Rick And Morty

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

Annotations already indicate safe, idempotent, read-only behavior. The description adds value by disclosing that it calls LLMs, requires a BYO key for Anthropic, and returns per-model details (score, confidence, signals, raw_response) plus a combined view, which goes beyond the annotations.

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

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

The description is three sentences, front-loading the core operation and then adding key details about defaults and returns. Every sentence adds value, and there is 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?

Despite having no output schema, the description explains the return format (per-model object plus combined view). It covers all parameters, usage context, and cost implications, making it complete for an information-probing 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%, but the description adds meaningful context: default model is free Workers AI Llama-3.3-70b, and passing _apiKey enables Anthropic probes with direct billing. It clarifies that the API key is passed straight through to api.anthropic.com, which is useful for agents.

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 specifies the tool probes LLMs for knowledge about an entity and scores visibility (0-100) per model, distinguishing it from siblings like scan_competitor_ai_presence by focusing on visibility scoring and specific use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring).

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

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

The description clearly states when to use the tool (AI-marketing audits, pre-launch checks, competitive monitoring) and provides context for the optional API key. It does not explicitly contrast with sibling tools, but the use case is well-defined.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context about routing to 4,476 tools, filling arguments, and returning structured answers with citation URIs, which goes beyond the annotations.

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

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

The description is long but front-loaded with the preference statement. Some redundancy (e.g., 'START HERE for most questions') and excessive detail (e.g., listing sibling tools) could be trimmed for better 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?

Given the tool's complexity (4476 tools, 1127 sources) and no output schema, the description covers purpose, usage guidance, examples, and alternatives, making it fairly complete for an AI agent to decide when to use it.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by documenting aliases (q, text, etc.) and providing examples, which helps the agent understand the flexibility of the question 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?

The description clearly states the tool is for factual questions about current or historical data from authoritative sources, listing specific domains (SEC, FDA, etc.) and providing examples. It distinguishes itself from sibling tools like 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?

Explicitly says 'PREFER OVER WEB SEARCH' for factual queries, gives when-to-use criteria (e.g., 'what is', 'look up'), and provides detailed guidance on when to use alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for multi-part questions).

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. Description adds behavioral details: costs one extra LLM call, returns explicit refusal with reasons, and extraction method. Adds value beyond annotations.

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

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

Single paragraph rich with information: purpose, mechanism, return format, use cases, trade-off. Every sentence adds value and it is front-loaded with the core idea.

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

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

Despite no output schema, the description details the return format and possible refusals. Covers behavior, usage, and cost, making it self-sufficient for an agent to decide when and how to invoke.

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

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

Schema description coverage is 100% with all aliases described. The description does not add extra meaning beyond the schema, 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 it is a 'hallucination-resistant answer mode for high-stakes reads' and explains that it extracts answers using only tool results, distinguishing it from sibling 'ask_pipeworx'.

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

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

Explicitly tells when to use: 'whenever an answer will be quoted, cited, or acted on' and when not: 'prefer ask_pipeworx for casual lookups', with examples like financial or medical 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?

The description goes far beyond annotations by detailing resolution, classification, fan-out behavior, response shapes, resolver contract, parent event extraction, news fallback, safety mechanisms for low-confidence matches, closed markets, wide spreads, and resolution-rule risk. Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) are fully supported and not contradicted.

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 front-loaded with the main purpose. Each paragraph adds value (e.g., fan-out examples, resolver contract, safety notes). Despite verbosity, it is well-structured and not redundant. Could be slightly more compact but justified by 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 (3 parameters, no output schema), the description covers all essential aspects: resolution, classification, fan-out examples, response shapes, resolver contract, parent event handling, news fallback, safety, closed markets, spreads, and resolution-rule risk. It is complete enough for effective use.

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

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

All three parameters have schema descriptions (100% coverage). The description adds significant context: examples of market input, explanation of depth enum values and default, and when to use include_raw. It also explains fan-out behavior based on classification, which enhances understanding beyond the schema.

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

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

The description clearly states it researches Polymarket bets by pulling Pipeworx data in one call. It distinguishes from sibling tools like 'deep_research' or 'validate_claim' by focusing specifically on betting data, with specific input types (slug, URL, question) and output structure.

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

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

Explicit use cases are provided: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. The description does not explicitly state when not to use it or mention alternatives, but the context is clear that this is the primary tool for initial bet research.

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 rich behavioral context beyond annotations: it explains data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles off-calendar fiscal years, sorts results by primary metric, and returns citation URIs. Annotations already mark it as read-only and idempotent, consistent with the description.

