Endoflife

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds important details: default model is free, Anthropic requires own key (cost implication), and return format per-model including {score, confidence, signals, raw_response}. No contradictions.

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

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

The description is concise but could be slightly more structured. It front-loads the purpose and key details, but includes inline parameter explanations that might be better as separate lines. Still, every sentence serves a purpose.

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

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

Given the complexity (4 parameters, no output schema), the description adequately covers return format, parameter examples, and cost implications. No missing critical information.

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 examples for entity, clarifies which models are supported and the API key requirement, and provides disambiguation help for context parameter. This adds meaningful 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 tool probes LLMs to score visibility (0-100) for entities like brands or topics, using specific verbs and resources. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on any entity not just competitors.

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

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

The description provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not mention when not to use or alternatives like 'deep_research'. The context signals imply the tool is for quick probes, but more explicit guidance would improve this.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: it routes to the correct tool, fills arguments, and returns structured answers with stable pipeworx:// citation URIs. This adds behavioral detail beyond annotations without contradiction.

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

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

Front-loaded with key guidance (prefer over web search). Every sentence adds value, but the description is somewhat long due to extensive domain listing. Could be trimmed slightly 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?

Despite no output schema, the description adequately explains the return format (structured answer with stable citation URIs). Covers routing behavior, argument filling, and safety profile via annotations. For a complex meta-tool, this is highly 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% for all 6 parameter aliases, each with clear description. The description adds minimal extra meaning beyond accepting natural language questions. It provides examples in the schema but no additional parameter-level 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?

Clearly states the tool routes questions to 3,725 tools across 881 verified sources, providing structured answers with citations. Distinguishes from web search by emphasizing authoritative structured data. Lists many specific domains (SEC filings, FDA drugs, etc.) making purpose highly specific and unambiguous.

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

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

Explicitly recommends preferring over web search and provides many example query types ('what is', 'look up', 'find', etc.). Includes concrete examples. However, it does not explicitly differentiate from sibling tools like deep_research or bet_research, which could cause confusion for edge 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral traits beyond annotations: costs one extra LLM call, returns refusal reasons on failure, uses only tool result (no hallucination). No contradiction. A small gap: does not explicitly state it is a read-only operation, but annotations cover that.

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

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

The description is three sentences long and efficiently conveys purpose, mechanism, and usage guidance. It is well-structured and front-loaded with the key differentiator. Could be slightly shorter, but no wasted words.

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

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

Given the tool's complexity (extraction, refusal reasons, cost) and absence of output schema, the description is comprehensive. It describes return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason}), error reasons, cost comparison, and explicit usage guidelines. This fully equips an agent to decide and use the tool correctly.

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

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

Schema coverage is 100% (all 6 parameters are aliases for 'question' and are described in schema). The description only says 'Your question in natural language', which adds no additional meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'ask' and resource 'Pipeworx — Grounded', and distinguishes from sibling 'ask_pipeworx' by emphasizing 'hallucination-resistant answer mode for high-stakes reads'. It specifies that it extracts answers using only tool results and returns explicit refusal reasons, which is a clear differentiator.

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: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts', and when not: 'prefer ask_pipeworx for casual lookups'. Provides concrete examples of high-stakes domains (financial verdicts, legal claims, medical 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?

Description goes far beyond annotations (readOnlyHint, idempotentHint) by detailing resolution contract (market_match_confidence, alternatives, suggestions), parent_event extraction, news fallback mechanisms, safety short-circuits for low-confidence matches, closed market handling, wide-spread liquidity warnings, and resolution-rule risk. It provides comprehensive behavioral disclosure.

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 long but well-structured with clear headers (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES) and organized information. While dense, every sentence adds value. Could be slightly more concise, but structure aids readability for a complex tool.

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

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

Given the tool's complexity, 100% schema coverage, and no output schema, the description is exceptionally complete. It covers input variations, behavior for different scenarios, response shapes, error handling, caching details, and safety mechanisms. 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% with all three parameters described. Description adds significant value by elaborating on the 'market' parameter (accepts slug, URL, or question text), explaining 'depth' (quick vs thorough with defaults), and clarifying 'include_raw' (default false, response size trade-offs). This goes well beyond the schema's basic 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 defines the tool as researching a Polymarket bet by pulling Pipeworx data. It uses specific verbs ('Research'), states the resource ('Polymarket bet'), and explains the process (resolve, classify, fan-out, return evidence packet). It distinguishes from sibling tools like polymarket_arbitrage or polymarket_edges by focusing on data retrieval and analysis.

