Podcastindex

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds that it probes multiple models, defaults to free Workers AI, requires BYO key for Anthropic, and returns per-model details with combined view. No contradictions.

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

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

Three sentences with no fluff: first states core action, second details models and API key, third covers return value and use cases. Front-loaded with essential info.

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

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

Despite no output schema, description explains return format (per-model fields + combined view). Covers use cases, required parameters, and optional behavior. No gaps.

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

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

All parameters have schema descriptions (100% coverage). Description adds context: default model for 'models', purpose of '_apiKey', and how 'context' disambiguates. Enhances understanding beyond schema.

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

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

The description uses a specific verb (probe), identifies the resource (LLMs), and clarifies the output (visibility score 0-100). It distinguishes itself from siblings like 'ask_pipeworx' by focusing on AI visibility audits.

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 cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). Doesn't explain when not to use, but the use cases are clear. No mention of 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 mark it read-only, open-world, idempotent, and non-destructive. The description adds meaningful behavior: it routes to sub-tools, fills arguments, returns structured answers with stable pipeworx:// citation URIs, and notes one fast call on every tier. It also implies a limitation by directing to ask_pipeworx_grounded for hallucination-resistant answers.

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 densely informative, front-loaded with the most important usage rule and followed by examples and alternatives. It is slightly wordy with repetitive domain lists, but every sentence contributes actionable guidance, so it earns above minimum.

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 router-like complexity and lack of output schema, the description provides comprehensive context: when to use, what it returns, citation URIs, starting point guidance, alternatives, and real-world examples. It fully equips an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, documenting the single required question parameter and five aliases with descriptions. The tool description itself adds no parameter-level detail beyond what the schema already provides, so this is a baseline-3 case where the schema does the 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 clearly states that ask_pipeworx routes questions to one of 5,521 tools across sources and returns structured answers with citations. It differentiates from siblings by positioning itself as the default entry point and explicitly contrasting with ask_pipeworx_grounded and deep_research.

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

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

The description gives explicit when-to-use guidance: 'PREFER OVER WEB SEARCH' for many domains, 'START HERE for most questions', and step-up alternatives for grounded answers or deep research. It also clarifies that breaking-news is handled internally, leaving no ambiguity.

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

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

Beyond the annotations (read-only, open world, idempotent), the disclosure adds critical behavioral context: the beta/experimental nature, live candidate routing improvements, the current state of no active candidate, and that it does not fall back to anything but is a fully functional router. This transparency goes well beyond what annotations 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 efficiently structured, front-loading the core purpose ('Beta version of ask_pipeworx') followed by essential details about current behavior, usage, and fallback. Each sentence contributes unique information without redundancy, making it appropriately sized 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?

For a beta router with a simple single-question parameter, the description is quite complete. It explains the relationship to the stable ask_pipeworx, current state, and usage. However, it does not explicitly describe the response format (only references 'same response shape'), which could be more explicit given the absence of an output schema.

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

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

The input schema covers 100% of parameters with descriptions (all aliases mapped to 'question'). The description adds no additional parameter-level detail beyond 'the same arguments' as ask_pipeworx, so with high schema coverage, a baseline score of 3 is appropriate.

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

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

The description clearly identifies the tool as a beta version of ask_pipeworx, a universal router with 5,521 tools, and explicitly differentiates it from the stable sibling by mentioning the experimental routing improvements. It states a specific verb (ask) and resource (pipeworx), 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 provides explicit usage guidance: 'Use it exactly like ask_pipeworx when you want the newest routing' and notes that results are compared against the stable router. This tells the agent when to use this tool and implies the alternative (ask_pipeworx) for stable routing, satisfying the when/when-not/alternatives criterion.

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 richly discloses behavioral traits beyond the annotations: it will only extract answers using what the tool result contains, returns a structured response with evidence and confidence, and explicitly refuses with specific refusal reasons when data doesn't directly answer. It also notes the extra LLM call cost. This adds substantial context beyond the readOnly/idempotent hints.

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 dense but well-organized, front-loading the core purpose ('Hallucination-resistant answer mode') before explaining mechanics. Every sentence adds value: routing details, return format, refusal reasons, use cases, and cost trade-off. Slightly verbose, but not wasteful.

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

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

Given the tool's moderate complexity and lack of an output schema, the description provides complete context: the full return structure on success and failure, refusal reasons, examples of use cases (financial, legal, medical), and the comparison with ask_pipeworx. It covers all necessary aspects for an agent to select and invoke this tool confidently.

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

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

The input schema is fully self-documenting with 100% coverage: all six parameters are aliases for question, and the 'question' field is described as 'Your question in natural language.' The description adds no additional parameter semantics, but the schema is sufficient, meeting the baseline for full 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's function as a 'hallucination-resistant answer mode for high-stakes reads' and describes the specific process of picking tools, fetching data, and extracting answers only from tool results. It distinguishes itself from the sibling ask_pipeworx with a clear differentiation: grounded extraction with evidence and refusal handling.