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

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

The description is comprehensive but slightly verbose. It front-loads key phrases and efficiently packs information, though some redundancy exists (e.g., multiple examples of queries). Still, it remains clear and well-structured.

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

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

Given the tool's complexity (two entity types, data retrieval, sorting, citations), the description is thorough. It explains output format (paired data + URIs), handling of fiscal years, and even suggests replacing multiple lookups. No output schema exists, but the description covers return expectations adequately.

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

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

With 100% schema description coverage, the description still adds significant value: it details what data each type pulls (e.g., revenue, net income for companies; adverse events for drugs) and gives concrete examples for values (tickers/CIKs, drug names). This enriches the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It uses specific verbs like 'compare', 'rank', 'head to head' and explicitly distinguishes from sequential lookups, making its function 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 provides explicit guidance on when to use (comparing entities, queries like 'X vs Y') and when not to (sequential single-pack lookups). It states 'ALWAYS PREFER over sequential single-pack lookups' and mentions alternatives implicitly by indicating it replaces many lookups.

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

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

Annotations already declare readOnlyHint=true, etc. Description adds account requirements, expected timing (15-60s), behavior for out-of-catalog topics (returns gaps[]), and that it's not open-web. Adds value beyond annotations.

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

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

Well-structured with front-loaded critical info (account, alternative). Covers all aspects but slightly verbose; could be more concise without losing meaning.

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

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

Given two well-documented parameters, full annotations, and no output schema, the description provides complete context: expected return format (findings packet with evidence, gaps), timing, prerequisites, and alternatives.

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 meaning for depth enum (quick=3, standard=5, thorough=8 facets) and question field (broad/multi-part okay, decomposition expected).

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 grounded multi-source research across 1127 structured data sources in one call, distinguishing it from sibling tools like ask_pipeworx (single lookup, live news) and open-web search.

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

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

Provides explicit guidance: use ask_pipeworx for single lookups, for breaking news prefer ask_pipeworx, and if not signed in use ask_pipeworx. Also notes account requirement and paid plan for 'thorough' depth.

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

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

Description adds significant value beyond annotations: it states that the tool returns top-N most relevant tools with full input schemas and curated examples, each result ready to call directly with no second schema lookup needed. Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, and the description aligns perfectly with 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 informative and front-loaded with purpose, but slightly verbose with many example domains. Could be trimmed slightly without losing value, but still efficient.

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

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

Given no output schema, the description fully explains what the tool returns (top-N tools with names, descriptions, schemas, examples) and how it should be used as a first-step discovery tool. The parameter count and complexity are well-handled.

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

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

Schema coverage is 100% with all 6 parameters well-described. The description adds context beyond the schema: explains that query accepts natural language and lists aliases (task, q, description, search), and specifies default and maximum for limit. This enriches the agent's 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 that the tool finds tools by describing data or task, using a specific verb ('find') and resource ('tools'). It distinguishes itself from sibling tools that are domain-specific queries by positioning itself as a discovery launcher.

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 'Call this FIRST when you have many tools available and want to see the option set (not just one answer)'. Provides concrete use cases like browsing, searching, looking up, and discovering tools across a wide range of domains, giving clear when-to-use guidance.

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

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

Annotations indicate read-only, idempotent, and non-destructive behavior. The description adds substantial behavioral context: it makes one parallel call, fans out across multiple sources, returns specific fields (CIK, filings, fundamentals, patents, news, LEI), and notes the USPTO PatentsView API sunset soft-fail. 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 dense but front-loaded with examples. It conveys a lot of information without unnecessary repetition. Could be slightly more concise, but every sentence adds value. Structure is good.

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, parallel execution, and no output schema—the description comprehensively covers return fields, limitations (patents soft-fail, name unsupported), and example usage. Annotations provide safety profile. Complete for an agent to understand and invoke correctly.

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

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

The input schema covers 100% of parameters, so baseline is 3. The description adds value by explaining the 'type' parameter is currently limited to 'company' and providing examples for 'value' (ticker or zero-padded CIK) along with the constraint that names are not supported. This goes beyond schema 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: providing a full cross-source profile of a US public company. It lists example queries, details the data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), and contrasts with sibling tools like resolve_entity. This makes the purpose unambiguous and 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 instructs to prefer this tool over chaining multiple single-purpose lookups for holistic views. It also notes that names are not supported and advises to use resolve_entity first if only a name is available. This provides clear when-to-use and alternative guidance.