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

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

Description explicitly provides use cases ('Use for "should I bet on X", "what does the data say about Y"') and gives examples of classifiers and fan-outs. It lacks explicit when-not-to-use instructions or direct comparisons with alternatives, but the context is clear. Score 4 because exclusions would strengthen 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 indicate read-only, open-world, idempotent, non-destructive. The description adds: results sorted by primary metric, correct handling of off-calendar fiscal years, and output includes paired data and citation URIs. No contradiction; it enriches behavior beyond annotations.

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

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

The description is a single dense paragraph that front-loads query phrases and covers all essential aspects. Every sentence adds value, though it could be slightly restructured (e.g., bullet points) for easier scanning. However, it is efficient and not verbose.

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

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

Given the tool's complexity (comparison, dual entity types, specific data sources, sorting, output format), the description covers all necessary context without needing an output schema. It explains what data is retrieved, special handling (fiscal years), sorting behavior, and output structure (paired data + URIs).

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

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

Schema coverage is 100%, and the description adds significant meaning: explains 'type' enum values with data sources (SEC EDGAR/XBRL vs FAERS) and specific data fields (revenue, net income, etc. for company; adverse events, approvals, trials for drug). 'values' examples include tickers and drug names with range 2-5, clarifying input 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?

The description begins with common query patterns, explicitly states it does side-by-side comparison of 2-5 entities in one call, details the data pulled for each type (company: 10-K financials; drug: FAERS/FDA/trial counts), and notes it replaces multiple sequential lookups. This clearly differentiates from sibling tools like 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 prefer this over sequential single-entity lookups when comparing entities. It does not list when to avoid, but the maxItems constraint and focus on comparison provide implicit boundaries. The description lacks explicit alternatives beyond sequential lookups.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds context about never inventing facts, using explicit gaps[], returning structured packets, and expected 15-60s wait time, which complements annotations well without contradiction.

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

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

The description is somewhat long but efficiently structured: first action, then details, usage guidance, prerequisites, timing. Every sentence contributes useful information; minor redundancy could be trimmed.

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: structured findings with verbatim evidence, confidence, source, fetched_at, citation, and gaps array. Also covers timing and account requirements, making it complete for a complex research tool.

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

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

Schema coverage is 100%, but description adds meaning by explaining depth enum values (quick=3, standard=5, thorough=8) and noting that broad questions are fine. It also mentions the paid requirement for depth:thorough, which goes 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 defines the tool as a multi-source research tool that decomposes questions into sub-questions, routes to many tools in parallel, and returns grounded answers with evidence. It distinguishes from sibling tools like ask_pipeworx by noting that the latter is for single lookups.

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

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

Explicitly states when to use this tool (broad or multi-part questions) and when to use an alternative (use ask_pipeworx for single lookups). Also provides prerequisites: requires a Pipeworx account, and depth:thorough requires a paid plan.

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 behavioral details beyond annotations: it returns the top-N most relevant tools with full input schemas and curated examples, and states that results are ready to call directly with no second schema lookup. Annotations already indicate read-only and idempotent, so the description complements without contradiction.

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

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

The description is relatively long but front-loaded with the core purpose. Each sentence adds value, from listing domains to explaining the return format. It could be slightly more concise, but it remains efficient 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 no output schema, the description satisfactorily explains return values: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples).' It also contextualizes the tool's role in the workflow. This is complete enough for a discovery 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% (6 parameters, all described). The description adds little beyond the schema, only providing examples of natural language queries. While it reinforces the query parameter's purpose, it does not significantly enhance parameter 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 'Find tools by describing the data or task.' It specifies a verb ('find') and resource ('tools') and distinguishes itself from sibling tools by being a meta-tool for discovering available tools, with instructions to call it first.

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 guidance to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also lists example tasks, providing context for when to use it. However, it does not explicitly state when not to use it, though this is implied.

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

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

Adds significant context beyond annotations: describes fan-out across multiple data sources, notes a potential sunset for patents API with soft-fail behavior, and lists output fields. No contradiction with annotations (readOnly, openWorld, idempotent, non-destructive).

