Epa Envirofacts

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about probing multiple LLMs, per-model results, and cost implications for Anthropic. No contradiction.

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

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

The description is well-structured with clear sentences, but slightly longer than necessary. It front-loads the purpose and provides key details efficiently.

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

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

Given 4 parameters and no output schema, the description covers input details and clearly states the return format (per-model {score, confidence, signals, raw_response} + combined view). It is sufficiently complete for an agent to 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 coverage is 100%, so baseline is 3. The description adds meaning by explaining the `context` parameter's disambiguation role and noting that `_apiKey` is passed through to Anthropic with a billing note.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100). It distinguishes from siblings by focusing on AI visibility checks, not specific data queries or research tasks.

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

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

The description explicitly lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default model and optional Anthropic probe with BYO key, but doesn't explicitly state when not to use it.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so safety is clear. The description adds crucial behavioral context: it returns 'structured answer with stable pipeworx:// citation URIs' and mentions it 'fills arguments' automatically. It does not disclose rate limits or latency, but given the extensive annotation coverage, this is adequate.

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

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

The description is comprehensive but slightly verbose at ~180 words. It effectively front-loads the core message ('PREFER OVER WEB SEARCH'), then layers on details, examples, and usage guidance. Every sentence adds value, but could be tightened without loss.

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 (routing to thousands of tools), the description fully covers purpose, usage, behavioral traits, and parameter semantics. It references sibling tools for fallback, addresses multiple query types, and explains the return format. No output schema exists, but the description adequately describes the return structure (structured answer with citations).

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 explaining that the question is routed to the right tool and that aliases (q, text, input, etc.) are accepted. It also provides rich examples that clarify parameter usage. This pushes the score to 4.

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

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

The description clearly states the verb 'routes the question' and the resource '5,564 tools across 1462 verified sources', providing a massive, concrete scope. It distinguishes itself from siblings by explicitly naming alternatives (ask_pipeworx_grounded, deep_research) and advising when to use them. The examples cover diverse real-world queries, reinforcing the purpose.

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 'PREFER OVER WEB SEARCH', gives trigger phrases ('what is', 'look up', etc.), and provides step-up guidance: 'START HERE' vs. alternatives for hallucination-resistant answers (grounded) or broad multi-part questions (deep_research). It also notes when web search might still be relevant but positions this as default.

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 readOnly, openWorld, idempotent, not destructive. The description adds significant behavioral context: it is a beta with no active candidate currently, so it matches ask_pipeworx exactly; it is a full working router (not a fallback); and results are compared to the stable router for decision-making. This goes beyond what annotations provide.

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

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

The description is a single paragraph of five sentences, each adding value: identification, current state, usage guidance, and clarification of scope. It is efficient without being terse, though it could be slightly more streamlined. No extraneous information.

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

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

Given the tool is a beta variant of a complex router, the description covers its relationship to the stable version, current status, usage pattern, and behavioral guarantees. It references the same arguments and response shape as ask_pipeworx, which is sufficient context. No output schema is provided, but the description implies it matches the stable tool's response, which is likely documented elsewhere.

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

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

Schema coverage is 100% with clear descriptions for all 6 parameters, including aliases for 'question'. The description does not add new parameter information, but the schema already handles it well. 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 explicitly states it is a 'beta version of ask_pipeworx' with identical functionality and experimental routing improvements. It distinguishes from siblings (ask_pipeworx, ask_pipeworx_grounded) by being the experimental edge, and from the many other tools by being a universal router. The verb+resource is clear: 'ask' (query) a beta router.

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

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

The description advises: 'Use it exactly like ask_pipeworx when you want the newest routing.' It contrasts with the stable router and implies that for stable results one should use the non-beta version. It does not explicitly list cases where not to use it, but the context is clear enough for an agent to decide based on preference for experimental vs. stable.

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 significant value: hallucination resistance, reliance on only tool result, explicit refusal reasons, and extra LLM call cost. 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 front-loaded with the core benefit, then explains the mechanism, return format, usage guidelines, and cost trade-off. Every sentence provides necessary 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 no output schema, the description fully details the return structure (with success and failure fields) and refusal reasons. It also explains the mechanism and usage context, making it self-contained for an agent to understand and invoke correctly.

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

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

Schema description coverage is 100% with all parameters documented. The description does not add new meaning beyond the schema; it just restates that the question is in natural language. Baseline 3 is appropriate as schema already carries the burden.

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 'Hallucination-resistant answer mode for high-stakes reads' and explains its mechanism: picks the right tool, fetches data, and extracts answer using only the tool result. It distinguishes itself from the sibling ask_pipeworx by noting the same routing but with extraction and refusal handling.