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

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

The description explicitly states when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also provides an alternative and trade-off: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This is clear when/when-not guidance with named 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 and idempotentHint. Description adds extensive behavioral details: low-confidence short-circuit, closed market status, spread warnings, resolution-rule risk, and response shapes. 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 very long but well-structured with bolded section headers. Front-loaded with purpose and usage. However, it is overly verbose with many details that could be condensed. Loses points for not being concise.

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

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

Given no output schema, description comprehensively explains all return fields: result.market, result.analysis, result.evidence, resolver contract, parent event, news fields, and edge cases. Covers all necessary context for correct 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 coverage is 100%, so baseline is 3. Description adds significant extra context: market parameter explains acceptable formats (slug, URL, question text), depth explains quick vs thorough, include_raw explains rationale for default false. Exceeds baseline.

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

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

Description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, specifying the verb and resource. It distinguishes from siblings like polymarket_arbitrage and polymarket_edges, implying this is the general bet research tool.

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

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

Explicitly lists use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and provides classifiers and fan-out examples. Does not explicitly state when not to use, but context is clear enough.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds valuable context beyond annotations: it discloses data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs. No contradictions.

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

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

The description is compact yet informative, front-loading the core purpose with examples. It contains about 5 sentences, each adding value. Some redundancy (e.g., repeating 'side-by-side comparison' and 'head to head') but overall efficient. Minor room for tightening.

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

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

Given the tool's moderate complexity (two entity types, multiple data sources, no output schema), the description covers key aspects: what data is returned per type, sorting behavior, and citation URIs. It also explains edge cases like off-calendar fiscal years. The absence of an output schema is mitigated by mentioning 'paired data + citation URIs'. Sufficient for confident 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 clear parameter descriptions, so baseline is 3. The description adds significant meaning: for 'type', it specifies what data is pulled for company (revenue, net income, cash, debt) and drug (adverse events, FDA approvals, trials). For 'values', it explains tickers/CIKs vs. drug names. This extra context helps the agent understand the semantics beyond the enum and array constraints.

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

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

The description clearly states the tool does side-by-side comparison of 2-5 companies or drugs, with specific verb-resource pairs like 'compare entities'. It distinguishes from siblings such as entity_profile by saying it replaces sequential lookups and is preferred over single-pack 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?

Explicit guidance is provided: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It gives natural language examples for when to use (e.g., 'which is bigger/better', 'rank these companies') and implies not to use for single entity lookups.

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

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

Beyond the readOnlyHint/openWorldHint/idempotentHint annotations, the description discloses account requirements, return packet structure (verbatim evidence, confidence, source, citation_uri, gaps[]), contradictions, semantic excerpting, and latency. These go far beyond what annotations convey and help set expectations for the agent.

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 every sentence supplies meaningful operational detail—auth, alternatives, internal decomposition, output format, citation semantics, latency. It's not fluff but could be better structured with bullets instead of a wall of text. The density justifies a 4 rather than 3.

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

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

Given the tool's complexity, the description covers auth, prerequisites, alternatives, output schema (since none is provided), latency, and edge cases (gaps, contradictions). An agent has everything needed to decide when to invoke and what to expect, making this 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 descriptions already cover both parameters (question, depth) with 100% coverage. The description adds extra semantic nuance for depth levels (gap recovery, follow-up hops, paid tier for 'thorough') and emphasizes that the question should be natural-language and can be broad, which the schema doesn't fully express. Still, most parameter meaning is in schema, so a 4 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 performs 'grounded multi-source research' across Pipeworx's structured data sources, decomposes questions, and routes to tools. It explicitly distinguishes itself from open-web search and from ask_pipeworx for single lookups, so an agent knows exactly what this tool does vs 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?

Provides explicit when-to-use guidance: use ask_pipeworx if not signed in, for single lookups, or for breaking/current news; recommends deep_research for broad/multi-part questions over structured data. Also explains depth-level choices, so the agent can select the right mode.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context about the output format (names, descriptions, full schemas with examples) and readiness to call, 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?

The description is relatively concise, front-loaded with the core purpose, and efficiently covers domains, output, and usage guidance. Every sentence adds value, though could be slightly more streamlined.

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

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

With no output schema, the description fully accounts for explaining the return value (top-N tools with names, descriptions, schemas, examples). It covers all necessary context for a discovery tool, including the directive to call first.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning by providing concrete examples of query values (e.g., 'look up FDA drug approvals') and explaining the aliases, which aids understanding 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 states a specific verb ('Find') and resource ('tools'), lists concrete data domains, and distinguishes from siblings by positioning it as the discovery tool to call first when many tools are available.

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

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

The description explicitly says when to use (browse, search, discover tools) and gives a clear directive to call it first in multi-tool scenarios. It doesn't provide explicit 'when not to use' or alternative names, but the context strongly implies its role.

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

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

Despite annotations already indicating readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds valuable behavioral context: it fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), mentions specific return fields, and discloses limitations like the USPTO API sunset soft-failure and GDELT→GNews fallback. No contradictions with annotations.

