Packagist

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that the default model is free and that using Anthropic requires a BYO key with direct payment, plus it outlines the return structure (per-model fields and combined view).

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

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

Three sentences: first states core function, second details model options and cost, third describes output and use cases. No wasted words, well front-loaded with the action.

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

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

All parameters are explained, the return structure is outlined despite no output schema, and use cases are given. Could be more complete by mentioning rate limits or pagination, but not essential for this tool.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds value by explaining the default model, the purpose of _apiKey (BYO key, direct payment), and how context helps disambiguate, which goes beyond the schema.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and provides a visibility score (0-100). It specifies the verb 'probe', the resource 'LLMs', and the output format, distinguishing it from siblings like 'entity_profile'.

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

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

Explicit use cases are given: AI-marketing audits, pre-launch brand checks, competitive monitoring. However, it does not explicitly state when not to use this tool or mention alternatives like 'scan_competitor_ai_presence', which is a close sibling.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that the tool routes to thousands of tools, fills arguments, and returns structured answers with stable citation URIs, providing transparency beyond annotations without contradiction.

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

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

The description is front-loaded with key guidance ('PREFER OVER WEB SEARCH') and lists use cases efficiently. While somewhat lengthy, every sentence adds value, and the structure is logical. Slight deduction for verbosity but still effective.

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

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

Given no output schema, the description mentions 'structured answer with stable pipeworx:// citation URIs', which is helpful but doesn't fully specify return format. However, with openWorldHint and the tool's broad scope, the completeness is adequate. Could benefit from mentioning error handling or fallback behavior.

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, documenting all six parameters as aliases for 'question'. The description adds minimal new meaning beyond 'natural language question', which is already implied. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states it answers factual questions using structured data from verified sources, distinguishing itself from web search. It lists specific data types (SEC filings, FDA, etc.) and provides example questions, making the tool's purpose explicit and actionable.

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

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

Explicitly advises 'PREFER OVER WEB SEARCH' for authoritative structured data. Lists when to use (e.g., 'current US unemployment rate') and provides examples. Effectively guides the agent on prioritization over sibling tools like search.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable behavioral context: costs one extra LLM call, returns refusal reasons on failure (not_in_source, no_tool_match, etc.), and extracts answers only from tool results. 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 well-structured: purpose, mechanism, return format, refusal reasons, usage guidance. Every sentence adds value without redundancy. Efficient and front-loaded.

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

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

Given the complexity (3,775 tools, 895 sources) and no output schema, the description covers purpose, mechanism, output format, failure modes, and when to use. It is 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?

Schema coverage is 100% with all parameters documented. The description does not add significant meaning beyond the schema, just mentions that the question is in natural language and that aliases are accepted. Baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: hallucination-resistant answer mode for high-stakes reads, extracting answers only from tool results. It distinguishes from sibling ask_pipeworx by specifying cost and use case, fulfilling the specific verb+resource+scope requirement.

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 (answers to be quoted, cited, or acted on, must not invent facts) and when to prefer the sibling ask_pipeworx (casual lookups), providing clear guidance and alternatives.

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

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

The description extensively adds behavioral details beyond the annotations: market resolution, fan-out logic, response shapes, resolver contract, safety mechanisms, and resolution-rule risks. 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 long but well-organized with clear sections and no wasted sentences. It could be slightly more concise, but the depth 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, no output schema, and three parameters, the description covers all necessary context: input handling, resolution, edge cases, and response structure. It is sufficiently 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 description coverage is 100%, so baseline is 3. The description adds some context (e.g., market input examples) but does not significantly enhance parameter understanding beyond the schema.

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

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

The description clearly states it researches Polymarket bets by pulling Pipeworx data, with flexible input formats. It distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on holistic research.

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

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

Explicit use cases are provided ('should I bet on X', 'what does the data say about Y'), and it describes when it short-circuits (closed markets, low confidence). However, it does not explicitly advise against using other tools for specific scenarios.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false. The description adds substantial behavioral context: for companies, it pulls latest 10-K from SEC EDGAR/XBRL handling off-calendar fiscal years; for drugs, it pulls FAERS counts, FDA approvals, trial counts. It mentions result sorting by primary metric and return of citation URIs.