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

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

Explicitly states when to use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when not to use ('prefer ask_pipeworx for casual lookups'), plus provides a cost trade-off. This gives clear decision 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 far exceeds annotations by detailing resolution behavior, classifier system, response shapes, safety mechanisms (low-confidence, closed markets), and resolution-rule 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 lengthy but well-structured with clear sections and front-loaded purpose. All sentences provide value. Minor deduction for verbosity, but justified by complexity.

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

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

Despite no output schema, the description covers inputs, process, outputs (response shapes, evidence keys), and edge cases (low confidence, closed markets, wide spreads). It is fully complete.

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

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

Schema coverage is 100%, but the description adds significant meaning beyond the schema, including explanations for each parameter (e.g., depth options, market input types, include_raw rationale) and examples.

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

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

The description clearly states the verb 'Research' and resource 'a Polymarket bet'. It provides specific examples of when to use the tool, such as 'should I bet on X', and distinguishes it from sibling tools by focusing on bet-specific data 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 explicitly lists use cases and provides fan-out examples for different bet types, giving clear context. However, it does not directly mention when not to use this tool or explicitly contrast with related siblings like polymarket_edges.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds valuable behavioral context: it discloses data sources, explains how fiscal year variations are handled, notes results are sorted by primary metric, and mentions citation URIs. No contradictions with annotations.

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

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

The description is a single paragraph that efficiently packs multiple pieces of information: triggers, use preference, entity types, data details, and output format. It is slightly dense but front-loaded with the most critical usage triggers.

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

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

Despite having no output schema, the description fully covers the tool's functionality: input constraints (2-5 entities), data content per type, sorting behavior, and return format (paired data + URIs). It also explains edge cases like off-calendar fiscal years, making it complete 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?

Input schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining what data is retrieved for 'company' vs 'drug' types and how results are sorted. This provides richer context for parameter selection.

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 comparisons of 2-5 companies or drugs, specifying the data sources for each type (SEC EDGAR/XBRL for companies, FAERS for drugs). It also lists natural language triggers like 'compare X and Y' and 'rank these companies,' making the purpose unmistakable.

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

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

The description explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool over alternatives like entity_profile. It does not list explicit when-not-to-use scenarios, but the strong preference is well communicated.

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

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

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint. The description adds rich behavioral context: return format (findings packet with verbatim evidence, confidence, source, citation, gaps[], contradictions[]), hop field, semantic excerpting of large records, performance expectations (15-90s), and the condition for citation_uri presence. No contradiction with annotations; the description complements and extends the structured data.

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 (~500 words) but front-loads critical information: account requirement, alternative tool, and core purpose. It is well-structured in logical paragraphs covering purpose, guidelines, behavior, and parameters. Some detail (e.g., specific URL, pricing mention) could be trimmed for conciseness, but the complexity of the tool justifies the length.

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

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

Given the tool's complexity (multi-step decomposition, multiple depths, structured data sources), the description is comprehensive. It covers behavior, constraints, alternatives, performance, return format, error handling (gaps), contradiction detection, citation resolvability, and semantic excerpting. With no output schema and rich annotations, the description fully compensates, leaving no critical gaps for an AI agent.

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

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

Schema coverage is 100% with good parameter descriptions. The description adds significant value beyond the schema: for the depth parameter, it details the exact behavior of each enum value (quick=3 facets, standard=3+gaps+contradictions, thorough=6+iterative hop+contradictions). For question, it provides usage examples and emphasizes multi-part suitability. The description meaningfully enriches parameter semantics.

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

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

The description clearly states the tool performs grounded multi-source research across Pipeworx's structured data sources, decomposing questions into facets and routing to thousands of tools in parallel. It distinguishes itself from ask_pipeworx (single lookup vs. multi-source research) and from open-web search, with a specific verb-resource pair: 'research across structured data sources'.

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

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

The description provides explicit when-to-use and when-not-to-use guidance: it recommends ask_pipeworx for single lookups and for breaking/current news topics, and it notes that deep_research returns mostly empty gaps for topics not in the structured catalog. It also explains account requirements and alternatives for different tiers, and describes when to use each depth level.

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, destructiveHint. The description adds that results include curated schemas ready to call, eliminating the need for a second lookup. This extends 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?

Four sentences front-loaded with purpose, then details, no wasted words. Every sentence contributes meaningful information.

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

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

For a search/discovery tool with no output schema, the description fully explains what it returns (top-N tools with schemas) and how to use it. It covers all necessary context given the tool's simplicity.