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

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

The description is somewhat long (multiple sentences) but each sentence adds value: example queries, explicit usage preference, list of data sources and return fields, and constraint notes. It is front-loaded with examples and avoids redundancy. Could be slightly more concise but is well-structured for the complexity.

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

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

Given the tool's complexity (multiple data sources, potential failures, and no output schema), the description is remarkably complete. It covers edge cases (name unsupported, patent API sunset soft-fail, news fallback), lists all return fields with details (e.g., pipeworx:// URIs for filings), and directs to resolve_entity for missing functionality. No gaps for an agent to guess.

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

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

Schema coverage is 100%, with both parameters already described in the JSON schema. The description adds context beyond schema: explains that 'type' currently only supports 'company' (others coming soon), and 'value' must be ticker or zero-padded CIK, not names. It also links usage to resolve_entity for names, adding practical guidance.

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

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

The description clearly states the tool's purpose as 'full cross-source profile of a US public company in ONE parallel call' with concrete example queries (e.g., 'tell me about X', 'research Acme'). It differentiates from sibling tools like resolve_entity for name resolution and compare_entities for comparisons.

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

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

Explicitly advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also specifies input constraints: 'Pass ticker "AAPL" or zero-padded CIK... names not supported (use resolve_entity first if you only have a name).' 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds no behavioral traits beyond annotations; it mentions 'recent' but that's data-centric, not behavioral. No contradiction.

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

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

Two concise sentences plus a clear example. No wasted words, front-loaded with the main 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 list tool without an output schema, the description is fairly complete: it explains the function, returned fields, and includes an example. However, it lacks details on pagination, error handling, or whether episodes are strictly recent or all available.

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 adds an example call but does not provide semantic value beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states 'List recent episodes for a podcast by its Podcast Index feed ID.' It specifies the verb (list), resource (episodes), and identifier (feed ID), and lists returned fields, distinguishing it from siblings like 'get_podcast' and 'search_podcasts'.

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 what the tool does but does not explicitly state when to use it versus alternatives or provide exclusions. The example implies typical usage, but no guidance on when not to use or which sibling to prefer.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data, which reinforces destructive nature but doesn't add significant new behavioral info beyond annotations.

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

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

Two sentences: first for purpose, second for usage guidelines. Front-loaded and efficient with no unnecessary words.

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

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

Given the tool's simplicity (one param, no output schema), the description covers purpose and usage adequately. It could mention behavior when key doesn't exist, but idempotence is hinted.

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

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

Schema coverage is 100% with a single 'key' parameter described as 'Memory key to delete'. The description does not add extra meaning beyond what the schema provides, so baseline of 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key' with a specific verb (delete) and resource (memory). It distinguishes from sibling tools like 'remember' and 'recall' by specifying the action of deletion.

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

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

The description provides explicit scenarios for use: 'when context is stale, the task is done, or you want to clear sensitive data'. It also references pairing with 'remember' and 'recall', though it lacks explicit when-not instructions.

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

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

The description discloses the tool's behavior: fetches the page, extracts title/description/key links, and emits standard markdown format. This adds value beyond the annotations (readOnlyHint, idempotentHint, etc.) by explaining the actual operations and the output nature. 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 three well-structured sentences. The first sentence states the main purpose and target audience, the second explains the process, and the third lists use cases. No redundant or superfluous 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 low complexity (2 parameters, simple fetch+extract), the description covers the purpose, process, output format, and use cases comprehensively. Even without an output schema, the description clearly communicates what the tool returns.

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

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

Schema description coverage is 100%, but the description adds extra context such as the default value of 25 and max of 50 for max_links, and explains that the output is a single text blob ready to drop at site-root/llms.txt. This goes beyond the schema's basic type and description.

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

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

The description clearly states the tool's purpose: generating a production-ready llms.txt file for any URL. It specifies the action (generate), the output format (standard llms.txt markdown), and distinguishes from siblings like scan_competitor_ai_presence by focusing on AI crawler indexing.

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

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

The description explicitly lists three use cases: getting a client's site indexed, drafting your own llms.txt, and auditing competitor visibility. It provides clear context on when to use, though it does not explicitly mention when not to use or name 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 readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false, so the core behavioral traits are covered. The description adds value by listing the types of metadata returned and providing an example, but does not disclose additional behaviors like rate limits or authentication requirements beyond the schema.

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

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

The description is a single concise sentence followed by an illustrative example. Every part earns its place with no redundancy or unnecessary information.

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

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

Given that there is no output schema, the description compensates by listing the returned fields. For a read-only, idempotent tool with good annotations, the description is reasonably complete. It could mention potential error conditions, but overall it provides sufficient context for correct selection and 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% (both parameters described in the input schema). The description mentions the feed_id example and the output fields, but does not add significant new meaning beyond what the schema already provides. The _apiKey parameter's purpose is described similarly in both places.

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 a specific verb (Get), resource (full metadata for a single podcast), and method (by Podcast Index feed ID). It lists the returned fields, making the purpose immediately clear. It naturally distinguishes from sibling tools like search_podcasts and episodes.

