Exoplanet Archive

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

Annotations already declare read-only, idempotent, non-destructive. Description adds key behavioral details: cost implication for Anthropic probes, return format (per-model score, confidence, signals, raw_response), and that it's a probing action. No contradictions.

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

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

Two sentences packed with information, front-loaded with main action and default. Could use more structure (e.g., bullet returns), but no unnecessary words.

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

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

With 4 parameters and no output schema, the description covers purpose, parameters, behavior, return format, and use cases. No obvious gaps given the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining default model, API key pass-through meaning, and providing examples for entity and context parameters, going beyond schema descriptions.

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

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

Description clearly states the tool probes LLMs for brand/product visibility and scores them 0-100. It distinguishes from siblings like 'ask_pipeworx' by focusing on multi-model visibility scoring.

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 mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Also notes default model is free and Anthropic requires a key. Does not explicitly state when not to use or compare to alternatives.

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

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

Annotations indicate read-only, idempotent, and non-destructive behavior. The description adds value by detailing that the tool routes queries to one of 3,745 tools across 884 sources, fills arguments, and returns structured answers with stable citation URIs, going beyond annotation hints.

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

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

The description is front-loaded with the key instruction 'PREFER OVER WEB SEARCH' followed by domain examples. It is well-structured but contains some redundancy (e.g., listing multiple aliases not necessary in description). Still effective and concise enough.

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 input schema (one parameter) and rich annotations, the description fully covers the tool's purpose, usage, and behavior. It provides enough context for an AI agent to select and invoke it correctly, including examples.

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

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

Schema coverage is 100% with one required parameter and several aliases. The description merely restates that the parameter is a natural language question, providing no additional semantics beyond the schema's description. 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's purpose: to answer factual questions using authoritative structured data, preferring over web search. It specifies the verb 'ask' and resource 'Pipeworx', and distinguishes from generic search by emphasizing structured citations and domain coverage.

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 (for factual questions, even if web search could answer) and includes examples. However, it does not mention when to avoid use or compare to sibling tools like ask_pipeworx_grounded, missing some discrimination.

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

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

Annotations already mark the tool as read-only and open-world. The description adds valuable behavioral details: the extraction process, return format with evidence and refusal reasons, and the extra LLM call cost. 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 concise at around 5 sentences, with the key purpose front-loaded. Every sentence is informative and non-redundant, efficiently covering purpose, usage, behavior, and trade-offs.

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

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

Despite lacking an output schema, the description fully compensates by detailing the exact return structure, possible refusal reasons, and behavioral characteristics. Given the tool's complexity (many alias parameters, high-stakes context), the description is complete.

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

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

Schema coverage is 100% with detailed descriptions of alias parameters. The description does not add significant new semantics beyond stating 'Your question in natural language,' which is already implied. 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 starts with a clear, specific verb phrase 'Hallucination-resistant answer mode for high-stakes reads,' immediately stating the tool's unique purpose. It further distinguishes from sibling ask_pipeworx by noting additional cost and providing explicit use cases (quoted/cited answers).

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 an answer will be quoted, cited, or acted on' and lists high-stakes domains. It also advises preferring ask_pipeworx for casual lookups, offering clear when-to-use and when-not-to-use guidance.

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

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

The description extensively details behaviors beyond annotations, including classifier types, fan-out examples, response shapes, resolver contract with confidence levels, parent event extraction, news fallback handling, safety mechanisms for low-confidence matches, closed markets, wide spreads, and resolution-rule risk. Annotations already indicate read-only and idempotent, but the description adds rich 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?

The description is lengthy but well-structured. It front-loads the purpose and then provides detailed sections. While every sentence earns its place for a complex tool, it could be slightly more concise without losing essential 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 thoroughly explains return values (result fields, resolver contract, parent event, news fields) and covers edge cases (low confidence, closed markets, wide spreads, cancellation rules). It is complete for effective use.

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

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

Although schema description coverage is 100%, the description adds meaningful context: examples of market input formats (slug, URL, question text), depth parameter behavior via fan-out examples, and include_raw's impact on response size. This goes beyond the schema's basic definitions.

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

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

The description states a specific verb and resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly differentiates from sibling tools like polymarket_edges by focusing on comprehensive data retrieval and analysis.

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

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

The description provides explicit use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' However, it does not explicitly exclude scenarios where sibling tools would be preferred, though the context implies it.

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

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

The description details what data is pulled for each type (company: latest 10-K financials; drug: FAERS counts, FDA approvals, trials). It mentions sorting by primary metric and handling of off-calendar fiscal years. This adds rich behavioral context beyond the annotations (which already indicate read-only, idempotent, non-destructive).