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. The description adds value by clarifying that multiple aliases (task, q, search, description) are accepted for query, and specifies default and max values for limit. This aids correct parameter usage.

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 'Find tools by describing the data or task', clearly defining the tool's purpose as a meta-tool for discovering other tools. It distinguishes itself from siblings (which are functional tools) by being a discovery interface.

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

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

The description advises 'Call this FIRST when you have many tools available and want to see the option set', providing clear context for when to use it. While it doesn't explicitly state when not to use it, the positive guidance is strong.

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 and idempotentHint=true, but the description adds rich behavioral details such as fanning across multiple sources, returning specific fields (e.g., recent_filings with URIs, fundamentals sorted), soft-fail for patents after May 2025, and news fallback (GDELT→GNews). 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 packs essential information in every sentence. It is front-loaded with examples and the core purpose. While verbose, it avoids redundancy and is well-structured. Could be slightly trimmed but is efficient given the complexity.

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

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

Despite no output schema, the description thoroughly lists what is returned (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI) and notes limitations (patents soft-failure). It also mentions prerequisites (use resolve_entity). This makes the tool's behavior predictable and complete.

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

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

Input schema covers both parameters with descriptions (100% coverage). The description adds semantics: value can be ticker or zero-padded CIK, and that names are not supported, directing to resolve_entity. This enhances understanding beyond the schema.

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

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

The description clearly states the tool provides a 'full cross-source profile of a US public company in ONE parallel call.' It uses example queries like 'Tell me about X' and specifies it covers SEC, XBRL, patents, news, and LEI. It distinguishes from sibling tools by saying 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' and directs to resolve_entity if only a name is given.

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

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

Explicit guidance is given: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also states when not to use: 'names not supported (use resolve_entity first if you only have a name).' This provides clear 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 declare read-only, idempotent, non-destructive. Description adds value by explaining deduplication across EPA programs and the 'Keyless' auth requirement. No contradictions; builds on annotations.

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

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

Two sentences with no fluff. Front-loaded with the main action and scope. Every sentence contributes meaningful 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, description adequately explains return format (each facility once with programs). Simple list tool with good annotations; missing pagination details but overall sufficient for selection and invocation.

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

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

Schema covers all parameters with descriptions (100% coverage). Description does not add new semantic meaning beyond restating 'state', 'city', and 'limit'. Baseline score of 3 is appropriate as schema adequately documents 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 lists EPA-regulated facilities by state (and optionally city). Distinguishes from siblings like facilities_by_zip and get_facility by the scope (state vs zip vs specific facility). The 'Keyless' note adds useful context.

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?

Implicitly indicates use for US state-level queries but does not explicitly guide when to prefer alternatives (e.g., for a ZIP code use facilities_by_zip, for a specific facility use get_facility). No exclusion criteria or when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by explicitly noting the deduplication ('Returns each facility once') and the keyless access, providing context 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 two sentences with no unnecessary words. The first sentence states the primary purpose, and the second adds key details (deduplication and keyless access). It is 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 the tool's simplicity (2 parameters, no output schema, rich annotations), the description covers all essential aspects: purpose, return format, deduplication, and authentication. It is fully adequate for an agent to understand and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add new parameter details beyond the schema (e.g., it mentions ZIP code but the schema already describes it). No additional parameter semantics are provided.

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

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

The description uses a specific verb ('List') and resource ('EPA-regulated facilities in a US ZIP code'), clearly distinguishing it from the sibling 'facilities_by_state' by scope. It also explains the deduplication behavior and mentions the keyless access.

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

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

The description states 'Keyless', indicating no API key is required, which is a strong usage hint. It implicitly differentiates from sibling tools like 'facilities_by_state' by specifying ZIP code scope. However, it does not explicitly state when not to use or mention 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 destructiveHint=true, and description confirms deletion. Adds context about clearing sensitive data, which is valuable. 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 with no wasted words: first defines purpose, second provides usage context. Front-loaded and efficient.

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

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

For a simple delete operation with one required parameter and clear annotations, the description covers when and why to use it completely.

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 parameter description is minimal. Description does not add meaning beyond the schema beyond implying it's a key to delete.

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' with a specific verb and resource, and distinguishes from siblings by mentioning 'Pair with remember and recall.'

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also mentions alternatives (remember, recall).

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 as readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral details: fetching the page, extracting title/description/key links, and outputting markdown. 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, front-loaded with the main action, and every sentence adds value (purpose, process, use cases). No fluff.

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

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

Despite no output schema, the description clarifies the output format ('single text blob', 'standard llms.txt markdown') and the location where it's meant to be dropped, making it complete for the tool's simplicity. Use cases further ground its purpose.