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

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

The description clearly states when to use the tool (retrieve metadata for a single podcast given a feed ID) and implies that the feed ID comes from search_podcasts or trending. It does not explicitly discuss when not to use it or provide alternative tools, but the context is sufficient for an AI agent.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by specifying return fields and default filtering (active only), which goes beyond what annotations 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 extremely concise, with two short sentences that efficiently convey purpose, output, and usage. 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?

Given the tool's simplicity (single optional parameter, no output schema), the description adequately covers return fields and usage. Could mention if there is pagination or ordering, but not necessary for this case.

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 the single parameter. The description does not add significant meaning beyond the schema's own description, as it merely implies the default behavior without additional insight.

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

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

The description clearly states the action ('List') and resource ('the caller's active subscriptions'), and the return fields are specified. It effectively distinguishes itself from sibling tools like 'subscribe' and 'unsubscribe'.

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

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

Explicit usage guidance is given: review before adding more subscriptions or find an id to cancel. This provides clear context for when to use the tool, though alternative tools are not named.

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 hints (all false), so the description carries the full burden. It discloses the claim_token workflow (returned when filing without an account, reusable to read resolution), the team's daily digest review, the 5-per-day rate limit, quota exemption, and the scope restriction to this Pipeworx connection. This is rich behavioral disclosure far beyond what annotations offer.

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?

Though the description is long, every sentence earns its place: purpose, use cases, exclusion scope, formatting instructions, token behavior, and rate limits. It is front-loaded with the core function and then layers details in logical order, making it appropriately sized 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?

With no output schema, the description must explain return values, and it does: it states that filing without an account returns a claim_token and explains the follow-up call to retrieve resolution. It also covers the four feedback types, scope boundaries, operational limits, and the relationship to the schema's nested context object. This is complete given the tool's complexity.

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

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

Schema coverage is 100% and each parameter already has a description, so the baseline is 3. The description adds meaningful context for the claim_token parameter by explaining how to pass it back to read whether the report was fixed, and hints at the context parameter usage ('describe the issue in terms of Pipeworx tools/packs'). This extra usage context justifies a 4.

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

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

The description opens with a clear, specific verb phrase 'Tell the Pipeworx team something is broken, missing, or needs to exist,' and immediately enumerates distinct use cases (bug, feature/data_gap, praise). This distinguishes it sharply from the sibling research/search tools and makes the tool's purpose unambiguous.

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

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

The description explicitly tells when to use the tool for each feedback type and, critically, when NOT to use it: it excludes tools from other MCP servers and directs the user to file with that server instead. It also provides formatting guidance (describe in terms of Pipeworx tools/packs, don't paste the end-user prompt), going well beyond basic usage.

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

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

The description adds valuable behavioral context beyond annotations: it explains the data is self-aggregating from analytics, no PII, and cached 5min-1h. This complements the readOnlyHint, idempotentHint, and openWorldHint 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?

The description is well-structured with a clear opening statement and bullet-point use cases. It is concise without unnecessary words, though the bullet list could be integrated for even tighter prose.

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 explicitly lists what is returned (top tools, top packs, total call volume) and covers caching and derivation. For a simple tool with one optional parameter, this is fully complete.

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

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

The schema covers 100% of the parameter 'window' with a description. The tool description adds further nuance by explaining that shorter windows surface current trends while longer windows show steady-state demand, going beyond the schema's explanation.

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

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

The description clearly states the tool returns trending data from other AI agents on Pipeworx, including top tools, packs, and call volume. It uses a specific verb ('returns') and resource, distinguishing itself from sibling tools like 'trending' or 'discover_tools' by focusing on Pipeworx-specific trending.

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 enumerates three concrete use cases (discovering hot data sources, confirming canonical tools, assessing alignment) and notes caching behavior. However, it does not explicitly state when NOT to use or mention alternative sibling tools, slightly reducing score.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. The description adds rich behavioral context: monotonicity checks, partition_sum, Jaccard similarity filter, placeholder filtering, and the fill check that queries live CLOB depth before suggesting trades. 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 well-structured with sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and is front-loaded with the main purpose. However, it is lengthy; some details could be condensed without losing value.

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

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

Despite no output schema, the description thoroughly explains the response format (opportunities[], partition_check fields, fill_check results) and edge cases (skipped_low_similarity, placeholders). It covers all necessary context for an AI agent to invoke the tool correctly.

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

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

Both parameters have schema descriptions (100% coverage). The description adds meaning: event accepts full URLs, topic triggers cross-event scanning with seed questions. It explains how each parameter affects the analysis, going 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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) and specifies what each does, differentiating 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 guides when to use each mode: 'Call with NO args for a trending_scan', pass event for a specific market, or topic for cross-event scanning. It also explains trade-offs (cross-event mode catches patterns single-event misses) and prerequisites like the fill check condition.