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

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

Annotations already indicate destructive and idempotent behavior. Description adds context on why deletion is needed (stale context, done task, clearing sensitive data), adding value beyond annotations.

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

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

Two concise sentences with front-loaded purpose and usage. Every sentence adds value without redundancy.

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

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

Given the simple one-parameter tool and no output schema, the description fully covers what the agent needs to select and invoke correctly. No gaps.

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

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

Schema covers parameter fully (100% coverage). Description only mentions 'by key', which aligns with schema. No additional semantics beyond what schema provides, so baseline 3 is appropriate.

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

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

Description clearly states the verb 'Delete' and resource 'previously stored memory by key'. It distinguishes from siblings 'remember' and 'recall' by mentioning pairing, indicating it's the deletion counterpart.

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 conditions for use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also advises pairing with 'remember' and 'recall'. No explicit when-not, but sufficient for clear guidance.

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

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

Annotations already provide safety hints (readOnly, idempotent, non-destructive). Description adds behavioral details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' No contradictions.

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

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

Two concise sentences plus a bullet list. Front-loaded with core purpose. Every sentence adds value, no fluff.

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

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

Despite no output schema, description covers purpose, usage, behavior, and parameters. Annotations supplement safety. Complete for a simple tool like this.

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. Description adds context: example URL format and default/max for max_links. This adds meaning beyond the schema.

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

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

Description clearly states verb 'Generate', resource 'llms.txt file', and context 'for any URL so AI crawlers can index the site cleanly'. It distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on file generation rather than scanning.

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

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

Provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' Does not mention when not to use or alternatives, but context is sufficient.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds value by specifying the return fields (name, status, etc.) but does not disclose additional behavioral aspects like rate limits or authentication.

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 sentence that front-loads the core action ('Get a single Rick and Morty character by numeric ID') and lists key return fields without extraneous 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 (single required parameter, no nested objects, output schema exists), the description adequately covers the what, how, and return details. No additional context is necessary for correct invocation.

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

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

Schema coverage is 100% with a detailed description for the 'id' parameter including range and an example. The tool description reinforces 'by numeric ID' but adds no new semantic information beyond what the schema already provides, meeting the baseline expectation.

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

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

The description clearly states the tool retrieves a single character by numeric ID and lists the returned fields. This directly informs the agent of the tool's purpose and distinguishes it from siblings like search_characters, get_episode, or get_location.

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

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

The description implies use when a specific character ID is known, and the sibling list includes search_characters for query-based lookups. However, there is no explicit guidance on when not to use this tool or 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing a strong safety profile. The description adds return field details, but this is likely redundant with the output schema. No new behavioral context beyond annotations is provided.

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

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

The description is a single sentence followed by a bullet list of return fields. It is front-loaded, concise, and every sentence serves a purpose with no 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?

For a simple tool with one parameter and an output schema, the description fully covers the tool's functionality and return information. It leaves no ambiguity about what the tool does or what it 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 coverage is 100% and includes a description and example for the 'id' parameter. The description reinforces that the ID is numeric but adds no additional semantics beyond what the schema 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 verb 'Get', the resource 'a single Rick and Morty episode', and the constraint 'by numeric ID'. It lists specific return fields (title, code, air date, characters) and distinguishes from sibling tools like search_episodes and get_character.

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

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

The description implies usage when you have a specific numeric ID for an episode. It doesn't explicitly mention when not to use or alternatives, but the context is clear enough for a simple get operation.

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, and openWorldHint=true, covering safety and behavior. The description adds the return fields (name, type, dimension, residents) but does not conflict with annotations. No additional behavioral details beyond annotations are needed.

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 sentences covering purpose, resource, and return fields. No unnecessary words or repetition. Every sentence adds value.

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

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

Given the simple retrieval nature, one required parameter, and the presence of an output schema, the description is complete. It informs the user of the key returned fields, which aligns with the output schema. No gaps remain.

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

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

Schema description coverage is 100%, with the id parameter fully described including example and description. The tool description adds no additional semantic meaning beyond stating 'by ID', so baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves a single Rick and Morty location by ID and lists the returned fields (name, type, dimension, residents). The verb 'Get' and resource 'location/planet/dimension' are specific and distinguish it from siblings like get_character or get_episode.

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

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

The description implies usage when you have a location ID, but does not explicitly state when to use or not use this tool versus alternatives. However, for a simple get-by-ID tool, the context is clear enough. No alternatives mentioned, but sibling names like search_characters indicate search alternatives.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds value by listing return fields (id, type, params, timestamps, fire_count) and mentioning the optional parameter behavior, which is consistent and informative.