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

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

The description is comprehensive and well-structured, starting with query examples and then detailing behavior. While slightly lengthy, the complexity of the two different entity types justifies the length. It is front-loaded and every sentence adds value.

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

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

The description covers all essential aspects: purpose, when to use, what data is returned (paired data + citation URIs), and scale saved. Given the tool has no output schema, the description provides enough context for an agent to use it correctly.

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

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

Although schema coverage is 100%, the description significantly adds meaning: for 'type' it explains what each enum value does, and for 'values' it provides formatting examples (tickers for company, names for drug). This goes well beyond the schema's brief descriptions.

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

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

The description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs. It provides example queries and explicitly distinguishes from sequential single-pack lookups, making its unique 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 explicitly says to use this tool when users ask comparison queries like 'X vs Y' or 'which is bigger'. It also advises to always prefer this tool over sequential single-pack lookups, providing clear guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, etc. The description adds no extra behavioral context (e.g., what the returned data represents, pagination, auth requirements).

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

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

While short, the description is under-specified. It front-loads a table name but fails to convey the tool's purpose, wasting the opportunity to be useful.

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 low complexity and a rich output schema, the description is completely inadequate. It does not state what the tool does, leaving the agent unable to select or invoke it correctly.

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

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

Schema description coverage is 0% and the description does not explain the 'limit' or 'query' parameters. It adds no meaning beyond the raw 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 'Composite planet parameter table `pscomppars`' is too vague. It names a table but lacks an action verb (e.g., 'query', 'list') to specify what the tool does, making it unclear how to use it.

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

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

No guidance on when to use this tool versus sibling tools like 'planets' or 'kepler_candidates'. The description provides no context for 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, openWorld, idempotent, non-destructive. The description adds significant behavioral context: returns structured findings packet with verbatim evidence, confidence, source, fetched_at, stable citation, gaps[] for unanswerable facets, and states 'never invented' and 'facts arrive pre-verified'. Also mentions expected 15-60s response time. No contradictions.

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

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

The description is detailed but well-structured, starting with the main purpose and value. Each sentence adds useful information. Slightly lengthy but justified by the tool's complexity. Could be trimmed slightly, 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 the tool's complexity (multi-source research with many sub-tools), the description covers purpose, usage, parameters, behavior, requirements, expected time, and return format (findings packet, evidence, gaps). No output schema but description sufficiently explains what the agent receives. Complete for an AI agent to select 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?

Schema has 100% coverage for both parameters. The description enriches by explaining depth options (quick=3, standard=5, thorough=8 paid) and clarifying that question can be broad/multi-part since decomposition is the point. This adds meaning beyond the schema enum and description.

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

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

Clearly states the tool does grounded multi-source research in one call by decomposing questions into sub-questions and routing to many tools/sources in parallel. Distinguishes itself from sibling ask_pipeworx for single lookups and provides example use cases like 'compare X and Y'.

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 to use for broad or multi-part questions, and to use the sibling ask_pipeworx for single lookups. Also mentions requirements (Pipeworx account, paid plan for 'thorough' depth), providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already provide readOnlyHint, idempotentHint, and non-destructive nature. The description adds important behavioral details: returns top-N tools with full schemas and examples, ready to call directly. No contradictions with annotations.

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

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

The description is relatively long but front-loaded with the main purpose. It efficiently uses sentences for usage guidance, output description, and a call to action. Could be slightly more concise, but every sentence earns its place.

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

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

Given no output schema, the description adequately explains what is returned (names, descriptions, schemas, examples) and that results are ready to call. It covers behavioral aspects well, though it could mention pagination or limit details more explicitly.

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). The description adds value by indicating aliases for query (task, q, description, search) and providing example query values, which helps agents understand parameter usage 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 'Find tools by describing the data or task.' It specifies the verb 'find' and the resource 'tools,' and distinguishes from sibling tools that search for data (e.g., ask_pipeworx) rather than tools themselves.

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

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

The description explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set.' It provides clear context but does not explicitly mention when not to use or 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?

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral details: fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), returns specific fields with URIs and sorting, mentions patent API sunset and soft-fail, and notes that names are not supported.

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 example queries. Every sentence provides useful information. While it could be slightly more structured, the length is justified given the complexity of the tool.

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

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

No output schema, so the description must explain return values. It does so comprehensively: lists all returned fields (cik, company_name, filings with URIs, fundamentals sorted, patents with sunset note, news fallback, LEI). It covers edge cases like patent API unavailability.