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

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

Annotations already indicate safe, idempotent read behavior. The description adds rich behavioral context: caching (1h), three model families, edge calculation with slippage and Kelly caps, 24h-move warnings, Fed bet exclusion, and diagnostic output. 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?

Despite its length, every sentence serves a purpose. The description is front-loaded with the core purpose, then structured into clear sections (model families, response segments, diagnostics). No fluff; highly informative and well-organized.

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

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

The description completely explains the output structure (by_segment, diagnostics), what each opportunity contains (edge_pp_net, kelly_fraction, liquidity, etc.), why segments might be empty, and how knobs affect results. For a complex tool with no output schema, this provides all necessary context.

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

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

Schema coverage is 100% with detailed parameter descriptions. The tool description explains the role of tradeable-edge knobs (min_liquidity, max_spread_pp, min_partition_leg_kelly) in the context of the overall algorithm, but does not add significant meaning beyond the existing schema descriptions. 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 scans top Polymarket markets for discrepancies with Pipeworx data, and is built for 'what should I bet on today'. It defines three model families, but does not explicitly contrast with siblings like polymarket_arbitrage or polymarket_edge_tracker, though the purpose is distinct.

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 frames the tool for daily bet discovery without browsing hundreds of markets. It explains tradeable-edge knobs and diagnostics for empty segments, but does not specify when to avoid this tool or list 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 mark it as read-only and idempotent. The description adds rich behavioral details: it reads from daily snapshots, explains response structure (tracked, expired, snapshot_dates), and discloses limits (60-day TTL, decay from closes). 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 detailed and well-structured, starting with purpose, then answering a key question, explaining parameters, and detailing response format. It is somewhat lengthy but every sentence adds value.

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

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

Given 2 optional parameters and no output schema, the description fully explains the response format (tracked[], expired[], snapshot_dates[]) and all fields, plus limits. It is complete and leaves little ambiguity.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions defaults ('default 14', 'default 1wk') and clamps (days 2-30) but does not add significant new semantics beyond what the schema already provides.

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

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

The description clearly states the tool's purpose: 'Edge persistence and decay telemetry' for answering how long an edge has existed and if it's shrinking. It distinguishes from sibling tools like polymarket_edges by focusing on historical trends and decay, not just current edges.

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

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

The description explicitly answers a specific question ('how long has this edge existed and is it shrinking?'), implying when to use it. It does not explicitly state alternatives or when not to use, but the purpose is clear enough for an agent to infer appropriate 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 indicate read-only, open-world, idempotent, non-destructive. Description adds essential behavioral context: it's a check tool, explains the risk of partial fills and unhedged positions. 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 lengthy but well-structured: starts with core purpose, then separates single-market and basket modes with clear bullet-like lists. Every sentence provides necessary detail. Slightly verbose but justifiable given 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 there is no output schema, the description fully enumerates return fields for both modes (top_of_book, vwap_fill_price, verdict, capture_ratio, profit_usd, thin_legs, etc.) and explains key risks. No gaps for an experienced trader using the tool.

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

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

Schema has 100% description coverage, but the description adds significant semantic value: explains the dual modes (market vs event), default side selection logic, and interpretation of size_usd for each mode. This goes beyond the basic 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?

Description clearly states the tool checks realizable vs theoretical edge against live CLOB order-book depth, and distinguishes between single-market and basket modes with specific verbs and resources. It separates itself from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly tells when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also warns about partial basket fills converting arb into directional position, providing clear guidance on when not to rely on theoretical overround.

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

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

The description discloses extensive behavioral traits beyond the annotations: it explains the compatibility_warning fields, temporal alignment, skipped cross types, and that most pre-mapped topics return warnings. This sets clear expectations for the agent.

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

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

The description is well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loads the core purpose. While slightly verbose, every sentence adds value. It could be trimmed without losing information.

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

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

Despite no output schema, the description thoroughly explains the response fields (leg-by-leg prices, spread, compatibility_warning, temporal_alignment, skipped counters), making it self-contained. All edge cases and warnings are covered.

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

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

With 100% schema coverage, baseline is 3. The description adds meaningful context by explaining the two modes, listing the exact topic shortcuts, and clarifying that explicit overrides override the topic mapping. The examples further enhance 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 computes the cross-venue spread between Kalshi and Polymarket for the same resolving question, distinguishing it from sibling tools like polymarket_arbitrage. It specifies two modes (topic shortcuts and explicit event pairs) and explains the output format, leaving no ambiguity.

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 strong usage context, including when to use each mode and the limitations (e.g., compatibility_warning cases indicating when arbitrage does not exist). However, it does not explicitly compare to alternative tools like polymarket_arbitrage or advise when to use this tool instead.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds scope detail (scoped to identifier) and pairing behavior, surpassing the annotations. No contradiction.

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

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

Three sentences, front-loaded with main purpose. Every sentence adds value: retrieval behavior, use case, scope, and pairing. No redundancy.

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

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

For a simple retrieval tool with one optional parameter and strong annotations, the description covers purpose, usage, parameters, scope, and relationship to siblings. No output schema is needed. 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 description for 'key'. The description adds critical behavior: 'omit to list all keys', which is not in the schema. This adds practical meaning.