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

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

The description is a single paragraph but packs essential information efficiently. It starts with example queries, then gives preference guidance, then details each type. While it could be broken into sections for readability, it is concise and front-loaded with the most important use case.

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 return format (paired data + citation URIs) and covers both entity types thoroughly. With only 2 parameters, this description fully meets the needs for an AI agent to select and invoke the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining parameter usage: for type, it describes what each enum value does; for values, it gives examples (tickers for company, drug names). It also clarifies the min/max constraints beyond the schema description.

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

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

The description clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It provides example queries ('Compare X and Y', 'X vs Y') and distinguishes between company and drug entity types with specific data sources. This makes the purpose unambiguous and differentiates it from sibling tools like entity_profile.

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

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

Explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' This gives clear when-to-use guidance. Also specifies the number of entities (2-5) and that it should be used in one parallel call, reducing ambiguity.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds rich behavioral context: the tool never invents (explicit gaps[]), returns verbatim evidence with confidence/source/timestamp/stable citation, and the internal parallelization mechanism. 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 lengthy but each sentence adds essential information: purpose, mechanism, usage, prerequisites, latency. It is front-loaded with the core value proposition. Minor redundancy in the first sentence ('Grounded multi-source research in ONE call') but overall justified.

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

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

No output schema exists, but the description thoroughly explains what the tool returns: structured findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, and gaps[]. It also covers timing and account requirements, leaving no critical ambiguity for agent invocation.

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

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

Schema coverage is 100%, so both parameters are well-documented structurally. The description adds value by explaining the depth enum meanings (quick=3, standard=5, thorough=8) and that 'thorough' requires a paid plan, plus elaborates on the 'question' parameter expecting broad/multi-part input.

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

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

The description clearly states that the tool performs 'grounded multi-source research in one call' by decomposing questions into sub-questions and routing them to 3,775 tools across 895 sources in parallel. It explicitly distinguishes itself from the sibling 'ask_pipeworx' for single lookups.

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

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

The description provides explicit guidance on when to use this tool ('broad or multi-part questions') and when to use alternatives ('use ask_pipeworx for single lookups'). It also mentions prerequisites (Pipeworx account, sign-in, paid plan for 'thorough' depth) and expected latency (15-60s).

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 value beyond annotations by stating that it returns top-N most relevant tools with names, descriptions, and full input schemas (with curated examples), ready to call directly. Annotations already mark readOnlyHint=true and idempotentHint=true, which are consistent with the description's behavioral claims.

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

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

The description is somewhat lengthy (4 sentences) but well-organized: it starts with the core purpose, then lists example use cases, then explains the return format and when to call it. Every sentence adds value, though it could be slightly more concise.

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

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

Given the tool's purpose (discovery), the description is complete: it explains what it returns (top-N tools with full schemas), when to use it, and includes context about return format. No output schema exists, but the description adequately covers what to expect.

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

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

Schema coverage is 100%, so the schema already documents all 6 parameters. The description adds minor value by explaining that 'query' accepts natural language and listing aliases, 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 finds tools by describing data or task, using specific verbs like 'browse', 'search', 'look up', and 'discover'. It distinguishes from sibling tools by being the discovery/search tool that returns tool definitions with full schemas.

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 when to use: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools and want to see the option set'. No alternatives are needed as this is the discovery 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 include readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral details: it fans out across multiple sources, returns specific fields (cik, company_name, recent_filings with URIs, fundamentals sorted by period_end DESC, patents via USPTO with sunset warning, news via GDELT→GNews fallback, LEI via GLEIF), and discloses that it 'soft-fails' if USPTO is inactive. 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 packed with valuable information and is front-loaded with example queries and a clear statement. However, it is somewhat dense and could be more structured with bullet points or sections for easier parsing. Still, every sentence adds value, and the length is justified by the tool's complexity.

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

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

Given the tool's complexity (2 required parameters, no output schema), the description fully covers what the tool returns: cik, company_name, recent_filings (with URIs), fundamentals (specific fields), patents, news, and LEI. It addresses limitations (USPTO sunset, name not accepted) and provides an alternative tool (resolve_entity). The description is adequate for an agent to use the tool correctly.

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

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

Schema coverage is 100% and both parameters (type, value) are described. The description adds meaning by explaining that type is currently limited to 'company', and value must be a ticker or zero-padded CIK (not a name). It reinforces the schema's restrictions and provides practical usage context.