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

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

Schema coverage is 100%, so the description does not need to add much. It repeats the parameter meaning and adds context like 'zero-padded' and that names not supported, but this does not significantly enhance understanding beyond the schema.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company. It uses specific verbs like 'profile' and mentions inputs (ticker or CIK). It distinguishes from siblings by saying 'ALWAYS PREFER over chaining single-pack lookups' and references resolve_entity for names.

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 (holistic view) and when not to (names not supported). It recommends preferring this tool over chaining other lookups and directs users to resolve_entity if only a name is available.

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 destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data, which adds value beyond annotations but does not contradict them.

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: action, usage guidance, pairing hint. Every sentence earns its place. Front-loaded with the core action.

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

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

Given the tool's simplicity (one parameter, no output schema, clear annotations), the description adequately covers purpose, usage, and behavioral context. No gaps.

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

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

Schema coverage is 100% with parameter 'key' described. Description mentions 'by key' but adds no additional meaning beyond the schema description 'Memory key to delete'. 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 'Delete a previously stored memory by key' - a specific verb and resource. It distinguishes from siblings 'remember' and 'recall' by indicating deletion vs storage/retrieval.

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

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

Explicitly states when to use: when context is stale, task is done, or to clear sensitive data. Names siblings 'remember and recall' for pairing, providing clear guidance on 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. The description adds value by detailing the process (fetches, extracts, emits standard format) and noting the output is 'ready to drop at site-root/llms.txt'. It does not mention error handling or rate limits, but overall provides good 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 a single paragraph of four sentences, each with a specific role: state purpose, describe process, define output, list use cases. It is front-loaded with the core action and wastes no 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 (2 parameters, no output schema, rich annotations), the description covers the essential information: what it does, how it works, what it outputs, and when to use it. It could mention potential limitations (e.g., dynamic site issues) but is largely complete for a read-only, idempotent 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?

Both parameters (url, max_links) have 100% schema description coverage. The tool description does not add new meaning beyond what the schema already provides; it only implicitly references the url parameter. Thus, it meets the baseline but does not 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 uses specific verbs and resources: 'generate a production-ready llms.txt file for any URL'. It clearly states the tool's output and lists three explicit use cases. However, it does not differentiate from sibling tools like 'scan_competitor_ai_presence' or 'ai_visibility_check', which could lead to confusion about which tool to use for what.

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

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

The description provides clear use cases (getting a client's site indexed, drafting for own project, auditing competitor) but does not specify when NOT to use this tool or mention alternatives. It implies usage context but lacks explicit exclusions or comparisons to 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?

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. However, the description adds no behavioral context beyond 'candidate table', failing to mention traits like pagination, filtering, or data freshness. The description does not contradict annotations.

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

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

The description is a single sentence, which is concise. However, it is under-specified; it could be extended with essential usage details without becoming verbose. The structure is acceptable but not optimal.

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

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

Given the tool has an output schema (not shown) and 2 simple parameters, the description is incomplete. It fails to explain how to query, what the response contains, or any constraints. The examples in the input schema partially compensate, but the description itself lacks completeness.

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

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

Schema description coverage is 0%. The description provides no explanation of the two parameters ('limit' and 'query'). While the input schema includes examples, those are not in the description. The description adds zero meaning beyond the schema's property names.

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

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

The description states 'Kepler Object of Interest (KOI) candidate table', which clearly identifies the resource but lacks a verb like 'query' or 'list'. However, the context of being a table implies data retrieval, and it distinguishes from siblings like 'planets' by name. A specific verb would improve clarity.

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

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

No guidance is provided on when to use this tool versus alternatives like 'planets' or 'microlensing'. The description does not mention any context, prerequisites, or exclusions, leaving the agent to infer usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds default active-only behavior and optional include_inactive parameter. No contradiction.

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

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

Two sentences: first defines purpose and output, second gives usage guidance. No wasted words, perfectly front-loaded.

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

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

For a simple list tool with one optional param and rich annotations, description covers purpose, return fields, default behavior, and usage. Fully adequate.

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% parameter description. Description does not add extra param info beyond schema, so baseline 3 is appropriate.

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

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

Description uses specific verb 'List', resource 'subscriptions', and scope 'caller's active'. Lists return fields, distinguishing it from siblings like subscribe, 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?

Explicitly states use cases: reviewing monitoring before adding more subscriptions or finding an id to cancel. Clear context, no exclusions needed.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds no additional behavioral context (e.g., data source, query patterns).