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

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

Single paragraph contains all essential information in a front-loaded manner with example commands. Could benefit from slightly more structuring (e.g., list format for outputs), but remains efficient.

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

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

Despite no output schema, the description fully enumerates return fields and sources. For a complex tool with 2 parameters and broad data integration, the description leaves no ambiguity about what is returned and how it behaves.

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

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

Schema already provides 100% coverage with descriptions for type (enum with note) and value (ticker or CIK format). Description reinforces this and clarifies that names are not supported, adding value beyond schema.

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

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

The description explicitly states the tool creates a 'full cross-source profile of a US public company in ONE parallel call' and lists concrete output components (CIK, recent filings, fundamentals, etc.). It differentiates from siblings like resolve_entity by specifying that names are not supported.

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

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

Includes explicit instruction to 'ALWAYS PREFER over chaining single-pack lookups' for holistic views, and provides clear when-not-to-use: 'names not supported (use resolve_entity first)'. Examples of input formats are given.

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

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

Annotations indicate destructiveHint=true and readOnlyHint=false, consistent with deletion. The description adds context about clearing sensitive data, complementing annotations without contradiction.

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

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

Two sentences, no fluff. Purpose, usage, and sibling pairing are all conveyed efficiently.

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; the description covers purpose, when to use, and relationships with siblings, fully satisfying contextual needs.

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

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

Only one parameter 'key' is described in the schema with 'Memory key to delete'. The description adds no further semantic value, meeting baseline for 100% schema coverage.

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

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

The description uses a specific verb ('Delete') and resource ('previously stored memory by key'), clearly distinguishing from sibling tools 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 ('context is stale, task done, clear sensitive data') and mentions related tools ('Pair with remember and recall'), providing 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?

Adds useful context beyond annotations (fetches page, extracts title/description/links, outputs markdown). Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false; description aligns and provides behavioral details.

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

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

Three sentences: purpose, process, use cases. Front-loaded with key information, no unnecessary words. Very efficient.

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

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

Covers purpose, process, output format, and use cases. No output schema, but description explains output is a text blob. Sufficient for a straightforward tool with well-documented parameters.

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 no extra semantic details beyond the schema's parameter descriptions, so baseline score of 3 applies.

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

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

Clearly states the verb 'Generate', the resource 'llms.txt', and the context of AI crawlers. Distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on file generation rather than scanning.

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

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

Explicitly lists three use cases (client indexing, own project, competitor auditing), but does not mention when to avoid using it or suggest alternative tools.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. Description adds 'Keyless' (no authentication needed) and explicitly lists return fields, adding value beyond annotations.

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

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

Two focused sentences plus parameter guidance. No extraneous words; every sentence adds unique 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?

Lacks output schema, but description compensates by enumerating all returned fields (release date, EOL, etc.). For a simple tool, this is sufficient and complete.

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

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

Schema coverage is 100% with examples. Description reinforces parameter meaning and crucially ties product slug to list_products, which aids 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?

Description clearly states it retrieves support details for a single release cycle, listing specific fields and distinguishing from list_products by referencing slug source.

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 concrete use case example ('when does Python 3.9 lose support?') and explains how to obtain parameters (slug from list_products), but does not explicitly exclude alternative tools.

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

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

Annotations already declare the tool as read-only and idempotent, so the description's behavioral transparency mainly adds the 'keyless' note and the specific data fields returned. No contradictions.

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

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

The description is concise (two sentences) and efficiently delivers purpose, use cases, and parameter source. Every word serves a purpose.

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

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

For a simple retrieval tool with one parameter and clear output fields described, the description is mostly complete. It lacks explicit error scenarios or confirmation of data freshness, but annotations hint at open world.

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 parameter description in the schema already fully explains the parameter. The tool description repeats that information without adding additional semantics, so it meets the baseline for schema coverage.

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

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

The description clearly states the tool retrieves a product's full release/support timeline with specific fields (release date, EOL, etc.). It provides explicit example queries and differentiates from sibling list_products by mentioning the product slug source.