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

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

The description uses specific verbs ('Retrieve', 'list') and identifies the resource (values saved via remember, saved keys). It clearly distinguishes from sibling tools remember (save) and forget (delete).

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

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

Explicitly states when to use ('look up context... without re-deriving') and implies when not to (omit key for listing). Pairing advice with remember/forget provides clear context, though no explicit exclusion of alternatives.

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

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

The description fully discloses the tool's behavior including the effect of mark_read (flagging events as read so next call shows newer ones), the returned fields, filtering options, and polling suitability. However, note the annotation contradiction: readOnlyHint=true conflicts with the ability to mark events read.

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

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

The description is concise, front-loaded with the purpose, and every sentence adds value. No redundant or irrelevant information.

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

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

Despite no output schema, the description explains what each returned alert contains (source, citation_uri, raw payload), covers side effects, filtering, and even provides an alternative access method. It is complete for a read tool with good annotations.

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

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

Schema coverage is 100%, providing baseline. The description adds meaning by explaining the effect of mark_read ('so the next call only shows newer ones') and giving an example for type ('sec_8k'). This adds value beyond the schema.

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

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

The description clearly states 'Pull fired events from your subscription feed' and details what is returned. It is specific about the resource and action, distinguishing it from sibling 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 provides clear context for using the tool (e.g., filtering by type and since, polling) but does not explicitly tell when to use it over alternatives or when not to use it. It mentions an alternate HTTP endpoint but no sibling tool comparison.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds substantial behavioral context: fans out to multiple sources (SEC, GDELT→GNews, USPTO with soft-fail), accepts ISO or relative dates, returns structured changes with URIs, and notes parallel execution. This goes well 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 detailed but front-loaded with example queries that quickly convey purpose. Every sentence adds necessary information, though the length could be slightly trimmed. The structure is logical, starting with purpose, then behavior, then parameter tips, and ending with sibling distinction.

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 3 required parameters and no output schema, the description thoroughly explains the return format (changes[] array, total_changes, citation URIs) and the multi-source behavior. It also covers edge cases like fallback and sunset. The agent can fully understand what to expect without additional documentation.

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

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

The input schema has 100% description coverage, providing clear definitions for all three parameters. The description adds extra value by including examples for 'since' (e.g., "7d", "3m"), specifying that 'value' can be a ticker or CIK, and noting that 'type' is currently fixed to 'company'. This expands on the schema, though not massively.

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 opens with concrete example queries like "What's new with X" and clearly defines the tool as a change feed for a company over a time window. It explicitly distinguishes itself from the sibling tool 'entity_profile' by stating when to use the alternative.

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

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

Provides explicit guidance on when to use this tool versus 'entity_profile' and gives a recommended default for the 'since' parameter ("30d" or "1m"). It also details fallback behavior and known limitations (PatentsView API sunset), helping the agent decide correctly.

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

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

Discloses key behavioral traits: key-value storage scoped by identifier, persistence differences between authenticated and anonymous sessions (24 hours). This adds value beyond annotations, which only indicate idempotence.

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?

Five sentences efficiently cover purpose, usage, technical details, and tool relationships. No extraneous content.

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

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

Missing no critical information for a simple key-value store. No output schema needed as behavior is clear. Could mention error conditions or size limits, but overall complete.

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

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

The input schema has 100% description coverage with examples. The tool description repeats these examples without adding new parameter-level detail, so baseline 3 applies.

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

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

The description clearly states the tool saves data for later reuse, with specific examples (tickers, addresses, preferences). It distinguishes from sibling tools by explicitly mentioning recall and forget.

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

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

Provides explicit guidance on when to use: when discovering something worth carrying forward. It also advises pairing with recall and forget, offering clear context for alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context beyond this: it reveals internal cascading ('cascades through several lookup endpoints internally'), auto-disambiguation, and the specific outputs (ticker, CIK, RxCUI, citation URIs). This goes well beyond the annotations, disclosing performance and result characteristics. No contradiction.

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

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

The description is dense but well-structured: it opens with example queries, states the core purpose, then breaks down supported types with outputs in a scannable format. Every sentence contributes either purpose, usage, or parameter context. Despite the length, it is not verbose and reads 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?

Given the absence of an output schema, the description fully explains return values for both entity types (company: ticker, CIK, company_name, citation URI; drug: RxCUI, ingredient, brand, citation). It also covers accepted input formats, the auto-disambiguation behavior, and the internal cascading. This is complete for a lookup tool with two params and clear annotations.

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

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

Schema coverage is 100% and the schema descriptions already detail each parameter (type enum, value formats for company and drug). The description largely repeats these formats, adding only the note about auto-disambiguation and cascading behavior. Since the schema carries the parameter semantics, the description adds marginal value, matching the baseline of 3 for high schema coverage.