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

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

Schema coverage is 100% with both parameters described. The description does not add new semantic information beyond the 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?

The description clearly specifies the verb 'generate' and the resource 'llms.txt file', and the phrase 'for any URL' distinguishes it from any sibling tools that might process URLs differently.

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

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

The description lists concrete use cases (client indexing, personal project, competitor audit) but does not explicitly state when not to use the tool or compare it to alternatives, which is acceptable given no similar sibling tools exist.

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

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

Description adds 'Keyless' access detail and specifies return structure (programs array listing every EPA program), beyond annotations which already indicate read-only and idempotent behavior.

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

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

Two sentences effectively convey purpose, input, and output. No extraneous information.

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

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

For a simple lookup tool with one parameter and no output schema, the description adequately covers input, output, and access requirements. No missing critical 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?

Schema coverage is 100% with a clear description for registry_id. Description merely restates the parameter's role without adding new semantics. Baseline score of 3.

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

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

Clear verb 'look up' with specific resource 'EPA-regulated facility' and identifier 'FRS Registry ID'. Distinguishes from siblings like facilities_by_state by specifying the lookup key and mentioning returned programs array.

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?

Implies usage when you have an FRS Registry ID. Does not explicitly mention alternatives, but the context of sibling tools makes it clear.

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

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

Annotations already declare readOnlyHint, destructiveHint false, and idempotentHint true, so the description doesn't need to repeat safety traits. It adds value by specifying return fields and the default 'active' filter, but does not address openWorldHint implications.

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

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

The description is two sentences, front-loading the purpose, and contains zero unnecessary words or 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?

No output schema, so the description compensates by listing six returned fields. It also provides usage context and parameter hint. For a simple list tool, this 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 a clear parameter description for include_inactive. The description adds no extra meaning beyond the schema, earning a baseline 3.

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

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

The description clearly states the tool lists the caller's active subscriptions and enumerates the returned fields (id, type, params, etc.). It distinguishes this from siblings like subscribe/unsubscribe by focusing on listing.

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

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

The description provides explicit use cases: 'review what you're monitoring before adding more or to find an id to cancel.' It lacks explicit when-not-to-use instructions, but the guidance is sufficient.

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

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

Annotations provide no safety signals (all false), so the description carries the full burden of behavioral disclosure. It delivers: filing without an account returns a claim_token for later status checks, rate-limited to 5 per identifier per day, free and doesn't count against quota, and the team reads digests daily. It also warns not to paste end-user prompts. This is rich, non-obvious behavioral context far beyond what annotations convey.

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 longer than average, but every sentence carries meaningful information: purpose, use cases, scope exclusion, token workflow, rate limits, quota impact. It is front-loaded with the core purpose and uses structured sentence groups. It could be slightly tighter (e.g., 'The team reads digests daily' is motivational, not strictly necessary), but it remains well-organized and readable.

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

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

Despite lacking an output schema, the description fully compensates by explaining the return value (claim_token) and its subsequent usage. It covers the tool's full behavioral contract: what to report, what not to report, how to claim feedback, rate limits, and quota. With only 4 optional params and a clear scope, this description is complete for an agent to correctly select and invoke the tool.

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

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

Input schema covers 100% of parameters with descriptions, so baseline is 3. The description adds important semantic value for claim_token: "pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed," which the schema only vaguely describes as 'Read the reply to a report you filed earlier.' It also adds message length guidance (1-2 sentences typical) not in the schema. This elevates it above baseline.

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

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

The description opens with a clear verb+resource: "Tell the Pipeworx team something is broken, missing, or needs to exist." It explicitly lists the feedback categories (bug, feature/data_gap, praise), which differentiates it from all sibling tools that are research/data retrieval tools. This is a specific, unambiguous statement of what the tool does.

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

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

The description provides explicit when-to-use guidance: "Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise)." It also gives a clear exclusion: "if the tool came from a different MCP server... file it with that server instead," plus a disambiguation fallback: "Not sure? Pipeworx tool names are the ones this connection lists." This is exemplary usage guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value by explaining the data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h), which go 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 very concise—two sentences plus a bullet list—with no wasted words. It front-loads the core purpose and then adds structured use cases.

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

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

Given the simple tool with one optional parameter, no output schema, and no nested objects, the description is complete. It explains what is returned, the windows, and caching, covering all necessary context.

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

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

Schema coverage is 100% and the description adds context for the single parameter (window) by explaining the trade-off between shorter and longer windows, enhancing the enum values.

