apple's latest 10-K'.

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

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

Explicitly instructs when to use ('Use when a user asks...') and implies alternative by saying 'don't want to figure out which Pipeworx pack/tool to call', providing clear context for use vs. manual tool selection.

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 readOnlyHint=true and destructiveHint=false. Description adds behavioral details: resolves market, classifies bet, fans out to relevant packs, returns evidence and comparison. 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?

Four sentences, front-loaded with purpose. Every sentence adds value, though text is slightly verbose. Could be trimmed slightly but remains effective and readable.

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

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

Given tool complexity and absence of output schema, description adequately explains inputs, processing, and output. Agent can infer what to expect. Schema covers parameters fully, so description completes the picture.

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

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

Schema coverage is 100% with both parameters described. Description adds meaning: explains depth enum values ('quick = 2-3 evidence sources') and clarifies accepted formats for market parameter (slug, URL, question text). Exceeds baseline 3.

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

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

Description uses specific verb 'Research' and clear resource 'Polymarket bet by pulling Pipeworx data'. It explains inputs (slug, URL, question text) and outputs (evidence packet, comparison), distinguishing it from siblings by being a one-call consolidation tool.

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

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

Explicitly states when to use with example queries: 'should I bet on X?', 'what does the data say...', 'is there edge...'. Does not list alternatives but positions as core demo product, implying it is preferred over manual discovery.

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?

With no annotations, the description carries full burden. It discloses data sources (SEC EDGAR/XBRL, FAERS, FDA, trials), return format (paired data + citation URIs), and efficiency gains (replaces 8-15 calls). Minor gap: no mention of rate limits or error conditions.

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

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

The description is somewhat lengthy but well-structured, front-loading the core purpose. Every sentence adds value, though minor redundancy exists (e.g., listing data points twice).

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 exists, so description adequately explains return content per type and mentions citation URIs. It covers primary use cases but lacks details on response structure or error handling, which is acceptable for this complexity.

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

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

Schema coverage is 100%, so description adds value beyond it. It provides concrete examples for values (tickers, drug names) and clarifies that company values are tickers/CIKs and drug values are names, aiding correct invocation.

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

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

The description clearly identifies the tool's purpose: comparing 2-5 companies or drugs side-by-side with specific data fields. It distinguishes itself from siblings like entity_profile by focusing on multi-entity comparisons.

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

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

Explicitly lists trigger phrases ('compare X and Y', 'X vs Y', etc.) and use cases (tables, rankings). It also implies when not to use (single entity queries) by contrasting with the need for comparison.

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?

No annotations provided. Description mentions it returns top-N relevant tools with names and descriptions, but does not explicitly state behavioral traits like read-only or statelessness. Adequate but not fully transparent.

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

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

Two sentences, front-loaded with purpose. The list of domains is slightly verbose but provides useful context. Efficient overall.

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, description explains return format (top-N relevant tools with names+descriptions) and usage context. Covers essential information for a discovery tool with moderate 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 covers both parameters (query, limit) with descriptions. Description adds usage context (e.g., default limit 20) but does not significantly enhance parameter meaning beyond schema, so baseline 3.

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

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

The description clearly states the verb 'find' and the resource 'tools', and lists specific domains (SEC filings, financials, etc.) making it distinct from sibling tools like 'search_books' or 'entity_profile'.

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

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

Explicitly advises to 'Call this FIRST' when needing to browse or discover tools, providing clear context for use. Does not explicitly exclude alternatives but implies when not to use.

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

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

No annotations provided, so the description carries full burden. It discloses that the tool returns recent SEC filings, fundamentals, patents, news, and LEI, and mentions citation URIs. It does not mention any side effects, but as a read-only tool this is sufficient. Lacks details on pagination or rate limits, but the description is clear about the aggregated nature.

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 of four sentences, front-loaded with purpose, then usage, then returns, then parameter details. Every sentence adds value; no filler. It is concise yet comprehensive.

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

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

Given no output schema, the description lists all major return categories (SEC filings, fundamentals, patents, news, LEI). It does not detail the structure of each part, but for a high-level 'profile' tool, this is adequate. The context signals (2 params, 100% schema coverage) support that the description covers the essentials.

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

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

Schema coverage is 100%, but the description adds crucial context: 'Names not supported — use resolve_entity first if you only have a name.' This goes beyond the schema's description of 'Ticker (e.g., "AAPL") or zero-padded CIK'. The example values and the type enumeration are also helpful.

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

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

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It lists specific use cases (e.g., 'tell me about X') and distinguishes itself from siblings by mentioning it avoids calling 10+ other tools. The verb 'Get' and resource 'company profile' are explicit.