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: 'full cross-source profile of a US public company in ONE parallel call.' It provides specific example queries ('Tell me about X', 'research Acme') and lists the data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF). It distinguishes from siblings by explicitly stating 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.'

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

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

The description gives explicit when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies when not to use: 'Names not supported (use resolve_entity first if you only have a name).' This provides clear context and alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds no additional behavioral context beyond 'Delete', so it is adequate but not enhanced.

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

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

Three sentences, front-loaded with purpose, then usage, then related tools. No wasted words.

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

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

Simple tool with one required param and no output schema. Annotations cover destructiveness. Description provides purpose, usage, and related tools – complete for agent context.

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

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

Schema coverage is 100% with a baseline of 3. The description adds no extra meaning beyond stating 'by key'. No further detail on format or constraints.

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

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

The description clearly states the tool deletes a stored memory by key, with a specific verb and resource. It distinguishes from sibling tools like 'remember' and 'recall'.

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

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

Explicitly states when to use: when context is stale, task is done, or to clear sensitive data. Also mentions pairing with remember and recall, providing clear guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral details: fetches page, extracts title/description/key links, outputs markdown blob. 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?

Four well-constructed sentences, front-loaded with the core action, followed by process and use cases. No redundant or extraneous content.

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

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

Covers purpose, process, output format, and use cases. No output schema exists, but description explains output adequately. Annotations handle safety. Complete for a simple 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 covers both parameters with full descriptions (100% coverage). Description reiterates defaults but adds no new semantic depth beyond schema.

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

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

Clearly states it generates a llms.txt file from a URL, using specific verbs like 'Generate', 'Fetches', 'extracts'. It does not explicitly distinguish from siblings like ai_visibility_check or scan_competitor_ai_presence, so misses the top score.

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

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

Provides clear use cases: getting a client's site indexed, drafting for own project, auditing competitor. Does not mention when not to use or alternatives, but context is helpful.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds that the metadata includes all versions, but does not disclose potential nuances like caching or rate limits.

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

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

The description is a single, concise sentence that front-loads the primary action. Every word earns its place with no redundancy.

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

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

Given the tool's low complexity (1 parameter, no enums), presence of output schema, and comprehensive annotations, the description adequately covers the needed context. No gaps remain.

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

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

Schema coverage is 100% for the single parameter 'name', with a clear description of its format. The description adds no additional semantic value beyond the schema.

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

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

The description clearly states 'Fetch full package metadata including all versions,' identifying the verb and resource. However, it does not differentiate itself from siblings like 'list_versions' or 'package_stats', which could overlap.

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

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

No explicit when-or-when-not guidance is provided. The usage is implied (when full metadata is needed), but no alternatives or exclusions are mentioned.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. Description adds behavioral detail: lists only active subscriptions by default, returns specific fields, and is scoped to caller. No contradictions.

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

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

Two sentences, front-loaded with purpose and return fields, followed by usage guidance. Every sentence adds value with no unnecessary words.

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

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

Given no output schema, description sufficiently covers return fields and usage context. Could mention limits or that list might be empty, but overall complete for a simple list tool with rich annotations.

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

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

Since schema coverage is 100% and the parameter 'include_inactive' is fully described in schema, description does not add extra information about parameters beyond what schema provides. Baseline score of 3 is appropriate.

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

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

Description clearly states 'List the caller's active subscriptions' with specific verb and resource, and lists return fields. Differentiates from sibling tools like subscribe and unsubscribe by focusing on listing existing 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?

Explicitly guides when to use: 'before adding more or to find an id to cancel'. This implies alternatives (subscribe, unsubscribe) but does not name them directly, though context is clear.

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

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

Annotations already declare readOnlyHint true, destructiveHint false, idempotentHint true; the description adds that it includes release dates, but does not disclose potential limitations like pagination or sorting. It adds some value beyond structured fields without contradicting them.

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

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

The description is a single sentence, front-loaded with the action and resource, containing no extraneous words.

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

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

For a simple tool with one parameter, existing annotations, and an output schema (not shown but present), the description adequately covers the tool's purpose and output (release dates) without gaps.