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

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

The description clearly states it returns top tools, packs, and total call volume over recent windows. It also provides specific use cases that distinguish it from siblings, like discovering hot data sources and confirming canonical tools.

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

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

The description lists three explicit use cases with bullet points, giving clear context on when to use this tool. However, it does not explicitly mention when not to use it or suggest 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 declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds substantial behavioral context: it scans markets, checks monotonicity, computes partition sums, filters placeholders, and includes a fill check against live CLOB depth. This goes well beyond the annotations, providing a clear picture of the tool's actions and outputs.

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

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

The description is long but well-structured, starting with the core purpose then explaining modes. Each sentence adds relevant detail. While it is verbose, the technical depth is necessary for a tool like this, and it avoids redundancy. Front-loading the brief purpose helps agents quickly grasp 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?

Given the tool's complexity (arbitrage detection with multiple checks), the description covers all essential aspects: input modes, output structure (opportunities array, partition_check fields), edge cases (placeholder filtering, similarity threshold), and the fill check for realizable edge. Despite no output schema, the description clearly documents the response. It is completely adequate for agent use.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaning by explaining what each mode does (event for specific market, topic for cross-event scanning), and notes that event accepts slugs or full URLs. This adds value beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes three modes (no args, event, topic) and explains each, making it unmistakable what the tool does and how it differs from siblings like polymarket_edges.

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

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

The description provides explicit guidance on when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. It contrasts cross-event vs single-event and mentions alternatives like polymarket_fill_risk for custom sizing. However, it does not explicitly state when not to use the tool.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds extensive behavioral context: three model families, edge calculation (edge_pp_net, Kelly fractions), caching (1h at KV level), diagnostics, and a 24h-move warning. 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 lengthy but well-structured with clear sections for model families, tradeable-edge knobs, and response format. Every sentence adds useful detail, though minor redundancy exists (e.g., repeated references to edge components). The front-loading is good: the first sentence captures the core purpose.

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

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

The tool is complex (9 parameters, multiple model families, no output schema) and the description covers all aspects: model families, edge computation, tradeable-edge knobs, response structure, and diagnostics. It provides enough context for an agent to understand when and how to invoke the tool effectively.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description adds extra context beyond schema (e.g., explanation of min_partition_leg_kelly, slippage assumptions, and tradeable-edge knobs). Given the high schema coverage, baseline is 3, and the added value pushes it to 4.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It is specific (verb 'scan', resource 'Polymarket markets'), and distinguishes from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on edge discovery without paging hundreds of markets.

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

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

The description includes explicit guidance: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It also describes tradeable-edge knobs and caching. However, it does not explicitly state when NOT to use the tool or compare with alternatives, though the sibling list implies differentiation.

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 and destructiveHint=false. The description goes beyond by detailing response structure (tracked, expired, snapshot_dates), sign conventions for decay_pp_per_day, trend types, and limitations (60-day TTL, daily closes). 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 front-loaded purpose, then response details and limitations. It is somewhat verbose but every sentence adds value; minor tightening could improve conciseness.

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

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

No output schema exists, so the description fully explains return values (tracked, expired, snapshot_dates) with subfields and edge cases (gaps, TTL). Covers all necessary context for a telemetry 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 already describes days and window. The description adds minor context (e.g., 'snapshot family' for window, default/clamp values) but 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 it provides edge persistence and decay telemetry, answering 'how long has this edge existed and is it shrinking?' It distinguishes itself from sibling tools like polymarket_edges by focusing on historical time-series analysis rather than current snapshots.

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

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

Implicit usage guidance is present: it contrasts fresh vs old edges and explains when to use (to assess edge durability). However, it does not explicitly list when not to use or compare to alternatives like polymarket_edges or polymarket_arbitrage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint; description adds further behavioral details such as walking the ladder, returning specific fields like top_of_book, slippage, verdict, and warnings about partial fills and directional risk.

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 headings and front-loaded purpose, but slightly verbose with minor repetition (concept of uncapturable overround mentioned twice). Still, each sentence adds value.

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

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

Given no output schema, the description thoroughly explains return fields (top_of_book, vwap, verdict, per-leg detail, thin_legs, forced_directional_risk) and edge cases, providing sufficient context for correct 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%; description adds context on mode-dependent semantics (e.g., size_usd meaning for single-market vs basket), defaults, and auto-detection of side in basket mode, going beyond schema.

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

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

The description clearly states the tool performs a realizable-vs-theoretical edge check against live CLOB order-book depth, distinguishes between single-market and basket modes, and explicitly contrasts with siblings polymarket_arbitrage and polymarket_edges.