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

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

The description provides explicit when-to-use guidance (user asks for company profile, otherwise needs multiple calls) and when-not-to-use (names not supported, use resolve_entity first). It also gives example inputs (ticker or CIK).

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?

No annotations provided, so description carries full burden. It discloses deletion behavior but does not specify side effects (e.g., irreversibility, permissions, or error conditions). Adequate but not comprehensive.

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

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

Two short, direct sentences with no unnecessary words. Front-loaded with action and 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 single parameter, no output schema, and clear sibling relationships, the description fully covers the tool's purpose and usage context.

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

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

100% schema coverage for the single parameter 'key' with description 'Memory key to delete'. Description adds 'by key' but no additional meaning beyond schema.

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

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

Description clearly states that the tool deletes a stored memory by key, using the verb 'delete'. It distinguishes itself from sibling tools 'remember' and 'recall' by specifying the action.

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, task done, or to clear sensitive data. Also mentions pairing with 'remember' and 'recall' for a complete workflow.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by detailing the extraction process (fetches page, extracts title/description/key links) and output format (single text blob), going beyond the minimal annotation info.

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

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

Highly concise: two sentences with a bulleted usage list. Front-loaded with the primary action and output. Every sentence earns its place—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, the description fully explains the return value ('single text blob ready to drop at site-root/llms.txt'). For a low-complexity tool with two parameters and a simple output, the description covers all necessary behavioral and output context.

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

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

Schema coverage is 100% with both parameters fully described. The description does not add significant meaning beyond the schema; it restates 'Full URL' and 'Maximum number of link entries' but provides no additional semantic nuance. Baseline 3 is appropriate.

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

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

Clearly states it generates a production-ready llms.txt file for any URL, specifying verb ('generate'), resource ('llms.txt'), and purpose (AI crawler indexing). Distinguishes from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on visibility scanning rather than file generation.

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

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

Explicitly lists three use cases: getting a client's site indexed, drafting for own projects, and auditing competitor AI visibility. This provides clear when-to-use guidance and implicitly contrasts with sibling tools that serve different purposes.

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?

No annotations; description carries full burden. Lists returned fields but does not mention side effects (likely none), error conditions, or access requirements.

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, no filler, front-loaded with verb and resource.

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

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

No output schema, so description includes return field list. Covers main aspects; could mention error handling but sufficient for simple fetch.

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%, schema already describes author_id. Description does not add new semantics beyond echoing ID format.

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

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

Description clearly states it fetches an author record and lists return content (bio, dates, names, links). Distinguishes from sibling tools like get_work or get_book_by_isbn.

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

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

No explicit when-to-use or alternatives mentioned. Implies usage when an author ID is available, but no guidance on when to use other tools.

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

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

No annotations exist, so the description carries full burden. It discloses return fields but omits behavioral traits such as error handling for invalid/unfound ISBNs, rate limits, or data freshness. For a simple lookup, this is adequate but not comprehensive.

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?

One sentence efficiently conveys purpose and return data. No superfluous words; every part earns its place. The structure is front-loaded with the action and resource.

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

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

Given no output schema, the description lists return fields adequately. For a single-book lookup, it is fairly complete, but lacks explanation of error cases (e.g., not found). It does not need pagination or sorting, so minor gap.

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

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

Schema description coverage is 100% for the single parameter 'isbn'. The description adds 'by ISBN (10 or 13)', which is already implied in the schema's description 'ISBN-10 or ISBN-13 (hyphens optional)'. Thus, minimal added semantic value beyond schema.

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

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

The description clearly states the verb 'Look up', the resource 'a single book by ISBN', and enumerates specific return fields (title, authors, etc.). This specificity differentiates it from siblings like 'search_books' (which likely handles broader search) and 'get_author' (different resource), making the purpose unambiguous.

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

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

The description implies usage when you have an ISBN and want book details, but it does not explicitly state when to use this tool versus siblings like 'search_books' or 'get_author'. No when-not-to-use or alternative guidance is provided, leaving the agent to infer context.

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

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

Annotations are empty, so the description carries the burden. It explicitly states it is a fetch operation that returns data, listing specific fields. However, it does not mention error conditions, authentication, or idempotency.

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

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

The description is a single sentence that efficiently states the purpose and return value, front-loaded with the action and resource.

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 provides a good list of return fields. It covers essential elements like title, description, subjects, etc. Could mention it returns a JSON object, but overall complete for a simple tool.

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

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

Schema description coverage is 100% with a clear description of work_id format. The description adds context by explaining the purpose of the parameter but does not significantly expand beyond the schema.