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?

Extremely short at four words, but this is under-specification rather than conciseness. The structure is minimal and lacks informative content.

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

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

The description is nearly useless for understanding the tool's purpose, input, or output. It does not leverage the existing output schema or provide enough context for correct invocation.

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

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

Schema description coverage is 0%, so the description must explain parameter meaning. It fails to describe 'limit' or 'query', leaving their semantics completely unclear.

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 'Microlensing event table' is vague and lacks a verb. It hints at listing events but does not clearly state what the tool does, nor does it differentiate from siblings like 'kepler_candidates' or 'planets'.

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?

There is no guidance on when to use this tool versus alternatives. The description provides no context about typical use cases or exclusions.

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

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

Discloses rate limit (5 per identifier per day), free nature (no quota cost), and team processing (daily digests, road map influence). Annotations indicate non-idempotent write but description provides practical constraints.

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

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

Four sentences, front-loaded with core purpose, efficiently covers types, usage, rate limit, and output. No redundancy.

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

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

For a feedback tool with no output schema, the description fully covers what happens after submission (team reads digests, affects roadmap), rate limits, and cost. Complete for agent decision-making.

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

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

Schema already covers all parameters with descriptions. The description adds value by instructing to describe issues in terms of Pipeworx tools/packs, providing additional usage guidance beyond schema.

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

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

Description clearly states the tool is for sending feedback about bugs, missing features, data gaps, or praise. It uses specific verbs ('tell', 'describe') and distinguishes from sibling tools which are analytical or query tools.

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

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

Explicitly specifies when to use each feedback type: bug for wrong/stale data, feature/data_gap for missing tools, praise for positive notes. Also advises against pasting end-user prompts, guiding appropriate use.

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

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

Annotations already cover read-only, idempotent, and non-destructive nature. The description additionally reveals data source (CF analytics-engine), privacy (no PII), aggregation details (pack, tool, count), and caching behavior (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 (5-6 sentences) and well-structured: purpose first, then returns, then use cases, then source and caching. Every sentence adds value with no redundancy.

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

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

For a read-only trending tool with a single parameter and no output schema, the description covers all essential aspects: what it returns, why it's useful, data source, caching, and privacy. Sufficient for correct invocation.

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

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

The single parameter 'window' is fully described in the schema (enum and explanation). The description echoes this but adds no new semantic information beyond what the schema provides. With 100% schema coverage, 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's purpose: showing what AI agents are calling on Pipeworx, returning top tools, packs, and call volume. It distinguishes from siblings like ask_pipeworx and discover_tools by focusing on trending data rather than direct queries or tool listings.

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

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

Three specific use cases are provided (discovering hot sources, confirming canonical choice, seeing alignment). While no explicit when-not-to-use is given, the sibling context implies this is for aggregate awareness. Adding a direct exclusion would improve clarity.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds the table name and 'quick' but provides no additional behavioral details beyond what annotations offer.

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

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

The description is a single concise sentence that conveys the essential purpose without unnecessary words. Could include more detail but remains efficient.

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

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

Given the presence of an output schema and full input schema coverage, the description is minimally sufficient. However, it lacks context about the query language or how it relates to other planet-related tools.

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

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

Input schema has 100% description coverage for both parameters. The description does not add any further explanation of parameter semantics beyond what is in 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 performs a quick planet search against the 'ps' table. However, it does not differentiate from the sibling tool 'kepler_candidates' which likely performs a similar 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?

No guidance on when to use this tool versus alternatives like 'kepler_candidates'. The context signals list sibling tools but the description lacks usage 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?

Discloses logic (monotonicity, partition-sum checks, Jaccard similarity threshold, placeholder filters) and limitations (fill check with book depth). Aligns with annotations (readOnlyHint, idempotentHint, destructiveHint=false) 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?

Description is long but well-structured with clear sections (modes, semantic anchor, partition filter, fill check). Every sentence adds value, but 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 no output schema, description details response fields (opportunities[], partition_check{}, fill_check fields). Covers all aspects for a complex arbitrage detection tool, compensating for missing 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 has 100% coverage, and description adds meaning: explains event slugs, seed questions, and the distinction between single-event and cross-event modes. Provides concrete examples for both 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?

Clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. Differentiates two modes (event/topic) with concrete examples, distinguishing it from siblings 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, with no-args-fail warning. Recommends event for specific markets and topic for cross-event scanning. Includes fill check guidance (do not trade if realizable_edge_pp ≤ 0) and references polymarket_fill_risk for custom sizing.