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

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

Schema coverage is 100% with a proper description for the single parameter 'name'. The description does not add additional meaning to the parameter beyond 'vendor/package', meeting the 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 clearly states the verb 'List' and the resource 'all versions of a package', and specifies 'with release dates', which distinguishes it from sibling tools like 'get_package' and 'list_subscriptions'.

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

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

No explicit guidance on when to use this tool versus alternatives; the description does not mention when not to use it or provide context for choosing it over siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, so the tool is clearly safe and non-destructive. The description adds what data is returned (download counts and dependents count) but does not disclose any behavioral aspects like authentication requirements or rate limits, which are not critical given 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 short sentence, front-loading the key outputs. It is concise but slightly incomplete as it lacks a leading verb. Every word earns its place.

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

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

For a simple tool with one parameter, high-quality annotations, and an output schema (not shown but exists), the description adequately conveys the return data. It is missing prerequisites or error scenarios but is sufficient for basic understanding.

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

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

Schema description coverage is 100% (the only parameter 'name' has description 'vendor/package'). The description adds no additional meaning 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 states the tool provides download counts (total, monthly, daily) and dependents count. It implicitly retrieves these stats for a package, distinguishing it from siblings like get_package (package details) or list_versions (version list). However, it lacks an explicit verb like 'Get' or 'Retrieve'.

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

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

No guidance on when to use this tool versus alternatives such as get_package or list_versions. The description only lists output fields without context for selection or exclusion.

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 that the tool sends feedback (a write operation) and includes rate limits (5 per identifier per day) and that it doesn't count against tool-call quota. Annotations provide no safety hints, so description carries the burden effectively.

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

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

Concise yet comprehensive, front-loading purpose, then usage guidelines, then restrictions. Every sentence adds unique value without redundancy.

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

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

Given the tool's simplicity (3 params, one enum, nested object), the description covers all necessary aspects: when to use, how to structure feedback, and behavioral constraints. No output schema needed for a feedback tool.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value by explaining the meaning of each type (bug, feature, etc.) and the context field, going beyond schema definitions.

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

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

Description clearly states the tool sends feedback to Pipeworx team about bugs, missing features, data gaps, or praise. It specifies the resource and action, and distinguishes from sibling tools (which are primarily search, research, or data 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 describes when to use: for bug reports, feature/data gap requests, or praise. Also provides clear exclusions (don't paste end-user prompts, describe in terms of Pipeworx tools) and mentions rate limits and quota-free nature.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds: data source (CF analytics-engine), no PII, aggregation format (pack, tool, count), and caching behavior (5min-1h). No contradiction.

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

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

Three sentences plus a bulleted list of use cases. Every sentence adds value; no filler. Core function front-loaded. Highly concise for the information conveyed.

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

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

For a tool with no output schema, the description adequately explains return structure (top tools, top packs, total call volume). Caching, data source, and privacy are covered. Slight gap: not specifying how results are ordered (by call volume? popularity?).

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

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

Schema coverage is 100% with description for the single parameter. Description adds practical guidance on window choice ('shorter windows surface what's hot right now; longer windows show steady-state demand'), going beyond 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?

Description starts with a clear, specific verb-resource combination: 'What other AI agents are calling on Pipeworx right now.' It lists exact outputs (top tools, top packs, total call volume) and window options, distinguishing it from sibling tools that focus on individual queries or entities.

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

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

Explicitly lists three use cases (discovering hot data, confirming canonical tools, checking alignment) that guide when to use. Does not explicitly mention when not to use or compare to siblings, but the use cases are strong enough context.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavioral context: the parameter requirement, the logic (monotonicity checks, partition-sum checks, Jaccard similarity, placeholder filtering), and the fill check that interacts with live CLOB depth. It also warns about conditions like 'realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it.' This goes well 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 relatively long but well-structured with clear sections and bullet points. It front-loads the requirement and uses bold for emphasis. While every sentence adds value, minor redundancy (e.g., 'SEMANTIC ANCHOR' and 'PARTITION FILTER' could be integrated more concisely). Overall, it's efficiently organized.