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

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

Two concise sentences: first states purpose and return fields, second gives usage context. No redundant words, front-loaded with key information.

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

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

For a simple tool with one optional parameter and no output schema, the description covers purpose, return structure, and usage context adequately. It could explicitly mention the parameter's effect, but it's implied.

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

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

Schema coverage is 100%, so the schema fully describes the only parameter. The description does not add additional semantics beyond implicitly referencing 'active' and 'include_inactive'. Baseline score 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 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes itself from sibling tools like subscribe and unsubscribe by focusing on listing.

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

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

Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use it, though it doesn't elaborate on when not to use or highlight 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 are all false, providing no behavioral insight. The description compensates by disclosing rate limits (5 per identifier per day), that it's free, and that the team reads digests daily. While it doesn't mention anonymity or exact processing, it sufficiently covers key behaviors beyond annotations.

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

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

The description is concise yet dense, starting with a clear purpose, then usage guidelines, then specific instructions, and ending with constraints. Every sentence adds value, and it's well-structured and 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 adequately explains what happens after submission (team reads digests, affects roadmap) and sets expectations (rate-limited, free). It covers all necessary context for a simple feedback 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?

Input schema coverage is 100%, so baseline is 3. The description adds value by advising to describe issues in terms of Pipeworx tools/packs, but it largely restates what the schema already defines for parameters like 'type' and 'message'. No substantial new semantics are added.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific feedback types (bug, feature, data_gap, praise) and distinguishes itself from sibling tools by being the only feedback mechanism.

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 (e.g., 'when a tool returns wrong/stale data') and when not to use it ('don't paste the end-user's prompt'). It also provides context on rate limits and that it's free, offering comprehensive guidance.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and nondestructive behavior. The description adds valuable context: data source (CF analytics-engine), no PII, cached 5min-1h. No contradictions.

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

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

The description is concise, well-structured with bullet points for use cases and key details. Every sentence adds value, no waste.

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

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

Even without output schema, the description explains what is returned (top tools, packs, volume) and data freshness. It is complete for an agent to understand the tool's output.

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

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

Schema coverage is 100% with enum and description for 'window'. The description adds semantic insight: shorter windows surface hot trends, longer windows show steady-state demand, which goes beyond the schema.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It uses the verb 'Returns' and specifies the resource. It distinguishes itself from siblings like 'discover_tools' by focusing on trending usage.

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

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

The description lists three specific use cases: discovering hot data sources, confirming a popular tool, and seeing alignment with use case. It provides clear context for when to use, though it does not explicitly state when not to use or 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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) are complemented by detailed behavioral explanation: parameter requirements, monotonicity check, partition check, semantic anchor, partition filter, fill-check with live CLOB depth, and edge case handling. 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 informative but somewhat verbose with nested details. It is front-loaded with the requirement but could be more concise. Still, it is well-structured with clear sections.

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

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

Despite no output schema, the description explains the response structure (opportunities[], partition_check, fill_check details) and all relevant behaviors. Covers complex aspects like similarity thresholds and placeholder filtering comprehensively.

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

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

Schema coverage is 100% with descriptions. The description adds significant meaning: explains the two modes, gives example slugs and topics, and clarifies the role of each parameter 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 it finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with explicit recommendations, making it distinct 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?

Explicitly requires one of event or topic, and explains when to use each mode (event for specific market, topic for cross-event scanning). Also provides a fill-check nuance. However, it does not explicitly contrast with all sibling 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=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds extensive behavioral context: caching at KV level (1h), response structure with diagnostics, model details, and filter knobs. No contradictions.

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

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

Description is long but well-structured with segments, response fields, and filter explanations. Almost every sentence adds value, though could be slightly more concise. Front-loaded with purpose.

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

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

With 9 parameters and no output schema, the description compensates by explaining the response structure (by_segment, diagnostics), model details, filter knobs, and caching. Complete enough for an agent to understand behavior and output without schema.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond schema: e.g., explains tradeable-edge filters (min_liquidity, max_spread_pp) purpose, and min_partition_leg_kelly logic. Provides additional context for min_kelly 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 states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price' and is built for 'what should I bet on today'. It clearly differentiates from sibling tools like polymarket_arbitrage and provides specific verb-resource scope.