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 extensive behavioral context: caching (1h KV), diagnostics on empty segments, Fed bets exclusion, edge calculation details, and tradeable-edge knobs. 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 very detailed and long, covering model families and edge math. While front-loaded with purpose, it could be more concise. Some details (e.g., exact parameters for Gates) may be excessive for quick agent understanding.

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

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

Despite no output schema, the description fully explains the response structure (by_segment, top-level fields, diagnostics) and all parameter behaviors. It covers caching, filters, and use cases comprehensively for a complex 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%, and the description adds rich meaning for each parameter (e.g., min_kelly explains it's half-Kelly fraction, max_spread_pp requires tight books). It goes well beyond the schema descriptions.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It is action-oriented ('what should I bet on today') and distinguishes itself from siblings like polymarket_arbitrage by focusing on disagreement-based opportunities.

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 it's for discovering opportunities without paging hundreds of markets, providing clear usage context. However, it does not explicitly state when not to use it or compare directly with sibling tools, though the purpose is distinct enough.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds important constraints: history depth bounded by 60-day TTL, snapshots written on cache-miss, decay computed from daily closes (not intraday). 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 front-loaded with the core question. It organizes information into a clear RESPONSE section and LIMITS. While wordy, every sentence adds necessary detail. Could be slightly tighter, but structure supports comprehensibility.

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 all response fields (tracked, expired, snapshot_dates) and their semantics. It covers limitations, behavior on missing data, and caching effects. For a telemetry tool with two parameters, this is comprehensive.

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

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

Schema coverage is 100%, so baseline is 3. The description repeats default values (days=14, window='1wk') and adds no significant new semantic detail beyond what the schema provides. No extra parameter guidance is given.

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

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

The description clearly states the tool's purpose: providing edge persistence and decay telemetry. It answers 'how long has this edge existed and is it shrinking?' and distinguishes from sibling tools like polymarket_edges by focusing on historical time-series analysis.

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

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

The description contrasts fresh wide edges with 3-week-old ones, implying that the tool is used to assess whether an edge is persistent or decaying. It does not explicitly state alternatives but provides clear context for when the tool adds value beyond current edge snapshots.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds valuable behavioral context beyond annotations, such as walking the ladder, returning verdict categories (clean|degraded|cannot_fill), and identifying thin legs and forced directional risk. No contradiction with annotations.

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

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

The description is well-structured with clear sections (SINGLE-MARKET, BASKET) and front-loaded with purpose. While moderately long, every sentence contributes essential information. Slight verbosity in explaining modes could be trimmed, but overall it is efficiently organized for an AI agent.

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 all return fields for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, shares_filled, verdict; theoretical_sum, realizable_sum, capture_ratio, thin_legs, forced_directional_risk). It addresses complexity with 4 parameters and no output schema, covering usage reasons, consequences of not using, and edge cases. Fully sufficient for agent invocation.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description significantly adds meaning by explaining the two operational modes (market vs. event), how side defaults auto for basket, and the interpretation of size_usd for buys vs. sells. It clarifies parameter interdependencies and provides context that makes the tool easier to use correctly.

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

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

The description clearly states the tool's purpose as a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, and explicitly differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by stating when to use this tool ('USE THIS before acting...'). The verb 'check' and resource 'edge' are specific and unambiguous.

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

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

Provides explicit when-to-use guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains why (theoretical overround not capturable on thin books, partial basket fills lead to directional risk) and details both modes and parameter usage. No exclusions needed.

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 and idempotent, and the description adds extensive behavioral detail: explains compatibility_warning conditions, skipped_cross counters, temporal alignment checks, and when spreads are mathematically meaningful, all without contradicting annotations.

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

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

The description is verbose but structured with clear sections (purpose, modes, response, safety fields), and every sentence adds necessary context; a slight reduction in verbosity could improve conciseness without losing substance.

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

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

Given the complexity of the tool and the lack of an output schema, the description thoroughly covers input modes, response fields, safety checks, and edge cases, though it omits explicit error handling or empty result formats.

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, and description adds value by explaining the dual-mode usage (topic vs explicit), the format constraints of tickers, and providing examples, going beyond the schema's basic type/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 explicitly states that this tool computes the cross-venue spread between Kalshi and Polymarket, clearly distinguishing it from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on cross-venue comparison.

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

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

Provides explicit guidance on when to use each mode (topic shortcuts vs explicit tickers), explains safety fields like compatibility_warning and temporal_alignment to prevent misuse, and warns that most pre-mapped topics return warnings rather than tradeable spreads.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds scoping details (by identifier) and the dual behavior of omitting key vs. providing it, which adds value beyond annotations.