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

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

Given the tool's complexity (two modes, multiple checks, integration with live data), the description provides comprehensive coverage. It describes the response structure (opportunities[], partition_check, fill_check) and edge cases (skipped_low_similarity, placeholders). No output schema exists, so the description adequately fills the gap. Minor missing: explanation of 'monotonicity violation' for new users, but overall very 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 description coverage is 100%, so the parameter descriptions already exist. However, the description adds operational details: for 'event', it explains it accepts slugs and full URLs; for 'topic', it provides example seed questions. It also clarifies the mutual exclusivity and the consequence of calling without arguments. This adds meaningful context beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies two distinct modes (event and topic) and provides differentiation from sibling tools like polymarket_edges and polymarket_fill_risk, which are mentioned for specific contexts. The verb 'Find' plus the specific analysis methods make the purpose unambiguous.

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

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

The description explicitly states that exactly one of 'event' or 'topic' is required ('call with no args fails'). It provides clear guidance on when to use each mode: event mode for a specific market (recommended) and topic mode for cross-event scanning. It also explains the advantages of cross-event mode and directs users to polymarket_fill_risk for custom sizing. No alternative usage is left ambiguous.

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, destructiveHint=false. The description adds extensive behavioral context: detailed model families (crypto_price, news_momentum, etc.), response segmentation (by_segment), diagnostic info (_diagnostics), caching behavior (1h at KV level), and error cases (Fed bets excluded). 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 very long and dense, containing extensive technical detail about model families and response structure. While informative, it could be more concise. The purpose is front-loaded, but the wall of text may overwhelm agents. Every sentence earns its place, but could be structured better (e.g., bullet points for models).

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

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

Given the complexity (9 parameters, nested model logic, no output schema), the description is exceptionally complete. It explains the response structure (by_segment segmentation, diagnostics), how to interpret results (edge_pp_net, kelly_fraction, 24h move warning), and gives actionable guidance (Fed bets excluded, knob recommendations). Agents have enough context 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% with all 9 parameters described. The description adds significant meaning: explains the purpose of each knob (e.g., 'Tradeable-edge filter' for max_spread_pp, min_liquidity), gives usage context (e.g., slippage context with zero fees but bid/ask depth), and clarifies interaction between parameters (e.g., min_partition_leg_kelly vs min_kelly). This goes well 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 'scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the resource (Polymarket markets) and verb (scan/return opportunities), distinguishing it from sibling tools like polymarket_arbitrage and polymarket_kalshi_spread which likely focus on different types of 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 mentions it's 'Built for "what should I bet on today"' and implies agents discover opportunities without paging, but does not explicitly compare to alternatives or state when not to use this tool versus siblings. Usage guidelines are implied rather than explicit.

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

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds rich behavioral context: data source (daily snapshots), response structure (tracked, expired, snapshot_dates), and limits (60-day TTL, daily closes for decay). 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 fairly long but packed with necessary information. It front-loads the core purpose and progressively details the response and limits. Could be slightly more concise, but no redundant sentences.

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

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

Despite no output schema, the description thoroughly explains the response fields (tracked, expired, snapshot_dates) and their significance. It also covers limitations and data freshness, making it complete for a tool with two optional parameters.

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

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

Schema coverage is 100% with descriptions. The description adds extra context like default and max values for 'days', default for 'window', and details about response fields. This adds meaning beyond the schema entries.

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

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

The description clearly states the tool's purpose: edge persistence and decay telemetry from daily snapshots. It answers a specific question about edge age and trend, and distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis rather than raw 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?

The description explains when to use the tool: to compare fresh vs. aged edges. It implies not to use it for raw snapshot data by referencing polymarket_edges. However, it lacks explicit 'when-not-to-use' or alternative tool names, though context is clear.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses detailed behavioral traits: walks the order-book ladder, returns fill details and a verdict, and warns of forced directional risk in basket mode. No contradiction with annotations—consistent with read-only check.

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?

Efficiently structured with capital letters for key requirements, bullet-like format for modes, and minimal wasted words. Every sentence earns its place by providing essential information.

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

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

Given the tool's complexity, no output schema exists, but the description enumerates all return fields for both modes, explains the risk of partial fills, and includes safety constraints (clamp 10–1,000,000). It is comprehensive for agent decision-making.

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

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

Schema coverage is 100%. The description adds meaning: REQUIRES market or event, explains default sizes, and interprets size_usd differently per mode (max spend, target proceeds, settlement notional). This adds value beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, each with specific outputs, and differentiates from siblings like polymarket_arbitrage by positioning itself as a prerequisite check.

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 instructs: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains when not to skip it by describing the risks of theoretical overround on thin books and partial basket fills converting arbs into directional 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?