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

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

Provides clear context for when to use: discovering betting opportunities without paging hundreds of markets. However, it does not explicitly state when not to use or name alternatives among siblings.

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

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

The description adds substantial behavioral context beyond annotations: details response structure (tracked, expired, snapshot_dates), explains decay computation, mentions limits (60-day TTL, daily closes), and clarifies that trend and decay are computed on absolute value. No contradiction with annotations.

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

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

The description is detailed and well-structured, starting with the core question, then breaking down response fields. It is somewhat dense but avoids redundancy, earning each sentence. Could be slightly more concise.

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

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

With no output schema, the description thoroughly explains all return values and fields, covers limits and data sources, and provides enough context for correct tool invocation. Very complete for a tool with two optional parameters.

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

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

Schema coverage is 100% with good parameter descriptions. The description slightly supplements by restating defaults and clarifying the lookback purpose of 'days', but adds minimal new meaning beyond the schema.

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

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

The description clearly states the purpose as tracking edge persistence and decay over time, answering the specific question about edge duration. It distinguishes itself from sibling tools like polymarket_edges by focusing on temporal analysis rather than current snapshots.

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

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

The description implies when to use (when edge aging matters) but does not explicitly contrast with alternative tools like polymarket_edges. It provides context but lacks clear 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, idempotentHint, and destructiveHint, so the tool is safe. The description adds rich behavioral context: it walks the ladder, returns top_of_book, vwap_fill_price, slippage_pp, verdict, and for basket mode, per-leg fill details, thin_legs, and forced_directional_risk. No annotation contradiction.

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

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

The description is moderately long but well-structured with clear sections (SINGLE-MARKET, BASKET) and bold keywords. It is front-loaded with the core purpose. Every sentence adds value, though some elaboration could be trimmed without losing meaning.

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

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

Even without an output schema, the description fully explains return values for both modes (top_of_book, vwap, slippage, verdict, etc.). It covers when to use, risks, and parameter constraints. The tool's complexity is well-addressed, making it complete for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant meaning: it explains that market and event are required but mutually exclusive, details the side parameter's default logic, clarifies size_usd's interpretation in both modes, and mentions clamping range (10–1,000,000). This goes 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 states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes two modes (single-market and basket) and explicitly differentiates from sibling tools polymarket_arbitrage and polymarket_edges by prescribing when to use this tool before acting on their signals.

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

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

The description provides explicit guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains when it is not appropriate (e.g., theoretical overround on thin books not capturable) and warns about partial fills converting arb into unhedged positions, the dominant loss 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 indicate read-only and idempotent behavior; the description adds deep behavioral detail: compatibility_warning conditions, temporal alignment, cross-type/subtype counters, and handling of non-equivalent bet shapes. 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 lengthy but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded purpose. Every sentence adds value, though some trimming could improve conciseness without losing critical 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 no output schema, the description fully explains the response structure (leg-by-leg prices, top spreads, safety fields) and edge cases (compatibility_warning, temporal misalignment). Covers all aspects of tool behavior for a complex multi-venue comparison.

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

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

Schema covers 100% of parameters with descriptions; the tool description adds significant value by explaining the interplay between parameters, the two modes, and the meaning of each parameter (e.g., topic shortcuts listed explicitly, override behavior for explicit parameters).

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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question, outlines two modes (topic shortcuts and explicit pairings), and distinguishes from sibling polymarket tools by focusing on spread rather than arbitrage or 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 describes two usage modes with examples and warns that most pre-mapped topics return compatibility warnings, guiding agents on when the tool is effective. Does not explicitly contrast with alternatives but provides sufficient context for decision-making.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the description does not need to repeat these. It adds useful behavioral details: the ability to list all keys by omitting the argument, scoping to user identifier, and pairing with remember/forget. No contradictions.

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

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

The description is three sentences long, each earning its place: defining the action, giving a use case, and noting scoping/relationships. It is front-loaded with the primary action and contains no redundant or vague phrasing.

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 a simple parameter (one optional), rich safety annotations, and no output schema required, the description sufficiently explains the tool's capability, scoping, and relationship to siblings. It does not detail return format, but for a recall tool, the implicit output (saved value or key list) is clear enough.

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

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

The single parameter 'key' is well-described in the input schema (100% coverage). The tool description reinforces the behavior of omitting the key to list all keys and provides concrete examples of what keys might contain ('user's target ticker, an address, prior research notes'), adding practical 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 the verb ('retrieve' or 'list') and the resource ('saved values' or 'keys'). It distinguishes itself from siblings by explicitly pairing with 'remember' (to save) and 'forget' (to delete), making its purpose precise and differentiated.