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

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

Explicitly tells when to use: before acting on polymarket_arbitrage sell/buy-every-leg signals or polymarket_edges trades above ~$500, with reasons about capturability and avoiding unhedged positions.

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 annotations (readOnlyHint, idempotentHint), the description adds critical behavioral details: compatibility_warning conditions, temporal_alignment, and skipped_cross_type counters. It warns that 'pre-mapped ≠ tradeable', providing full transparency.

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

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

The description is long but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loads the purpose. It could be slightly more concise but every sentence adds value.

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

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

Despite no output schema, the description thoroughly explains the response structure (leg-by-leg prices, matched spread, compatibility fields). It covers complex edge cases like mismatched bet shapes and temporal alignment, making it complete 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%, so baseline is 3. The description adds meaningful context beyond schema by explaining how parameters interact (topic auto-fetches, explicit overrides) and listing valid topic values, justifying a 4.

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

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

The description clearly states 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It uses specific verbs and distinguishes this tool from siblings like polymarket_arbitrage 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?

The description explicitly explains two usage modes (topic shortcuts vs explicit tickers), when to use each, and warns that most pre-mapped topics return compatibility warnings, effectively guiding the agent on when the tool is useful.

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

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

Description adds behavioral context beyond annotations (idempotent, read-only): scoped to identifier, lists all keys. Annotations already indicate safe operation, so bar is lower; description provides useful extra detail.

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

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

Two sentences, front-loaded with the main action. Every sentence adds value without redundancy. 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?

For a simple retrieval tool with no output schema, the description covers scope, pairing with sibling tools, and the difference between retrieving vs listing. 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 covers 100% of the single parameter with a clear description. Description restates the same info (omit to list all keys) and adds little beyond the schema. Baseline 3 is appropriate.

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

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

Clearly states it retrieves a value saved via remember or lists all keys if omitted. Distinguishes from related tools (remember, forget) and specifies the resource (previously stored context) and action (retrieve/list).

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 usage context: look up stored context without re-deriving, omitting key to list all. Mentions scope (identifier-based) and pairs with remember/forget. Lacks explicit when-not-to-use, but overall clear.

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

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

Description claims mark_read flag modifies read status, contradicting the readOnlyHint=true annotation. According to rubric, this contradiction warrants a score of 1.

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 4 sentences, front-loaded with purpose, no redundant information. Each sentence provides specific value.

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

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

Despite no output schema, description covers return fields, all parameters, behavioral effects (mark_read), and alternatives (HTTP endpoint). Complete for a tool with 5 optional parameters.

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

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

Schema has 100% coverage with descriptions for all 5 parameters. Description adds value with examples (e.g., 'sec_8k' for type) and explains mark_read behavior, going beyond the schema.

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

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

Description clearly states it pulls fired events from subscription feed and returns recent alerts. It specifies what each alert carries (source, citation_uri, payload). This distinguishes it from sibling tools like 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?

Description explains when to use (polling works fine) and provides alternative via HTTP endpoint. It gives filtering guidance (type, since, mark_read) but doesn't explicitly state when not to use or contrast with other tools.

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

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

Annotations already indicate read-only, idempotent, non-destructive, and open-world behavior. The description adds valuable context: fan-out to multiple sources (SEC, GDELT/GNews, USPTO), fallback logic, API sunset soft-fail, and return format with citations. No contradiction with annotations.

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

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

The description is a single paragraph but front-loaded with example queries. Every sentence adds value, though the density could be slightly reduced. 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?

Despite no output schema, the description explains the return structure (changes[] grouped by source, total_changes count, citation URIs). It also notes the USPTO soft-fail limitation. For a tool with 3 required params, this is well-rounded.

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 each parameter described. The description adds examples for the 'since' parameter (ISO date and relative shorthand) and clarifies 'value' accepts ticker or CIK. This adds meaningful guidance 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 defines the tool as a change feed for companies, listing example queries like 'What's new with X' and explicitly contrasting with sibling tool entity_profile for static profiles.

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

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

The description provides explicit when-to-use scenarios ('latest on Y', 'updates') and a clear when-not-to-use directive ('Use entity_profile instead when you want the static profile'), with the sibling tool named.

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

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

Description aligns with annotations (idempotentHint=true, destructiveHint=false) and adds context: scope by identifier, 24-hour retention for anonymous, and that it's a persistent write. 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?

Five sentences, front-loaded with purpose, each sentence adds unique information (when, how, scope, pairing). No redundancy or 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 simple 2-param tool with no output schema, the description covers purpose, usage, scoping, persistence, and pairing thoroughly. Complexity is low, but description is fully complete.