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

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

Three sentences with no redundancy. Front-loaded with the primary action, followed by usage examples and pairing information. Efficient and clear.

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

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

Given the tool is simple (one optional param, no output schema), the description covers core behavior and scoping. However, it does not specify the return format when listing keys (e.g., array of strings). Slightly incomplete.

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

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

Schema coverage is 100% for the single parameter. Description adds crucial context: omitting key lists all keys, providing key retrieves value. This clarifies the optional nature and listing behavior.

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 saved value or lists all saved keys. It distinguishes from sibling tools like 'remember' and 'forget' by specifying the retrieval and listing actions.

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

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

Provides concrete use cases (ticker, address, research notes) and context for when to use (look up previously stored context). Mentions pairing with remember/forget but lacks explicit 'when not to use' or alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds behavioral traits: the effect of 'mark_read:true' (flags events read so next call shows newer ones) and polling friendliness. No contradictions.

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

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

Four sentences with zero wasted words. First sentence states purpose, second details output, third explains key parameters, fourth mentions polling and alternative access. Front-loaded and efficient.

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

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

Given no output schema, the description adequately explains return structure (source, citation_uri, raw payload). It covers key usage patterns (filtering, polling, mark_read) and provides alternative access. For a simple read-only tool with 5 optional params, the description is 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 all 5 parameters with descriptions (100% coverage). The description adds value by explaining how 'type' and 'since' filter events, what 'mark_read' does, and providing context about the returned payload fields (source, citation_uri, raw event payload) not in the schema.

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

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

The description clearly states 'Pull fired events from your subscription feed' with a specific verb and resource. It also details what each alert carries (source, citation_uri, raw payload), making the tool's function unambiguous and distinct from siblings like 'list_subscriptions' or 'subscribe'.

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

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

The description mentions polling suitability and an alternative endpoint (GET registry.pipeworx.io/alerts.json), but does not explicitly tell when to use this tool versus siblings like 'subscribe' or 'list_subscriptions'. Usage context is implied rather than stated.

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

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

Annotations already indicate read-only, idempotent, open world, non-destructive. The description adds rich behavioral details: fans out to multiple sources (SEC, GDELT/GNews, USPTO), describes fallback order (GDELT preferred, GNews on rate limit/5xx), notes USPTO soft-failure due to API sunset. Also outlines return structure (changes grouped by source, total_changes, citation URIs).

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

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

Well-structured with front-loaded examples, then functional description, sibling differentiation, parameter details, and return format. Every sentence adds value, though the phrase 'in ONE parallel call' is slightly redundant. Still, it is efficient for the amount of information conveyed.

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

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

No output schema exists, but the description explicitly states the return format: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' Combined with detailed parameter descriptions, agent has full understanding. Also covers fallback behaviors and tool alternatives, making it complete for its complexity.

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

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

Schema coverage is 100%. Description adds value beyond schema: provides relative shorthand examples ('7d', '30d', '3m', '1y'), suggests typical values, clarifies 'value' accepts ticker or zero-padded CIK. The description makes the parameters more usable.

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

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

The description opens with concrete example queries ('What's new with X') and defines the tool as a change feed for a company over a time window. It clearly distinguishes from the sibling tool 'entity_profile', which provides static data. The verb 'fetches recent changes' is specific and actionable.

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

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

Explicitly specifies when to use this tool (dynamic changes with a time window) and when to use 'entity_profile' instead (static profile regardless of window). Also provides parameter guidance, e.g., suggesting '30d' or '1m' for typical monitoring.

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 idempotentHint and destructiveHint; description adds storage behavior (persistent vs 24-hour, scoped by identifier), which adds value beyond annotations. Could be more explicit about overwriting behavior, but overall good.

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 single paragraph, front-loaded with main purpose, every sentence provides value (usage scenarios, persistence, pairing with related tools). 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?

Covers purpose, usage, persistence, scope, and related tools. No output schema, but not needed. Minor gap: does not mention return value or confirmation, but overall complete for an agent.

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

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

Schema coverage is 100% with good descriptions for key and value. The tool description does not add new parameter details beyond examples already in 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 saves data for reuse later, specifies verb (save) and resource (key-value memory), and distinguishes from siblings (recall, forget) by mentioning pairing with them.

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

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

The description explicitly says when to use (when discovering something worth carrying forward) and gives examples. It mentions alternatives (recall, forget) but does not explicitly state when not to use.