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

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

The description advises when to use ('to look up context the agent stored earlier') and implies alternative tools ('remember' to save, 'forget' to delete). It also notes scoping ('Scoped to your identifier'), but lacks explicit 'when not to use' guidance, which is minor given the context.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive. The description explains that mark_read:true flags events as read, affecting future calls. This adds behavioral context beyond annotations, without contradiction.

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

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

The description is concise with five sentences, front-loaded with the main purpose. Each sentence adds value without unnecessary repetition.

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 optional parameters and no output schema, the description covers return fields, filtering, state management, and alternative access. It lacks details on error handling or invalid inputs but is generally 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?

All parameters have schema descriptions (100% coverage). The description adds value by providing examples (e.g., 'sec_8k' for type) and explaining the effect of mark_read. However, the schema already covers parameter purposes sufficiently.

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 pulls fired events from the subscription feed, returning recent alerts with specific fields (source, citation_uri, payload). It also mentions filtering options, 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 context on when to use the tool (e.g., polls are fine) and mentions an alternative access method (GET registry.pipeworx.io/alerts.json). While it gives good guidance, it does not explicitly compare to sibling tools or state when not to use this tool.

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

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

Annotations already declare readOnlyHint and idempotentHint. The description adds transparency by detailing data sources (SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail), response structure (grouped changes, total count, citation URIs), and date format handling. No contradiction with annotations.

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

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

The description is relatively long but well-structured with examples, alternative tool mention, and clear explanation of data sources. It efficiently communicates necessary information, though could be slightly more concise.

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

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

Despite lacking an output schema, the description thoroughly explains the return structure (changes grouped by source, total_changes, citation URIs). It covers all aspects of tool behavior, making it complete for an agent to use effectively.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by providing examples of valid input formats (e.g., '2026-04-01', '30d') and clarifying that value can be a ticker or CIK. This enrichment justifies a score of 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 explicitly states it provides a change feed for a company, covering SEC filings, news, and patents. It uses specific verbs like 'fans out' and lists concrete data sources, making the purpose very clear.

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 example queries, specifies when to use the alternative tool entity_profile instead, and explains fallback behavior between GDELT and GNews. This provides clear guidance on when and how to use the tool.

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

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

Annotations provide basic safety info (idempotent, non-destructive). Description adds scope (by identifier), persistence (authenticated vs 24h), and key-value nature. No contradictions.

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

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

Three sentences front-loaded with purpose. Efficient but could trim redundant phrasing (e.g., 'across sessions' implied). Good structure.

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 2-param tool with no output schema, description is complete: covers storage, scoping, persistence, and sibling tools. No gaps.

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

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

Schema covers 100% of parameters. Description adds practical examples and clarifies value as any text, improving usability beyond schema.

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

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

Clearly states the tool saves data for later reuse, with specific examples (ticker, address, preference) and distinguishes from sibling tools 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?

Explicitly says when to use (discover something worth carrying forward) and pairs with recall/forget. Lacks explicit when-not-to-use but context is clear.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds internal cascading through multiple lookup endpoints and explains the return format for each type, providing valuable behavioral context beyond annotations.

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

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

The description is comprehensive but efficiently organized, front-loading the purpose and examples. A few more structural breaks could improve readability, but no wasted sentences.

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

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

Given no output schema, the description fully explains return values for both entity types, including citation URIs. It covers input formats and the internal cascading. Missing edge cases like unresolved entities, but otherwise complete.

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

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

Schema covers both parameters with descriptions, but the description adds significant value by providing examples and explaining the output format (e.g., ticker+CIK for company), helping the agent understand how to interpret results.

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

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

The description clearly states the tool resolves names to official identifiers with specific examples ('ticker', 'CIK', 'RxCUI'). It names supported types (company, drug) and distinguishes its role from manual lookups, though it doesn't explicitly differentiate from sibling entity 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 advises 'Use FIRST whenever you have a name but need an ID,' giving clear context for when to use it. However, it does not mention when not to use it or compare with alternatives like entity_profile or search_within.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by explaining the internal use of 'ai_visibility_check', the ranking process, and output fields (score, confidence, signal density). No contradiction with annotations.

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

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

The description is two concise sentences: first sentence states the core purpose and mechanism, second provides context and output details. No wasted words.

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

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

For a tool with 4 parameters, no output schema, and sibling tools, the description covers the return format (ranked list with score, confidence, signal density) and the high-level process. It does not cover error cases or edge behaviors, but is sufficiently complete 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?