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

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

The description clearly states the verb 'Fetch' and the resource 'canonical work record' with an explanation of what a work is. It lists specific return fields, distinguishing it from siblings like get_author and get_book_by_isbn.

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

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

The description implies use when you have a work ID, but does not explicitly provide when-to-use or when-not-to-use compared to sibling tools. No alternatives are mentioned.

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?

No annotations provided, so description carries full burden. Discloses rate limit ('Rate-limited to 5 per identifier per day'), cost ('Free; doesn't count against your tool-call quota'), and impact ('The team reads digests daily and signal directly affects roadmap'). No contradictions.

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

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

Description is a single well-structured paragraph with clear sentences. Each sentence provides distinct value: purpose, when-to-use, how to describe, team impact, rate limit, cost. Slightly verbose but effective.

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

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

Given it's a feedback tool with no output schema, the description covers all necessary aspects: purpose, usage, behavioral constraints, and expected outcome (team reads digests). No gaps for an agent to understand when and how to use it.

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

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

Schema coverage is 100%. Description adds value by explaining enum semantics (e.g., 'bug = something broke or returned wrong data') and message format ('Be specific... 1-2 sentences typical, 2000 chars max'). 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 clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies a verb (tell/feedback) and resource (Pipeworx team about issues). Distinguishes from siblings like ask_pipeworx and discover_tools by being a dedicated feedback channel.

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

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

Provides explicit when-to-use scenarios: '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).' Also gives format guidance: 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt.' Mentions rate limit and free usage.

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

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

The description adds significant behavioral context beyond annotations: it explains the data source (CF analytics-engine), states it contains no PII, and documents caching behavior (5min-1h). Annotations already declare read-only, idempotent, and non-destructive, so the description enriches that with operational detail.

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 that are highly efficient: the first states what the tool returns, the second lists three specific use cases, and the third provides technical provenance and caching. No wasted words; the most critical information 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?

Despite no output schema, the description adequately describes the return structure ('top tools, top packs, and total call volume') and includes caching details. For a simple tool with one optional parameter, this is fully complete – an agent can understand inputs, outputs, and limitations.

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% and the description adds meaningful context for the single parameter 'window': it clarifies that shorter windows surface real-time trends and longer windows show steady-state demand, plus it notes '24h' as the default. This goes beyond the schema's enum list.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a specified window. It succinctly explains the resource ('trending data') and the action ('returns'), making the purpose unambiguous and distinct from siblings 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?

Three concrete use cases are provided (discovering hot data sources, confirming a tool's canonical choice, seeing alignment). While no explicit alternatives or when-not-to-use guidance is given, the sibling tools are sufficiently different that an AI agent can infer when this is the right choice. A minor gap remains in not explicitly stating when to avoid this tool.

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

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

Annotations indicate readOnlyHint, openWorldHint, and destructiveHint false. The description adds behavioral details: it searches, groups, and checks monotonicity, and mentions returned output (ranked opportunities with reasoning). 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, front-loading the main purpose and modes. It is efficient but could be slightly more concise by trimming examples; still, every sentence adds value.

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

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

Given the lack of output schema, the description includes return value info (ranked opportunities with direction and reasoning). It covers both modes adequately. A response structure example could further improve completeness.

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

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

Input schema has 100% coverage with descriptions for both parameters. The description adds extra meaning by providing examples and clarifying that event expects a slug/URL while topic expects a seed question. This exceeds the baseline.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations, with two distinct modes. It differentiates itself from siblings by specifying cross-event detection for cases where Polymarket splits related questions into separate events.

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

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

The description explains when to use each mode (event slug vs. topic), and highlights that cross-event mode catches cases missed by single-event mode. It does not explicitly state when not to use the tool, but the context is 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?

The description details the model (lognormal from FRED + coinpaprika), the steps (scans, groups, fetches once, computes, ranks), and output (top N with edge magnitude and direction). This goes well beyond the readOnlyHint and destructiveHint annotations, providing rich behavioral insight.

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

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

The description is front-loaded with the main action and then explains details. It contains 5-6 sentences, most of which add value. A slightly more concise phrasing could be beneficial, but it is well-structured and not overly long.

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 only says 'Returns top N ranked by edge magnitude with suggested trade direction' but does not detail the structure of each returned item (e.g., fields like market name, probability, edge, direction). For a tool with 3 parameters, this is adequate but not complete.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add new meaning beyond what the schema descriptions already provide for limit, window, and min_edge_pp. It restates defaults and bounds, which is adequate but not additive.