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

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

Annotations declare the tool read-only, idempotent, and non-destructive. The description adds context about internal cascading lookups and replacing 2-3 manual steps, which enriches understanding 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 well-structured with a question-answer lead, followed by usage guidance and detailed type-specific information. While somewhat lengthy, the front-loading of examples makes it practical. Minor redundancy could be trimmed.

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

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

The description is comprehensive for a lookup tool, covering all supported entity types, return values (including citation URIs), and use cases. Without an output schema, the description effectively explains what the tool returns.

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

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

With 100% schema coverage, the description adds value by providing concrete examples for the 'value' parameter (e.g., ticker, CIK, brand names) and clarifying accepted formats. This enhances understanding beyond the schema's minimal 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 resolves names to canonical identifiers, supported by specific examples like 'What's the ticker for…'. It distinguishes between entity types and explicitly contrasts with sibling tools that may require IDs as input.

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 advises 'Use FIRST whenever you have a name but need an ID.', providing clear guidance on when to use. However, it does not include explicit when-not-to-use scenarios or name alternatives, which would elevate the score.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. Description adds behavioral details: probes each entity with ai_visibility_check, ranks by score, returns score/confidence/signal density. No contradictions.

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

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

Two sentences: first state purpose and output, second adds usage context. Front-loaded, no wasted words, clear 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?

Annotations and schema cover safety and parameters. Description explains return format (ranked list) and internal steps (probes, ranks). No output schema needed as description adequately conveys result shape.

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

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

Schema coverage is 100%, and the description adds semantic context: entities array first entry is subject, rest are competitors; models default; context disambiguates. Goes beyond schema by explaining behavior.

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 compares AI visibility across multiple entities side-by-side, using a specific verb 'Compare' and resource 'AI visibility'. It distinguishes from sibling tools like ai_visibility_check (single entity) by emphasizing multi-entity comparison and ranking.

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

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

Provides a concrete use case: competitive AI-marketing audits with an example question. Implies when to use but does not explicitly state when not to use or name alternative sibling tools for similar tasks.

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

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

Discloses key behavioral traits: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed lists timeouts. Return structure explained. 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?

Dense but not overly long. Every sentence provides value. Front-loaded with core purpose. Could benefit from slight structuring but effective.

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

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

Despite no output schema, description thoroughly explains return structure (summary, per-advisory, links, alternatives) and failure behavior. Fully adequate for this tool's complexity.

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

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

Schema coverage is 100%, so baseline 3 applies. Description adds context about version defaulting and scoped packages, but does not significantly extend beyond schema for the two 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?

Clearly states it is a composite check for npm package evaluation, combining deps.dev and bundlephobia data. Differentiates from siblings by being NPM-specific and composite, with explicit use cases.

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

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

Explicitly mentions when to use: when agent asks about safety, popularity, size, or cost. Also states NPM-only and provides fallback for other ecosystems, giving clear guidance on 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?

Beyond the idempotent and read-only annotations, the description details embedding model (BGE-base-en), chunking (500-char overlapping windows), character offset provision for verification, and a hard cap of 200K chars with truncation and flagging. 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 concise but informative, with a clear main statement, practical use cases, and technical details all in four sentences. No fluff, every sentence adds value.

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

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

Despite lacking an output schema, the description clearly states what is returned: 'top-N passages with character offsets and similarity scores.' This is sufficient for an agent to understand the output format. The complexity of semantic search is well-covered with embedding and chunking details.

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 (text, query, limit) are described with example values and constraints in the description, adding significant context beyond the schema's description. The description explains the purpose of each parameter and how they interact (e.g., text is the document, query is natural language).

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 'Semantic search INSIDE a fetched record' with clear verb and resource. It distinguishes itself from sibling tool ask_pipeworx_grounded by explaining how they pair together. The examples (SEC 10-K, article) make the purpose concrete.

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

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

Explicit guidance: 'Use when the record is too big to cram into the prompt' and mentions when to use the sibling tool instead. This provides clear when-to-use and when-not-to-use context.

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

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

The description discloses behavioral traits beyond annotations: requires OAuth account, persistence limitations for anonymous/BYO, delivery channel constraints (SMS cap, webhook auto-disable), and that webhook secret is returned once. Annotations indicate idempotent=true, readOnly=false, destructive=false, which are consistent 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 a single dense paragraph that front-loads the main purpose. While it contains all necessary information, it could be more structured (e.g., bullet points for types or delivery channels). It is not overly long given the complexity, but slightly verbose.