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

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

Schema coverage is 100% with descriptions for key and value. Description reinforces meaning with example patterns like 'subject_property', adding value beyond the schema but not essential. Baseline 3 with slight bonus.

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

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

Description clearly states the verb 'Save' and the resource 'data', specifies reuse across sessions, and gives concrete examples (ticker, address, preference). It distinguishes from siblings like 'recall' and 'forget' by mentioning pairing.

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

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

Explicitly tells when to use: 'Use when you discover something worth carrying forward'. Describes pairing with 'recall' and 'forget' as alternatives, and notes persistence differences between authenticated and anonymous sessions.

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. The description goes beyond by disclosing graceful degradation (if GLEIF/OpenFIGI unavailable, EDGAR identifiers still return), cascading lookups, source labelling for every identifier, and explicit display of unresolved identifiers. This is rich behavioral context not in 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 an extremely long, dense single paragraph. While all information is relevant, it lacks structure (e.g., bullet points for supported types, output details) and is not concise. The example query list is somewhat repetitive and could be trimmed for readability.

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

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

With no output schema, the description fully explains return behavior: identifiers labelled by source, unresolved identifiers explicitly listed, and fallback behavior. It covers edge cases (ISIN-to-LEI mapping, non-US issuers) and internal cascading logic, making the tool's behavior predictable for the 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%, but the description adds significant meaning: it explains each enum value ('company' details cross-source identity spine, ISIN mapping; 'drug' returns RxCUI, ingredient, brand) and value format rules (ticker, CIK, name, etc.). This goes far 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 defines the tool's purpose: resolving user-spoken names to official identifiers. It provides example queries and explicitly distinguishes from siblings like entity_profile by focusing on identifier resolution. The verb 'resolve' and resource 'canonical/official identifiers' 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?

The description gives explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also mentions that the tool replaces 2-3 manual lookups. While it doesn't name a specific sibling tool as an alternative, the usage context is clear and actionable.

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

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

Description discloses probing each entity with ai_visibility_check, ranking by score, and returning ranked list with score, confidence, signal density. No contradiction with annotations (all safe, read-only, idempotent).

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

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

Two concise sentences with front-loaded purpose, followed by use case and output details. No unnecessary information.

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

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

Despite no output schema, description sufficiently outlines return format (ranked list with score, confidence, signal density). All required parameters are explained, and optional ones given context. 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 meaning beyond schema: explains entities first entry as subject, models default, _apiKey condition, context role.

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 'Compare AI visibility across multiple entities side-by-side', distinguishing it from single-entity tools like ai_visibility_check. Uses specific verbs and resources.

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 frames use case for competitive AI-marketing audits with example question. Does not explicitly state when not to use, but context is clear. Could mention alternatives like ai_visibility_check for single entities.

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

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description adds significant behavioral context: composite call fanning out to two services, returned fields (summary block, per-advisory detail, links, alternatives), and performance caveat (bundlephobia first measurement can take 5-30s). 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 dense yet well-structured. Front-loaded with composite nature and purpose. Every sentence earns its place, covering input, behavior, output, and edge cases 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?

No output schema exists, but description fully explains return structure: summary block with listed fields, per-advisory detail, links, and alternative versions. Also covers failure mode and timing nuance. Complete for a 2-param tool.

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

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

Schema coverage is 100% with both parameters described. Description adds that scoped packages ('@types/node') are accepted and that version defaults to latest when omitted, providing value beyond schema.

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

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

The description clearly defines the tool as a composite check for npm packages, combining deps.dev and bundlephobia. It specifies the exact verb ('scan_dependency'), resource ('npm package'), and scope ('in ONE call'). No sibling tools overlap in purpose.

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 ('is X safe / popular / small' or 'what does adding lodash cost me') and notes ecosystem restriction ('NPM ecosystem only'). Also mentions alternative approach for other ecosystems ('deals.dev:version directly'). Provides graceful degradation on partial failures.

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

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

Description goes beyond annotations (readOnlyHint, etc.) by detailing the embedding model (BGE-base-en), windowing (500-char overlapping), similarity metric (cosine), and truncation behavior (200K char cap, flagged). Also explains that every passage carries an offset for verification.

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

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

Description is a single well-structured paragraph: purpose first, then usage guidance, then technical details. Every sentence adds critical information without redundancy. Front-loaded with key point.

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

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

Despite no output schema, the description explains that results are passages with character offsets and similarity scores, and covers constraints (200K char limit, truncation warning). Sufficient for the tool's complexity and parameter count.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by providing natural-language query examples and clarifying the truncation behavior for the 'text' parameter. Exceeds baseline but not max due to schema already being sufficient.