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 concrete example queries and implies prerequisite use of list_products to get the slug. It does not explicitly exclude other use cases or compare to sibling get_cycle, but the guidance is sufficient for common scenarios.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds that it is 'keyless' (no auth required) and that results are slugs feeding other tools. This provides useful behavioral context beyond annotations, but no details on rate limits, pagination, or response structure.

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

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

Two sentences, zero fluff. Front-loaded with the core purpose, followed by usage guidance and examples. Every word adds value.

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

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

With no output schema, the description explains what is returned (slugs) and their use within the domain. For a simple list tool with two optional params, this is sufficient. Could mention ordering or that results are paginated via limit, but not critical.

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

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

Input schema covers 100% of parameters with descriptions. The description enriches this by noting the search filter is case-insensitive and showing examples. This adds practical guidance beyond the schema's generic 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 lists ~460 software product slugs tracked by endoflife.date, specifies categories (languages, frameworks, etc.), and explicitly connects to sibling tools get_product and get_cycle. The verb 'List' combined with the scope and resource makes the purpose unambiguous.

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

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

Provides explicit guidance to use the 'search' filter for finding specific products, with concrete examples ('postgres', 'ubuntu', 'node'). While it doesn't list when not to use, the connection to siblings implies its role as a precursor to get_product/get_cycle. A short exclusion statement would improve it further.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds default active-only behavior and return field details, but doesn't contradict annotations.

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

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

Two concise sentences: first defines purpose and output, second provides usage guidance. No redundant or missing 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?

For a simple list tool with one parameter, description fully covers purpose, usage, return fields, and default behavior. 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% and includes parameter description. Description reinforces default behavior (active) but doesn't add significant new 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?

Clearly states 'List the caller's active subscriptions' with specific verb, resource, and scope. Mentions return fields, distinguishing it from subscribe/unsubscribe siblings.

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

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

Explicit suggests using it to review subscriptions before adding more or to find an ID for cancellation. No explicit exclusions or alternatives, but context is clear.

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

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

Annotations provide no behavioral hints, but the description adds valuable transparency: rate-limited to 5 per identifier per day, free, and that the team reads digests daily. This covers behavioral traits beyond what structured fields provide.

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 and well-structured: purpose first, then usage guidelines, then constraints. Every sentence adds essential information without redundancy.

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

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

Given the tool's simplicity (feedback submission), the description covers purpose, types, usage tips, rate limits, and impact. It addresses the tool's complexity adequately and sets clear expectations.

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 providing usage guidance for the 'context' parameter (describe in terms of tools/packs) and advising message length (1-2 sentences typical), which 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 identifies the tool's purpose: sending feedback (bug, feature, etc.) to the Pipeworx team. It uses specific verbs ('tell') and resource ('Pipeworx team'), and distinguishes from sibling tools by being the only feedback submission tool.

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

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

The description explicitly states when to use the tool (for bugs, missing features, praise) and provides clear guidance on what to include (describe in terms of tools/packs) and what to avoid (don't paste end-user prompt). It also notes rate limits and that the tool doesn't count against quota.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by disclosing caching behavior, data source (CF analytics-engine), and no PII.

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 a clear first sentence, numbered use cases, and technical notes. It could be slightly more concise but overall efficient.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description covers purpose, use cases, data source, caching, and privacy comprehensively.

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

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

Schema description coverage is 100% with a detailed description of the 'window' parameter. The tool description adds marginal value by repeating the window purpose, so baseline 3 is appropriate.

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

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

The description clearly states it returns top tools, top packs, and total call volume over a recent window. It distinguishes from siblings like discover_tools by focusing on real-time agent usage trends.

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

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

Three explicit use cases are listed, providing clear context for when to use the tool. However, there is no mention of alternatives or explicit 'when not to use' guidance.

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

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

The description goes far beyond the annotations by detailing required parameters, partition-check logic, Jaccard similarity threshold, placeholder filtering, fill-check behavior, and the shape of the response. It discloses that calling with no args fails and explains edge cases like low similarity and partition filter.

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 quite long but well-structured, starting with the requirement and then explaining each mode in detail. Every sentence adds value for such a complex tool. While it could be slightly more concise for a simpler tool, the length is justified by the complexity.

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

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

Given no output schema, the description thoroughly explains the response structure (opportunities array, partition_check object, fill_check details). It covers parameter behavior, thresholds, filters, and edge cases. The tool is highly complex, and the description leaves no major 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?