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

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

Given the tool has no output schema and 3 parameters with nested objects, the description covers all aspects: input parameters with examples, return value (subscription ID), authentication requirements, delivery channel limitations, and error-prone scenarios (webhook auto-disable). It is comprehensive for the tool's complexity.

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

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

The description adds significant meaning beyond the schema's 100% coverage. It provides concrete examples for each subscription type (e.g., 'items:["5.02"] = officer change') and explains delivery channel details (SMS verification, webhook signing). This helps agents understand parameter usage beyond the schema's enum and type 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 creates a proactive monitoring subscription to a live-data event stream and returns the subscription ID. It uses specific verbs ('Create') and distinguishes from sibling tools like 'unsubscribe' or 'list_subscriptions' by focusing on creation.

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

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

The description provides context for when to use the tool, including prerequisites (Pipeworx OAuth account), supported types, and delivery options. However, it lacks explicit guidance on when not to use it or direct comparisons with 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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral details: returns example questions with tool calls, drawn from a live catalog, and the effect of passing a topic. 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 a single paragraph that front-loads common user queries, then explains functionality and usage. Every sentence is informative, though slightly verbose. It is well-structured and efficient.

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

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

Given the simple tool (1 optional parameter, no output schema), the description completely covers purpose, usage, parameter, and return value. It also adequately differentiates from 30+ sibling tools by positioning itself as the onboarding entry point.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by listing example topic values and explaining that omitting topic gives a full spread, while passing a topic focuses the results. This enriches the parameter 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 it returns category-bucketed example questions with tool+argument shape, and positions itself as the onboarding entry point. It distinguishes from siblings like discover_tools and ask_pipeworx by specifying its role as a first-use orientation tool.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do' and explains how to call with no arguments or with a topic. It does not explicitly say when not to use it, but the context is clear enough.

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

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

The description adds no behavioral information beyond what annotations already provide. Annotations declare readOnlyHint=true and destructiveHint=false, but the description does not elaborate on what operations are safe or any other behavioral traits.

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?

At three words, the description is under-specified. While brevity is valued, it fails to convey any useful information, making it ineffective rather than 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 the tool's complexity (ADQL TAP query with examples in schema), the description is completely inadequate. It does not explain what ADQL or TAP are, how to construct queries, or what output to expect, despite having an output schema.

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

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

Schema coverage is 100% with descriptions for both parameters (query and format). The description does not add meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description 'ADQL TAP query.' is essentially a tautology, restating the name without explaining what the tool does. It fails to specify the verb or resource, and does not distinguish itself from 30+ sibling tools.

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

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

No guidance is provided on when to use this tool versus alternatives like 'planets' or 'kepler_candidates'. The description gives no context for appropriate usage.

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

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

Discloses that the row is deactivated not deleted and historical events remain via recent_alerts. This adds value beyond annotations (destructiveHint=false, idempotentHint=true).

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

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

Two concise sentences, front-loaded with core action, 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 single-parameter tool with no output schema, the description covers effect, enforcement, and data retention linkage.

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

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

Schema coverage is 100%; description adds that the id is 'returned by subscribe', providing source context beyond schema.

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

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

The description states 'Cancel a subscription by id' with a clear verb and resource. It distinguishes from siblings like 'subscribe' and 'list_subscriptions'.

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

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

It specifies ownership enforcement, clarifying when to use (own subscriptions) and implying not for others. Lacks explicit exclusions 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 already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: it replaces 4-6 sequential calls, returns specific verdict types, uses authoritative sources (SEC EDGAR + XBRL), and provides citational evidence. No contradictions with annotations.

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

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

The description is moderately long but efficiently structured. It front-loads usage cues ('Is it true that...'), explains the domain, return format, and efficiency gains. Every sentence provides essential information without redundancy.

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

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

Despite absence of an output schema, the description fully explains the return values (verdict, structured form, actual value, delta, citation) and the underlying process. For a one-parameter tool with complex reasoning, this provides sufficient completeness for an agent to understand and invoke it correctly.

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

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

Schema coverage is 100% with a descriptive parameter. The tool description further enriches semantics with natural-language examples and explains the expected format, adding value beyond the schema alone.

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

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

The description clearly specifies the tool's verb-resource combination: 'validate claim' / 'fact check'. It distinguishes from siblings by being a specialized one-call verification tool that replaces multiple sequential calls, making its purpose unambiguous.

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

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

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and specifies supported claims (company-financial). While it doesn't list specific alternatives, the context from sibling tools and the description's scope (SEC EDGAR + XBRL) provides clear guidance on when 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.