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

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

The description clearly states the tool scans Polymarket markets and identifies those where Pipeworx data disagrees with market price, using a specific model. It explicitly targets the 'what should I bet on today' question, distinguishing it from siblings like polymarket_arbitrage or bet_research.

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

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

The description provides clear context for when to use the tool (discovering betting opportunities) but does not explicitly state when not to use it or mention alternative tools. The phrase 'without paging through hundreds of markets by hand' implies its efficiency value.

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 non-destructive. The description adds valuable behavioral context: the delta is a real arbitrage signal, returns raw probabilities and percentage-point spreads, and explains the two operational modes. 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 relatively concise given the complexity: three sentences covering purpose, rationale, modes, and output format. Slightly verbose with the enumeration of topic values, but still front-loaded and well-structured.

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

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

With no output schema, the description fully describes the return format (leg-by-leg prices, spreads). For a tool with three optional parameters and a well-defined purpose, the description covers all necessary context: inputs, outputs, and why it exists.

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 each parameter. The description adds significant meaning: explains the 'topic' parameter as pre-mapped macros with examples, and clarifies the override behavior of the explicit parameters with example values. This enriches understanding beyond the schema.

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

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

The description clearly states the tool's purpose: computing cross-venue spreads between Kalshi and Polymarket for the same resolving question. It uses specific verbs ('computes', 'returns') and names the two venues, distinguishing it from siblings like 'polymarket_arbitrage'.

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

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

The description explains the context (pricing difference due to different participant pools) and the two usage modes (topic shortcuts vs explicit tickers). It implies when to use (searching for arb opportunities) but does not explicitly state when not to use or mention 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?

Discloses scoping and conditional behavior (list all keys vs. retrieve), but with no annotations, it omits details like return format or error handling.

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 compact sentences front-load the core functionality, provide use cases, and mention pairing with siblings—no unnecessary information.

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

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

Given the tool's simplicity (one optional param, no output schema), the description covers retrieval behavior, listing mode, and scoping—minor omissions like return format are acceptable.

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

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

Schema coverage is 100% and the description mirrors the schema for the key parameter, adding context on its purpose but no new semantic detail.

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 retrieves saved values or lists keys, distinguishing it from siblings remember (save) and forget (delete).

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 use cases for looking up stored context and pairs with remember/forget, but does not explicitly state when not to use.

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

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

In absence of annotations, description discloses fanout to three sources (SEC EDGAR, GDELT, USPTO), input formats for 'since', and return structure (changes + count + URIs). Lacks details on error handling or rate limits, but covers core 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?

Front-loaded with purpose and examples. Some redundancy with schema fields, but overall efficient at roughly 4 lines. No superfluous content.

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

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

Given no output schema and no annotations, description adequately explains behavior, return structure, and query formats. Covers multiple data sources, making it suitable for monitoring tasks.

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

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

Schema coverage is 100% and description aligns with schema. Description adds minimal extra value (recommended default '30d'), but does not significantly surpass schema detail. Baseline of 3 is appropriate.

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

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

Description starts with a clear question-answering summary ('What's new with a company...'), gives concrete use-case examples, and differentiates from siblings like entity_profile (static) and compare_entities by focusing on temporal change detection.

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 example queries and conditions for use. Does not state when not to use or list alternative siblings for exclusion, but the examples clearly define the intended context.

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

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

With no annotations, the description fully covers behavioral traits: scoping by identifier, persistence duration (24h anonymous vs permanent authenticated), and that data is stored as key-value pairs. No contradictions and no hidden 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?

Four sentences, all essential. Front-loaded with the core purpose. No wasted words. Efficient and 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?

Covers purpose, usage, parameters, and behavioral details. Lacks only what the return value is (e.g., confirmation of save). For a simple write tool with no output schema, this is still fairly complete.

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

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

Schema coverage is 100%, but the description adds value by giving concrete examples of keys and explaining that values can be any text. This goes beyond the schema's brief descriptions.

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

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

The description clearly states the action ('Save data the agent will need to reuse later') and identifies the resource (key-value pairs scoped by identifier). It distinguishes from siblings 'recall' and 'forget' by mentioning them specifically.

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

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

Provides explicit guidance on when to use the tool ('when you discover something worth carrying forward') and explains the difference in persistence based on authentication status. Mentions pairing with recall/forget, giving clear context for usage.

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

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

No annotations are provided, so the description must disclose all behavioral traits. It states that the tool looks up identifiers and returns IDs plus citation URIs, but does not mention if it is read-only, error behavior, data freshness, or any side effects. It is adequate but could be more explicit.