Although the schema already describes both parameters (100% coverage), the description adds significant meaning: examples of valid slugs and topics, the distinction between single-event and cross-event modes, and how each parameter triggers different algorithms. This is far beyond what the schema alone provides.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It explains two distinct modes (event and topic) and their specific behaviors. This is more than sufficient to distinguish it from sibling tools like polymarket_edges or polymarket_fill_risk.

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 advises when to use each mode: event for a specific market, topic for cross-event scanning. It also directs users to `polymarket_fill_risk` for custom sizing, providing clear 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. The description adds extensive behavior details: explains three model families, edge calculation, caching with 1h KV keyed on knobs, and diagnostic fields. This exceeds annotation coverage.

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

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

The description is very long and dense, lacking clear structure or bullet points. It is not front-loaded effectively; the first sentence is good but then dives into technical details without breaks.

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 (three segments, nine parameters, no output schema), the description covers all essential aspects: response structure, edge fields, knobs, diagnostics, and caching. No missing critical information.

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

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

Schema coverage is 100%, so the description does not need to add much parameter meaning. It provides some extra context (e.g., slippage explanation) but the baseline is 3. No major gaps.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, with a specific purpose 'what should I bet on today'. It distinguishes from siblings by focusing on Pipeworx data disagreement and providing three model families.

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 for daily opportunity discovery but does not explicitly state when to use this tool versus alternatives like polymarket_arbitrage. It lacks explicit 'when not to use' guidance.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context beyond these, such as the 60-day snapshot TTL, that decay numbers are based on daily closes of edge_pp_net, not intraday, and the response structure. 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 somewhat lengthy but every sentence adds value. It front-loads the purpose and then details parameters and response. It could be slightly more concise, but it is well-structured and easy 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?

The description fully compensates for the lack of output schema by explaining the response structure (tracked, expired, snapshot_dates) in detail. Given the tool's complexity, it covers all necessary aspects including limits and nuances, making it complete for an agent to use.

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

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

Schema description coverage is 100% for both parameters. The description adds value by explaining the purpose of 'window' as a snapshot family and providing default and max values for 'days'. It enhances understanding beyond the schema's 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 the tool's purpose: providing edge persistence and decay telemetry from daily snapshots. It answers a specific question about how long an edge has existed and whether it's shrinking, which distinguishes it from related tools like polymarket_edges. The verb 'track' and resource 'edge persistence' are specific 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 provides a clear use case by contrasting fresh versus old edges, implying when to use this tool. However, it does not explicitly state when not to use it or offer alternatives among sibling tools. The context is clear enough for an agent to decide, but lacks explicit exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds rich behavioral context: requires one of market/event, walks the ladder, returns judgment (clean/degraded/cannot_fill), and warns about thin books and partial fills causing unhedged 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 verbose but densely informative. All sentences serve a purpose—defining mode, parameters, return fields, and usage guidance. It is front-loaded with purpose. Could be slightly more structured but appropriate for the tool's 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?

No output schema, but description comprehensively lists return fields for both modes (single-market: top_of_book, vwap_fill_price, slippage_pp, etc.; basket: theoretical_sum, capture_ratio, profit_usd, thin_legs, etc.). It also explains when to use the tool and why (risk of partial fills). Complete for the tool's complexity and sibling 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% parameters with descriptions, but the description adds significant operational semantics: explains how size_usd is interpreted differently in single-market (spend/proceeds) vs basket (settlement notional), clarifies side defaults and mutual exclusivity of market/event. This goes beyond schema, though schema already does heavy lifting.

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 precisely states the tool's function: 'Realizable-vs-theoretical edge check against live CLOB order-book depth'. It clearly distinguishes between single-market and basket modes, and lists specific return fields (e.g., top_of_book, vwap_fill_price, verdict). This verb-resource combination is unique among siblings.

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

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

Explicit usage guidance is provided: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains why—'theoretical overround on thin books is not capturable' and partial baskets create unhedged directional positions. This clearly differentiates from sibling tools and provides actionable context.

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

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

The description adds extensive behavioral context beyond annotations: it details compatibility warnings, temporal alignment checks, skipped leg comparisons, and the conditions under which spreads are meaningful. 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 clear sections for modes, response fields, and safety fields. It front-loads the core purpose. Some redundancy (e.g., listing shortcuts twice) could be trimmed, but the density is justified by the tool's 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?