With 100% schema coverage, baseline is 3. The description adds meaning by explaining that the first entity in 'entities' is the subject for narrative, and clarifies the relationship between 'models' and '_apiKey'. This 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?

The description uses specific verbs ('compare', 'probes', 'ranks', 'surfaces') to clearly state the tool's action on multiple entities. It differentiates from sibling 'ai_visibility_check' by emphasizing batch comparison with 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 provides a clear use case ('competitive AI-marketing audits') and an example question. It does not explicitly state when not to use or mention alternatives like 'compare_entities', but the context is sufficient 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia's first measurement may take 5-30s, and sources_failed lists timeouts. 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 lengthy but efficient, front-loading the purpose and key details. Every sentence adds value, though minor trimming could improve conciseness without losing clarity.

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

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

Given no output schema, the description provides a detailed breakdown of the return value (summary block fields, per-advisory detail, links, alternative versions). It covers ecosystem limitations, graceful degradation, and latency expectations. Complete for a composite lookup tool with strong 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 description coverage is 100%, so baseline is 3. The description doesn't add significant new meaning to parameters beyond what the schema provides, but it does confirm that scoped packages are accepted and version defaults to latest. No extra semantics added.

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

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

The description clearly states the tool's composite purpose: checking npm package viability across deps.dev and bundlephobia. It specifies the resources (license, advisories, bundle size, etc.) and distinguishes from sibling tools by limiting scope to NPM ecosystem and mentioning alternatives.

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 ('is X safe / popular / small' or 'what does adding lodash cost me') and when not (other ecosystems like PyPI, Maven, etc. fall under deps.dev:version directly). Provides clear context for agent decision-making.

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

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

Annotations already declare safe, read-only, idempotent behavior. The description adds concrete pagination details (up to 20 per page) and response fields, adding value beyond annotations.

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

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

Two efficient sentences front-load the action and resource, with no wasted words.

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

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

Given the output schema and no nested objects, the description fully covers search capability, filters, pagination, and response fields, making it complete.

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

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

Schema coverage is 100%, but the description adds practical context like partial name matching and example usage, going 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 it searches Rick and Morty characters by name with optional filters, distinguishing it from sibling tools like get_character (single character) and search_episodes (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 implies usage for filtered character searches but does not explicitly state when to use this tool over alternatives like get_character or search_episodes.

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, destructiveHint false. Description adds that it returns episode metadata (air date, character list, ID), which is extra. However, it does not mention pagination behavior despite the 'page' parameter, limiting full transparency.

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 compact sentences, front-loaded with action and scope. No wasted words; format is efficient and readable.

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

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

Given annotations cover safety, output schema exists, and schema thoroughly documents parameters, the description provides sufficient context. A minor gap is the lack of pagination explanation, but overall it's complete for a search tool.

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

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

Schema coverage is 100% with clear descriptions for all 3 parameters. Description mentions parameters 'name' and 'episode' but adds no additional meaning beyond the schema. Baseline score of 3 is appropriate.

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

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

Description clearly states the tool searches Rick and Morty episodes by name or episode code, with a specific style example ('S01E03'). It distinguishes from sibling tools like search_characters and get_episode by specifying the search criteria and return metadata.

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

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

Description indicates two use cases (search by episode name or code). It does not explicitly state when to use this tool versus alternatives like get_episode for exact IDs, but the context is clear enough for an agent to infer selection.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds technical details like embedding model (BGE-base-en), windowing (500-char overlapping), cap (200K chars with truncation and flagging), and output features (offsets for verification). 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?

Concise and well-structured: purpose first, then use case, technical details, and caveats. Every sentence adds value without redundancy.

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

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

Given no output schema, the description covers return values (passages, offsets, similarity scores), usage context, constraints (cap), and behavioral details. Completely addresses what an agent needs to know.

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

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

Schema covers all 3 parameters with clear descriptions (coverage 100%). The description adds limited per-parameter value beyond the schema (e.g., examples for query). Baseline 3 is appropriate as description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the tool performs semantic search inside a fetched record, using specific verbs like 'search inside' and 'pass the text'. It distinguishes from siblings by mentioning its pairing with ask_pipeworx_grounded for grounding over passages.

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

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

Explicitly advises use when the record is too large for the prompt, and cites the alternative 'ask_pipeworx_grounded' for grounding. Provides clear context without 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?