The description adds substantial behavioral context beyond the annotations. It explains compatibility_warning scenarios, temporal alignment, and why some spreads are not meaningful. Annotations already indicate readOnly, openWorld, and idempotent; the description enhances this with operational details. 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: start with definition, then modes, response fields, safety fields, and a caution. It front-loads the key purpose. While somewhat lengthy, every sentence adds value. Minor verbosity prevents a 5.

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

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

Given no output schema, the description fully covers the response structure (leg-by-leg prices, spreads, safety fields). It addresses common pitfalls like temporal misalignment and non-equivalent bet shapes. All optional parameters are handled. Sufficient for agent to use effectively.

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

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

Schema coverage is 100%, with each parameter described. The description adds value by explaining the two modes, providing examples, and clarifying that explicit parameters override topic mapping. This goes beyond just repeating schema descriptions.

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

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

The description clearly identifies the tool's purpose: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It uses a specific verb ('spread') and resource ('cross-venue'), and distinguishes itself from sibling tools like 'polymarket_arbitrage' by focusing on the same question across venues.

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

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

The description explains two modes (topic and explicit ticker) and provides guidance on when to use each. It also warns that pre-mapped topics often yield compatibility warnings, implying they are not always tradeable. However, it does not explicitly state when to avoid using the tool, but the context is clear enough.

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

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

Annotations already provide readOnly and idempotent hints. Description adds scoping to identifier and pairing context, without contradicting annotations.

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

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

Three concise sentences, front-loaded with main action, no unnecessary words.

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

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

Sufficient for a retrieval tool; explains behavior and scope. Could mention return format but not necessary.

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

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

Schema covers the parameter fully; description adds nuance about omitting key to list all keys, which is valuable beyond the schema.

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

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

The description clearly states the verb (retrieve/list) and resource (saved memories), and distinguishes it from sibling tools like remember and forget.

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

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

Explicitly says when to use (to look up previously stored context) and how it's scoped, and pairs with remember/forget for lifecycle management.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive hints. The description adds value by explaining mark_read behavior, polling support, and the alternative registry URL, without contradicting annotations.

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

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

The description is front-loaded with key purpose and returns, then details parameters and usage. It is efficient (two sentences) but 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 no output schema, the description adequately explains return fields (source, citation_uri, raw event payload). All parameters are covered, and for a read-only tool with good annotations, the description is complete.

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

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

Schema coverage is 100%, and the description adds meaning beyond parameter names: it explains 'type' with an example ('sec_8k'), 'since' as ISO timestamp, and 'mark_read' with its effect on subsequent calls.

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 ('Pull') and resource ('fired events from your subscription feed'), clearly distinguishing it from sibling tools like 'search' or 'recent_changes'. It details the returned fields and filtering options.

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

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

The description provides clear context on when to use (pulling alerts) and mentions filtering by type and since. It also notes polling suitability and an alternative URL. However, it lacks explicit when-not-to-use or comparison to siblings.

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

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

Annotations declare the tool as read-only, idempotent, and non-destructive. The description adds significant behavioral context: multi-source fan-out, GDELT→GNews fallback, USPTO soft-failure due to API sunset, and date parsing details. 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 dense but efficient, packing many examples and details into one paragraph. It is front-loaded with natural language queries. However, a more structured format (e.g., bullet points for sources) could improve readability.

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

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

Given the tool's complexity (multiple data sources, fallback logic, date formats, return structure), the description covers all essential aspects: return format (grouped changes, total count, citation URIs), source-specific behavior, and date syntax. No output schema exists, so the description adequately fills that gap.

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

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

Schema covers 100% of parameters. The description adds value by providing examples and usage guidance for the 'since' parameter (e.g., '30d' or '1m' for typical monitoring), which goes 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?

The description clearly identifies the tool as a change feed for a company in a specified time window, citing multiple sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from the sibling tool 'entity_profile' by explicitly stating when to use that instead.

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 example queries ('what's new with X', 'updates on Acme') and explicitly directs users to 'entity_profile' for static profiles, offering clear when-to-use and when-not-to-use guidance. It also explains fallback behavior for news.

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 this is a write operation (readOnlyHint=false) and idempotent (idempotentHint=true). The description adds useful context about key-value pairing, session scoping, and persistence duration, going beyond annotations without contradiction.