Despite lacking an output schema, the description fully describes the response structure, including leg-by-leg prices, top spreads, and safety fields. It covers edge cases and interpretation, making it comprehensive for agent invocation.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description expands on each: it lists the 10 macro shortcuts for 'topic', provides format examples for 'kalshi_event_ticker' and 'polymarket_event_slug', and explains how they interact.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on comparisons across two venues.

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 two usage modes (topic shortcuts vs explicit pairings) and provides explicit guidance on when to expect meaningful results, including warnings that pre-mapped topics often return compatibility warnings and are not automatically tradeable.

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=true and idempotentHint=true. The description adds that scoping is tied to an identifier (anonymous IP, BYO key hash, or account ID), which goes beyond annotations.

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

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

Three sentences, each serving a distinct purpose: what it does, when to use it, and how it pairs with siblings. Front-loaded and efficient.

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

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

For a simple tool with one optional parameter and no output schema, the description covers all essential behaviors: retrieval, listing, scoping, and pairing.

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 the single parameter with 100% coverage. The description adds that omitting the key lists all saved keys, providing additional semantic 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 retrieves a value saved via remember or lists all keys when the argument is omitted, distinguishing it from sibling tools like forget and remember.

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

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

It explicitly says when to use (to look up context stored earlier) and suggests pairing with remember and forget, but does not explicitly mention alternatives or when not to use.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds important behavioral context: the mark_read parameter affects future calls by flagging events as read, and the same feed is accessible via a GET endpoint. No contradictions with annotations.

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

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

The description is a single, well-structured paragraph that front-loads the purpose and efficiently conveys all essential information without unnecessary words. Every sentence serves a purpose.

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

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

For a read-only tool with 5 optional parameters and no output schema, the description covers return fields, filtering options, polling suitability, and an alternative API endpoint. It lacks an explicit example of the response structure but is otherwise comprehensive.

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 giving an example for the 'type' filter ('e.g. 'sec_8k'') and explaining the effect of 'mark_read' ('flag returned events read so the next call only shows newer ones').

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 a specific verb and resource ('Pull fired events from your subscription feed'), making the tool's operation immediately clear. It distinguishes itself from siblings by focusing on alerts from the subscription feed, unlike other tools like 'subscribe' or 'list_subscriptions'.

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

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

The description gives guidance on when to use polling ('Polls work fine') and offers an alternative endpoint for scripts/dashboards. However, it does not explicitly contrast with nearby sibling tools or state when not to use this tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds rich behavioral context: parallel fan-out to multiple sources, fallback logic, USPTO soft-fail due to sunset, return structure (changes grouped by source). No contradiction with annotations.

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

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

Description is dense but well-organized in a single paragraph with front-loaded query examples. Every sentence adds value, though it could be slightly more structured (e.g., bullet points). 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 three required parameters, no output schema, and good annotations, the description is fully complete. It explains fan-out, fallback, limitations (USPTO sunset), output structure, and alternative tool usage. No gaps for an AI agent to misunderstand.

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 value: explains 'since' accepts ISO or relative shorthand with examples ('7d', '30d'), suggests '30d' for monitoring, clarifies 'type' only supports 'company', and 'value' can be ticker or CIK. This goes beyond the schema definitions.

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

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

Description clearly states it provides a change feed for a company in the last N days, listing specific sources (SEC EDGAR, GDELT/GNews, USPTO) and distinguishing from the static 'entity_profile' sibling tool. The specific verb+resource combination is 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?

Description includes explicit query examples (e.g., 'What's new with X'), explains when to use each data source (GDELT preferred, GNews fallback), and directs to 'entity_profile' for static, window-independent profiles. 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?

Annotations already indicate idempotentHint=true and non-destructive. Description adds valuable context: key-value scoping by identifier, persistence details (24h for anonymous, persistent for authenticated), and storage behavior.

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

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

Three sentences, no redundant phrases, front-loaded with main purpose. Every sentence adds value.

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

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

For a simple tool with 2 string params, no output schema, and good annotations, the description is fully complete. Covers use cases, pairing, and behavioral differences.