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

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

The description clearly states the tool's function: 'resolve a user-spoken NAME to the canonical/official identifier other tools require as input.' It specifies concrete examples ('What's the ticker for...', 'find the CIK for...'), distinguishes it from siblings by positioning it as the initial lookup step, and mentions supported entity types (company, drug). This is a specific verb+resource description that avoids tautology.

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 usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains the tool's advantage ('replaces 2-3 manual lookups'), implying it should be preferred over multi-step processes. However, it does not explicitly name alternative tools or specify cases where this tool should NOT be used, so it misses the full 'when-not/alternatives' criterion.

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, openWorldHint. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns metrics (score, confidence, signal density), which is useful beyond annotations. No contradiction.

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

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

The description is concise (5 sentences), front-loaded with the core purpose, and every sentence contributes value without redundancy.

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

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

Given no output schema and 4 parameters, the description adequately explains input, process, and output format. It does not cover error handling but is sufficient for typical 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%. The description adds extra meaning by explaining the first entity is treated as the subject and the context parameter for disambiguation, going beyond 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 specifies a verb ('scan', 'compare') and resource ('competitor AI presence'), and distinguishes from sibling tools like ai_visibility_check by emphasizing multi-entity comparison and ranking.

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 includes a concrete use case (competitive AI-marketing audits) and implies when to use it, but does not explicitly state when not to use it or name alternatives like compare_entities.

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

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

Discloses behavior beyond annotations: composite call to two services, potential 5-30s delay on bundlephobia first measurement, graceful degradation with sources_failed list. No contradiction with annotations (readOnlyHint, idempotentHint all consistent).

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

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

Dense but efficient; front-loaded with core purpose. Could be slightly more concise but every sentence adds value, including performance note and fallback behavior.

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

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

Despite no output schema, description thoroughly details return structure (summary block, per-advisory detail, links, alternative versions) and partial failure handling. Fully covers what an agent needs to know given tool complexity.

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

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

Schema coverage is 100%, baseline 3. Description adds context: scoped packages accepted, version defaults to latest, and ecosystem constraint. This adds marginal but meaningful value 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 tool performs a composite check for adding npm packages, covering license, advisories, bundle size, etc. It distinguishes itself by specifying the exact use case ('is X safe / popular / small') and the services used (deps.dev, bundlephobia), and is unique among sibling tools.

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

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

Explicitly states when to use ('whenever an agent asks...') and when not to ('NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly'), providing clear alternatives and handling of partial failures.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, etc. The description adds context about the database source and return format but does not mention rate limits, cost, or behavior when the optional _apiKey is used. Since annotations carry the safety profile, the description is sufficient.

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

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

The description is extremely concise: two clarifying sentences plus a code example. It front-loads the purpose and includes a helpful example. No redundant or irrelevant 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?

The description covers the database source, search behavior, and return fields. It lacks information on pagination (e.g., offset/cursor) or sorting order, but for a simple search list tool with no output schema, this is adequate. The example provides concrete usage context.

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

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

With 100% schema description coverage, the schema fully explains all three parameters. The description provides an example usage pattern but does not add new semantic meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'search' and resource 'podcasts', and specifies the search domain ('open podcast database'). It lists return fields (title, author, description, etc.), making the purpose very specific. However, it does not explicitly differentiate from sibling tools like 'get_podcast' or 'episodes', which slightly reduces clarity.

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

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

The description implies usage via the example and mentions searching by term, but does not provide guidance on when to use this tool versus alternatives (e.g., 'get_podcast' for a single result or 'episodes' for specific episodes). No exclusions or prerequisites are stated.

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

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

Discloses retrieval method (BGE-base-en embeddings, cosine similarity, 500-char windows), output structure (passages with offsets and scores), and truncation behavior (200K char cap with flagging). This goes well beyond the annotations (readOnlyHint, idempotentHint) and fully informs the agent.

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 core action and packs all essential information without redundancy. Every sentence earns its place.

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

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

Despite having no output schema, the description details return values (passages with offsets and scores), truncation behavior, and embedding model. This makes the tool fully self-contained and actionable.

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 value by explaining text as a fetched record, query as natural language with examples, and limit as max passages. This semantic enrichment helps the agent craft correct invocations.

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

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

The description clearly states the tool performs semantic search inside a single fetched record, using verbs like 'search INSIDE' and examples (SEC filing, article). It distinguishes itself from sibling tools by focusing on document-level retrieval rather than cross-document search, and even suggests a pairing with ask_pipeworx_grounded.

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

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

Explicitly tells when to use: 'when the record is too big to cram into the prompt.' Also describes how it pairs with ask_pipeworx_grounded for grounded answers. Provides clear context and alternatives, making it easy for an agent to decide.

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

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

The description discloses behavioral details such as the requirement for an OAuth account, the persistent feed, optional delivery channels with specific constraints (e.g., phone verification, 10/day SMS cap, webhook auto-disabling). Annotations (idempotentHint=true) are complemented by the description's detail on subscription creation, with no contradiction.

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

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

The description is front-loaded with the core purpose, followed by structured details on requirements, types, and delivery. Every sentence adds necessary information without redundancy, maintaining conciseness despite its length.