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 brief (5 sentences) and front-loaded with the primary action. It uses examples and a call to action ('Use this BEFORE...'). It is well-organized but could tighten the phrasing slightly.

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 essential aspects: purpose, input format, output content (IDs and citation URIs), and ordering relative to other tools. It lacks details on ambiguity resolution or failure handling, but is largely complete for a lookup tool.

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

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

Schema coverage is 100%, so the description adds value by providing examples of valid inputs (e.g., 'Apple' → AAPL, 'Ozempic' → RxCUI). The description clarifies the mapping between type and value, reinforcing what the schema says with concrete instances.

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: look up canonical identifiers for companies or drugs. It explicitly lists the ID systems (CIK, ticker, RxCUI, LEI) and provides concrete examples. While it doesn't contrast with sibling tools like entity_profile, the purpose is unambiguous and actionable.

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

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

The description gives explicit context for when to use this tool ('when you need CIK, ticker, RxCUI, or LEI') and instructs to use it BEFORE other tools that need identifiers. It implies it replaces multiple lookups, but does not mention when NOT to use it or alternatives.

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

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

Annotations already indicate a safe, idempotent read operation. The description adds behavioral context such as probing each entity with ai_visibility_check, ranking by score, and returning a ranked list with score, confidence, and signal density per entity. It also details the use of API keys for Anthropic. 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 of about four sentences. It is front-loaded with the main action and every sentence adds value, with no superfluous information. It is concise and well-structured.

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

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

Given the tool has no output schema, the description adequately explains return values: 'ranked list with score, confidence, signal density per entity.' It covers all input parameters comprehensively. The complexity is moderate (four parameters, one required), and the description is complete for an AI agent to use the tool correctly.

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

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

The input schema has 100% description coverage, and the tool description adds additional meaning: for entities, it notes 'First entry treated as the "subject" for narrative; rest are competitors.' For models, it says 'Omit for just workers-ai.' For _apiKey, it specifies conditions for use. The description enhances the schema's explanations.

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: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare), resource (AI visibility), and that it does side-by-side comparison across multiple entities. It mentions probing each entity with ai_visibility_check and ranking by score. This differentiates it from the sibling single-entity tool ai_visibility_check.

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 concrete use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' It implies when to use this tool (comparing multiple entities) versus alternatives (single entity check, likely using ai_visibility_check). However, it does not explicitly state when not to use it or name alternatives directly.

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

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

No annotations provided. Description does not disclose behavioral traits such as rate limits, pagination behavior, or read-only nature. It only mentions search syntax and return fields.

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 purpose, syntax, and return data. No wasted words.

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

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

Given no annotations and no output schema, description covers query format and return fields well but lacks behavioral context (pagination, error handling) and does not discuss any side effects.

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 on parameters. Description adds value by explaining prefixed search syntax and listing return fields, going beyond the schema's simple 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?

Description clearly states the tool searches Open Library by title, author, or subject, and specifies return fields. This distinguishes it from siblings like get_author or get_book_by_isbn which are specific 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?

Description implies use for broad search but does not explicitly state when to use this tool vs alternatives like get_author or get_work, nor does it mention 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?

No annotations are provided, so the description must disclose behavioral traits. It discloses the data source, scope (v1, company-financial only), return values (verdict with five categories, structured form, actual value with citation, percent delta), and the fact that it replaces multiple calls. It does not explicitly mention read-only nature or rate limits, but the tool is inherently non-destructive and the description is fairly transparent.

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

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

The description is concise, consisting of 4-5 sentences with no superfluous information. It is front-loaded with the core purpose and usage, followed by scope, return details, and an efficiency note. Every sentence serves a clear purpose, making it easily digestible.

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

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

Given the tool's simplicity (single parameter, straightforward action), the description covers the essential aspects: purpose, when to use, input specification, return format, and limitations. It lacks details on error handling or edge cases, but for the tool's complexity level, it is sufficiently 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 a clear description of the 'claim' parameter, including examples. The description adds value beyond the schema by specifying that the tool supports natural-language claims and the specific domain (company-financial), which helps the agent provide appropriate inputs. Thus, it goes slightly beyond 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 specifies the tool's purpose: fact-checking and validating natural-language factual claims against authoritative sources. It distinguishes itself from sibling tools by focusing on claims validation, and it explicitly states the supported domain (company-financial claims) and data source (SEC EDGAR+XBRL).

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

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

The description explicitly states when to use the tool ('when an agent needs to check whether something a user said is true') and provides example queries. It does not explicitly state when not to use it, but the context and scope limitations are clear. It also mentions that it replaces multiple sequential calls, implying efficiency benefits.

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