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 'key' and 'value'. Description mentions key-value pair scoping but adds no new parameter details. 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 saves data for reuse across conversations or sessions, with specific verb 'Save' and resource 'data'. It distinguishes from siblings 'recall' and 'forget' by mentioning them.

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

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

Explicitly says when to use ('when you discover something worth carrying forward') and notes pairing with recall and forget. Also differentiates between authenticated and anonymous session behavior.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds significant behavioral context, such as cascading through multiple endpoints internally and auto-disambiguation for company inputs. This goes beyond the annotations and helps an agent understand the tool's composite 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 front-loaded with example queries and a clear statement of purpose. While it is relatively long, every sentence contributes meaningful information, such as usage guidance, type details, and internal behavior. It balances thoroughness with readability.

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

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

Given 100% schema coverage, good annotations, and no output schema, the description covers essential aspects: purpose, usage context, parameter examples, and return values for both entity types. It omits edge cases or error handling but is sufficiently 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% with parameter descriptions. The description adds substantial value by providing concrete examples of valid inputs (e.g., ticker 'AAPL', CIK '0000320193', drug names like 'ozempic') and explaining the output structure, which is not in the schema. This enriches the parameter understanding.

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

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

The description clearly states the tool resolves user-spoken names to canonical/official identifiers, with specific examples like 'What's the ticker for…' and lists supported types. It effectively distinguishes itself by emphasizing it is the first step for obtaining IDs needed by other tools, making its purpose unmistakable.

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

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

The description explicitly instructs 'Use FIRST whenever you have a name but need an ID,' providing clear context. It also notes it replaces 2-3 manual lookups. While it doesn't explicitly name alternatives or when not to use, the guidance is strong for a tool of this nature.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, signal density per entity, going beyond annotations.

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

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

The description is concise with two sentences and a utility note, front-loaded with the core action. 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 the tool has 4 parameters, no output schema, and moderate complexity, the description explains purpose, internal mechanism, output structure, and parameter roles completely. The agent has all necessary information to decide when to use and what to expect.

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

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

Schema coverage is 100%, but the description adds value by explaining that the first entity is treated as the subject, context disambiguates common names, and omitting models uses default workers-ai. These details go 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 it compares AI visibility across multiple entities side-by-side, using a specific verb (compare) and resource (AI visibility). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general compare) by focusing on multi-entity AI visibility comparison.

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

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

The description provides a concrete use case (competitive AI-marketing audits) and an example question. It implicitly distinguishes from single-entity checks by mentioning 'your brand + N competitors', but does not explicitly state 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 mark it as read-only, open-world, idempotent, non-destructive. The description adds behavioral details beyond these: partial failures for bundlephobia timing out, graceful degradation with 'sources_failed' listing. 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?

Concise yet information-dense. Front-loaded with purpose, followed by usage, output summary, caveats, and failure behavior. No filler; every sentence serves a purpose.

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

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

Despite no output schema, the description details the return structure (summary block fields, advisory details, links, alternatives) and failure handling. For a tool with 2 parameters, this is highly 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 covers both parameters fully (100% coverage). Description adds practical context: scoped packages accepted, default version behavior. This extra info justifies a score above baseline 3.

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

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

The description clearly states the tool's purpose: a composite check for deciding whether to add an npm package, aggregating data from deps.dev and bundlephobia. It distinguishes itself from sibling tools by its specific use case and output structure.

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

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

Explicitly states when to use: 'whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also specifies the ecosystem limitation (NPM only in v1) and suggests alternatives for other ecosystems.

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

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

Annotations already indicate read-only, idempotent, open-world. The description adds substantial behavioral context: explains underlying model (BGE-base-en, cosine, 500-char windows), cap (200K chars with truncation flag), and output characteristics (offsets, similarity scores). 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 paragraphs, front-loaded with the core purpose. Every sentence serves a purpose: what it does, when to use, how it works, technical details. Could be slightly more structured, but no waste.

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

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

Despite no output schema, the description explains the return format (passages with offsets and scores). It covers input constraints, use case, and pairing with another tool. Fully sufficient for an AI agent to understand the tool's behavior and output.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing examples for query, clarifying the max limit range (1-20, default 5), and noting the ~200K char cap for text. This goes beyond the schema's 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 verb 'search inside a fetched record' and specifies the resource as 'a fetched record' (e.g., SEC 10-K). It distinguishes from siblings like ask_pipeworx_grounded by noting they pair together, and emphasizes the key feature of returning passages with offsets and scores.