The description adds significant behavioral context beyond annotations: authentication requirements, delivery channel behaviors (webhook signing, SMS cap, auto-disable), and one-time return of webhook secret. Annotations indicate readOnlyHint=false and idempotentHint=true, and the description aligns with these 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 front-loaded with the main purpose and well-organized, but the delivery section includes verbose technical details (e.g., HMAC signature explanation). While all content is relevant, it could be slightly more concise.

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

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

Given 3 parameters (2 required) and no output schema, the description covers all necessary aspects: authentication, subscription types, delivery options, limitations, and what is returned (subscription id, webhook secret). It provides thorough guidance for correct invocation.

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

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

Schema coverage is 100%, but the description enriches each parameter with concrete examples (e.g., items for sec_8k, topic for polymarket_edge) and detailed delivery object field explanations (phone verification, webhook signing). This adds substantial meaning 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 'Create a proactive monitoring subscription to a live-data event stream' and specifies the return value 'Returns the new subscription id.' It distinguishes from sibling tools like list_subscriptions 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?

The description provides explicit context: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions).' It details when to use each subscription type and delivery channels, including limitations like SMS caps and phone verification requirements. However, it does not explicitly state when not to use this tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds context about returning live catalog examples and that call with no arguments gives full spread. No contradiction, and adds value beyond annotations, though no mention of return format size or performance.

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?

Desdcription is thorough but slightly verbose, front-loading common queries. Every sentence is informative, but could be tightened without losing meaning.

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

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

For a simple tool with one optional parameter, description fully covers purpose, usage, return structure, and examples. No output schema needed as return type is clearly described.

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

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

Schema covers 100% of parameters. Description adds semantics by listing exact topic values (finance, pharma, etc.) and explaining behavior when omitted. Exceeds minimal coverage requirement.

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 returns category-bucketed example questions with tool+argument shapes, and positions it as the onboarding entry point. It uses specific verbs and distinguishes from siblings like ask_pipeworx and entity_profile.

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

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

Explicitly states when to use ('first when you don't know what Pipeworx can do', 'to learn how to call meta-tools') and provides optional topic focusing. Implicitly tells when not to use (once you know what to ask, use other tools). Strong guidance.

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

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

The description adds valuable behavioral details beyond annotations: ownership enforcement, deactivation (not deletion), and that historical events remain available via 'recent_alerts'. Annotations already indicate non-destructive and idempotent, so 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 that efficiently convey the main action and key behavioral constraints without redundancy.

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

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

Given the simple tool with one parameter and no output schema, the description fully covers purpose, ownership, and deactivation semantics.

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

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

Schema coverage is 100%, so the baseline applies. The description does not add parameter-specific information beyond what the schema provides, which is already clear.

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

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

The description uses the clear verb 'Cancel' with the resource 'subscription', and distinguishes it from siblings like 'subscribe' and 'list_subscriptions' by specifying ownership enforcement and 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?

The description states that ownership is enforced, implying the tool should only be used for one's own subscriptions, and mentions the effect (deactivation). However, it does not explicitly state when not to use it or suggest alternatives.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds significant context: it specifies the return verdict types (confirmed, approximately_correct, etc.), includes extracted structured form, actual value with citation, and percent delta. It also explains the underlying process (SEC EDGAR + XBRL) and that it replaces 4-6 sequential calls. No contradiction with annotations.

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

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

The description is a single dense paragraph that efficiently conveys purpose, usage, domain, sources, and output. While it packs a lot of information without verbosity, it could be more readable with bullet points or breaks. Every sentence serves a purpose.

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

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

Given no output schema, the description thoroughly details the return value: verdict types, structured form, actual value with citation, and percent delta. It covers the input (natural-language claim), process (SEC EDGAR + XBRL), output, and domain restrictions. For a specialized tool, this provides sufficient context for an agent to decide when and how to use it.

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

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

The single parameter 'claim' has a schema description with examples (e.g., 'Apple's FY2024 revenue was $400 billion'). Schema coverage is 100%. The description enhances this by specifying the types of claims supported (company-financial, public US companies) and the natural-language trigger phrases. Additional formatting details could improve it further.

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 validates natural-language claims against authoritative sources, specifically company-financial claims from SEC EDGAR + XBRL. It uses clear verbs like 'verify', 'confirm or refute', and differentiates from siblings by noting it replaces multiple sequential calls.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also restricts the domain to company-financial claims for public US companies. While it does not explicitly mention when not to use it or list sibling alternatives, the domain restriction provides clear guidance.

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