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

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

Given the tool's complexity (3 parameters, nested objects, no output schema), the description covers purpose, return value, prerequisites, all subscription types and their parameters, delivery options, and even references related tools. It is fully actionable for an AI agent.

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

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

The schema has 100% coverage, but the description adds significant value by explaining type-specific parameters with examples (e.g., sec_8k items codes, polymarket_edge topics) and detailing the delivery object fields 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 starts with 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' This clearly states the verb (create) and resource (subscription), distinguishing it from sibling tools like list_subscriptions (list) and unsubscribe (remove).

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 a Pipeworx OAuth account, lists supported types and delivery channels, and differentiates from recent_alerts for pulling alerts. It provides clear context on when to use the tool and mentions 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable context: it is the onboarding entry point, returns categorized examples, and shows exact tool+argument shapes. No contradiction.

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

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

The description is slightly long but well-structured with a front-loaded list of synonyms and clear explanations. Every sentence provides value, though minor tightening is possible.

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 single optional parameter, no output schema, and annotations covering safety and idempotency, the description is complete: it explains what the output looks like, when to use it, how to use it, and provides examples.

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

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

Schema description coverage is 100% for the single optional parameter (topic), listing allowed values. The description adds further context by showing examples of topic values and explaining that omitting gives a full spread.

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

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

The description clearly defines the tool as an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It distinguishes itself from siblings by specifying 'use this FIRST' and listing alternative meta-tools.

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

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

Explicitly states when to use the tool ('when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'), how to use it (no args or topic argument), and provides references to 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 readOnly, idempotent, and non-destructive behavior. The description adds concrete behavioral details: it returns specific fields (title, author, categories, artwork, trend score) and provides an example call. This adds meaningful context beyond annotations. No contradictions.

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

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

The description is two concise sentences plus a clear example. Every sentence adds value: first states purpose and filters, second lists return fields, third shows usage. No redundancy, perfectly front-loaded.

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

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

Given there is no output schema, the description sufficiently explains what is returned. The tool is simple and the description covers key aspects: source, ranking, filters, and return fields. It could mention pagination or default limit, but the schema's max property description covers the limit. Overall complete enough for this tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description mentions optional filters and the example shows parameter usage, but it does not explain semantics beyond what the schema already provides (e.g., default values or format constraints). This is adequate but not exceptional.

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

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

The description clearly states the action ('Get trending podcasts'), the resource ('across the open podcast database'), and the ranking basis ('ranked by recent activity'). It also mentions optional filters. However, it does not differentiate from the sibling tool 'pipeworx_trending', so it loses a point for not helping the agent distinguish between similar tools.

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

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

The description provides a clear use case (getting trending podcasts) and includes an example invocation. However, it does not specify when not to use this tool or mention alternatives, leaving the agent to infer usage boundaries. This is adequate but lacks explicit 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?

Description adds context beyond annotations: it clarifies that the mutation is not destructive ('row is deactivated (not deleted)') and that historical events persist. Annotations indicate idempotentHint=true and destructiveHint=false, which align with the description, but the description provides valuable detail about the 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?

Two sentences, no wasted words. The description is front-loaded with the main action ('Cancel a subscription by id') and then provides essential behavioral details. Every sentence earns its place.

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

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

Given the tool's simplicity (1 param, no output schema, no enums), the description is complete. It covers the action, ownership enforcement, and the soft-deletion behavior. No additional information is needed for correct usage.

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

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

Schema coverage is 100% (1 parameter with description). The description adds that the id is 'returned by subscribe', which provides source context beyond the schema's type/description. This is helpful but not critical given the schema already describes the parameter.

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 action ('Cancel a subscription by id') and the resource ('subscription identified by id'). It distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying ownership enforcement and soft-deactivation behavior.

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 the tool can be used: 'Ownership is enforced — you can only cancel your own subscriptions.' Also explains the effect (deactivation not deletion). However, it does not mention when not to use it or provide 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 indicate read-only and idempotent behavior. The description adds significant context: the dual-pipeline logic (SEC vs. grounded), the verdict types, citation format, and reasoning. No contradiction with annotations.

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

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

The description is detailed and front-loaded with examples, but it is slightly verbose. Every sentence adds value, though some rephrasing could improve conciseness.

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

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

Despite no output schema, the description fully explains return values (verdict, actual value, citation, reasoning) and the two processing paths. Complete for a claim verification tool with good annotations.

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

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

Schema coverage is 100% with clear descriptions. The description adds practical guidance for tolerance_pct (e.g., use 1-2 for hallucination detection) and explains the default behavior, going 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 uses strong verbs like 'verify the claim', 'fact check', and specific examples (e.g., 'Apple's FY2024 revenue'), clearly distinguishing the tool from siblings by detailing the specialized financial claim path via SEC EDGAR and the fallback for other claims.

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 describes the two distinct pipelines. Lacks explicit when-not-to-use, but the sibling context provides differentiation.

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