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: 'Use when the record is too big to cram into the prompt.' Also mentions pairing with ask_pipeworx_grounded. While it doesn't explicitly list when not to use, the context is clear enough for an AI agent to decide.

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

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

The description adds behavioral context beyond annotations: OAuth requirement, SMS cap, webhook auto-disable. However, it does not explicitly address the idempotentHint=true annotation; the tool's creation behavior is non-idempotent, leading to an annotation 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 well-structured with a clear first sentence, followed by requirements, types, and delivery. While dense, every sentence adds value; could be slightly more concise 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?

Despite no output schema, the description completely covers requirements, supported types with parameter examples, delivery channels with constraints, and return value (subscription id). Adequate for a complex subscription tool.

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

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

Schema coverage is 100% with descriptions, but the description significantly enhances meaning with concrete examples for each type, detailed delivery options (webhook signing, autodisable), and constraints (SMS cap, phone verification).

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

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

The description clearly states the verb 'Create', the resource 'subscription to a live-data event stream', and the outcome 'Returns the new subscription id'. It distinguishes from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by specifying proactive monitoring.

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 context: requires Pipeworx OAuth account, details type-specific parameters, and delivery channels. It implies when to use this tool (creating subscriptions) vs alternatives (list_subscriptions, unsubscribe), though not exhaustively excluding other contexts.

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

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

Annotations already indicate readOnly, idempotent, openWorld, non-destructive. Description adds value by explaining the output structure (category-bucketed example questions with tool+argument shape) and behavior variations (full spread vs. focused topic). 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?

Front-loaded with many synonymous questions, which is repetitive. Every sentence adds value but could be more concise. Structure is clear but verbose for the amount of 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 fully covers purpose, usage variations, output format, and when to invoke. No gaps identified.

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

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

Schema already describes the 'topic' parameter at 100% coverage. Description enhances it by listing specific focus areas (finance, pharma, etc.) and explaining that omitting it yields a cross-category spread. Adds practical context beyond schema.

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

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

Clearly states it is the onboarding entry point for suggesting questions and learning how to use Pipeworx. Distinguishes from siblings like ask_pipeworx and discover_tools by specifying it returns example questions with exact tool+argument shapes and should be used first.

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

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

Explicitly says 'Use this FIRST' when you don't know what Pipeworx can do, and explains how to call with or without the 'topic' argument. Lacks explicit when-not-to-use, but provides clear context and mentions learning to call meta-tools.

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

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

Annotations indicate non-destructive and non-read-only. Description adds ownership enforcement and deactivation (not deletion), preserving historical events. This adds value beyond annotations.

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

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

Three sentences, each providing essential information. Purpose is first, followed by constraints and behavior. 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?

Tool is simple with one parameter and no output schema. Description covers purpose, ownership, and lifecycle effect. Sufficient 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 covers the single 'id' parameter fully. Description adds context: 'subscription id returned by subscribe', reinforcing usage. Baseline 3 with high coverage, but description adds extra clarity.

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 'Cancel a subscription by id' which is a specific verb+resource. It clearly 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?

The description mentions ownership enforcement and deactivation behavior, providing clear context for when to use. However, it does not explicitly exclude scenarios or name 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 cover readOnly, openWorld, idempotent, non-destructive hints. Description adds valuable behavioral details: returns verdict, extracted form, actual value with citation, percent delta, and mentions single-call replacement of 4-6 steps. 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 reasonably concise given the tool's complexity, uses front-loaded examples, and is well-structured. Some redundancy with sibling mention, but overall efficient.

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

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

Despite having no output schema, the description fully explains return values and behavior. Covers use cases, scope, and limitations. Complete for a single-parameter tool with 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?

Only one parameter with 100% schema description coverage. Description adds examples of claim format (e.g., 'Apple's FY2024 revenue was $400 billion'), but schema already defines it clearly. 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?

Description uses specific verbs like 'fact check' and 'verify', defines the tool as natural-language claim verification, and explicitly distinguishes from sibling tools by noting it replaces multiple sequential calls.

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 scopes to company-financial claims for public US companies, providing clear when-to-use guidance.

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