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 'Semantic search INSIDE a fetched record' with examples like SEC 10-K and long tool results. It differentiates from siblings by mentioning 'Pairs with ask_pipeworx_grounded', but could be more explicit about when not to use this over siblings.

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

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

Explicitly states when to use: 'Use when the record is too big to cram into the prompt'. Mentions pairing with ask_pipeworx_grounded as an alternative workflow, and describes the benefit (saves context, returns relevant passages). 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?

The description adds behavioral details such as authentication requirements, persistence, and specific constraints (e.g., phone verification, 10/day SMS cap) beyond the annotations, which only hint at non-read, non-destructive, idempotent behavior.

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

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

The description is well-structured, front-loading the main action and then detailing types and delivery channels, though it is dense and somewhat lengthy.

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

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

Given the tool's complexity, the description covers all necessary context: subscription types, delivery channels, authentication requirements, and return value, making it fully complete for correct 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?

The description enriches parameter semantics with detailed examples and constraints (e.g., phone verification, daily cap) that go beyond the already comprehensive input 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 uses a specific verb ('Create') and resource ('proactive monitoring subscription'), clearly distinguishing it from sibling tools like list_subscriptions and unsubscribe.

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

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

The description explicitly states prerequisites (Pipeworx OAuth account) and supported subscription types, providing good context for when to use, though it does not explicitly discuss when not to use or compare with 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?

While annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds value by detailing the output structure (category-bucketed examples with exact tool+argument shape) and the effect of calling with vs. without arguments.

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 common user questions and is comprehensive, though slightly verbose. Every sentence adds value, but it could be slightly more concise without losing clarity.

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

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

Given the tool is an onboarding tool with a single optional parameter and no output schema, the description fully covers what it returns, how to use it, and its role relative to other tools. It is complete for an AI agent to understand and invoke correctly.

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

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

Schema coverage is 100% with one optional parameter. The description explains the parameter's purpose and lists possible values, making it clear beyond the schema's 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?

Description uses specific verbs ('returns category-bucketed example questions') and clearly identifies the tool as the onboarding entry point for new users, distinguishing it from sibling tools like ask_pipeworx or discover_tools.

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

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

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides guidance on when to omit or pass the 'topic' parameter, effectively telling the agent when and how to use it.

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

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

Discloses ownership enforcement and deactivation (not deletion), adding value beyond annotations. However, idempotency is annotated but not described.

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 efficient sentences with zero redundancy. Every sentence adds necessary context.

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?

Complete for a simple one-parameter tool. Covers purpose, constraints, and state change, referencing sibling 'recent_alerts' for historical data.

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

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

Schema covers parameter fully with description linking to subscribe. Description adds no new meaning, 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?

Description clearly states it cancels a subscription by id, distinguishing it from sibling 'subscribe'. The verb 'Cancel' and resource 'subscription' are specific, and the id parameter is mentioned.

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?

Tool usage is clear: cancel your own subscription by id. No explicit when-to-not-use or alternatives, but context is sufficient given sibling names.

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

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

Annotations already indicate a safe, read-only operation. The description adds crucial behavioral nuances: verdict types (confirmed, approximately_correct, refuted, inconclusive, unsupported, could_not_verify), the meaning of verification_error, and a caveat that could_not_verify must not be treated as evidence. It also discloses the internal routing and the use of a structured fast path, going beyond the 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 verbose but every sentence contributes: trigger phrases, usage context, routing, return components, and important error semantics. It is front-loaded with examples and use cases, and the 'IMPORTANT for callers' caveat is a valuable clarifier. Despite length, there is no redundancy.

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

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

With no output schema, the description thoroughly covers return values (verdict types, actual value with citation, reasoning) and error semantics. It explains what happens for different claim categories and addresses edge cases like unsupported vs could_not_verify. For a claim verification tool, this is a complete description.

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

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

The input schema already provides full descriptions for both parameters, including tolerance_pct's override behavior, valid range, and default. The tool description does not add new parameter-level semantics; it focuses on overall behavior and return values, so a baseline score of 3 is appropriate given 100% schema coverage.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It provides multiple trigger phrases ('fact check', 'verify the claim', etc.) and distinguishes the tool from general-purpose Q&A siblings by emphasizing that it checks factual claims, with a specific routing path for company-financial data.

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

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

Explicit guidance is given: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (SEC EDGAR path) and other factual claims (grounded pipeline), and explains that it replaces multiple sequential calls. This is strong contextual guidance without naming specific alternatives.

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