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

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

Four sentences, front-loaded with purpose, efficient and no wasted words. Each sentence adds value: purpose, when to use, storage details, pairing with siblings.

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 lack of output schema, the description fully explains the tool's behavior: write operation, persistence rules, and integration with recall/forget. Complete for a simple storage tool.

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

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

Schema description coverage is 100%, so parameters are already well-documented. The description provides naming conventions for keys ('subject_property', 'target_ticker') and notes value can be any text, but these add minimal extra semantic value beyond the schema.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It provides specific examples like resolved ticker, target address, user preference, and distinguishes from siblings by mentioning 'recall' and 'forget'.

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

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

Explicitly says when to use: 'when you discover something worth carrying forward'. Gives examples of use cases and references alternative tools: 'Pair with recall to retrieve later, forget to delete.' Also notes persistence behavior for authenticated vs 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 indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description goes beyond by explaining that each call 'cascades through several lookup endpoints internally' and that using resolve_entity 'replaces 2-3 manual lookups,' adding valuable behavioral context without contradicting annotations.

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

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

The description is moderately long but each sentence adds value. It is front-loaded with example queries and core purpose. The supported types section is detailed but essential. Slight verbosity in examples could be tightened, but overall efficient.

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

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

Given no output schema, the description thoroughly explains return values for both entity types, including citation URIs. It covers internal cascading behavior and the tool's role as a first step. With only 2 parameters and rich annotations, this description 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 coverage is 100% and the description enriches both parameters significantly. For 'type,' it lists the two enabled values with examples. For 'value,' it gives concrete examples for each entity type (ticker, CIK, name for company; brand or generic for drug) and explains the return fields (ticker+CIK+name+citation URI for company; RxCUI+ingredient+brand+citation for drug), adding meaning well beyond schema descriptions.

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

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

The description begins with clear example queries and states the core purpose: 'resolve a user-spoken NAME to the canonical/official identifier other tools require as input.' It distinguishes from sibling tools by emphasizing that other tools need these identifiers, positioning resolve_entity as the first step. Supported types (company, drug) are explicitly listed with variations, making the purpose unambiguous.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' providing strong when-to-use guidance. It details input variants for each entity type. However, it lacks explicit when-not-to-use instructions or alternative tool references, though the sibling list shows many tools that might use resolved IDs.

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, destructiveHint=false. The description adds that it probes each entity with ai_visibility_check, returns ranked list with score, confidence, signal density. No contradictions; adds valuable behavioral context.

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 that front-loads the main action. It is concise but could be slightly tighter; still effective and easy to parse.

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

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

Given no output schema, the description adequately explains return values (ranked list with score, confidence, signal density). It covers purpose, usage, parameters, and behavioral details, making it complete 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 descriptions for all 4 parameters. The description adds meaning by explaining that the first entity is treated as subject for narrative, and the tool compares entities. This enriches understanding beyond schema.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, using ai_visibility_check, and ranks results. It distinguishes from sibling tools like ai_visibility_check (single probe) and compare_entities (likely different scope).

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

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

The description explicitly frames usage for competitive AI-marketing audits with an example question. It implies when to use but does not explicitly state when not to use or name alternative tools. Clear context provided.

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

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

Annotations already indicate safe, read-only, idempotent behavior. The description adds critical behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and sources_failed will list timeouts. This is transparent and helpful.

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

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

The description is informative but slightly long. It front-loads the core purpose and provides structured details about return values and behavior. Could be 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 outlines return fields including summary, advisories, links, and alternative versions. It also covers error handling and latency. This is 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?

Schema coverage is 100%, so dependency is low. The description adds value by noting scoped packages (e.g., '@types/node') are accepted, which is useful context beyond the schema. It doesn't restate defaults but that's acceptable given high 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 composite nature of the tool, combining checks from deps.dev and bundlephobia to answer if an npm package should be added. It distinguishes itself from siblings by covering license, advisories, version history, bundle size, and ESM support in one call.

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

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

The description explicitly instructs when to use this tool: for questions like 'is X safe/popular/small' or 'what does adding lodash cost'. It also clarifies scope: NPM only, with alternatives for other ecosystems noted. This provides clear guidance versus sibling tools.

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

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

The description adds some context by specifying that it searches Packagist and supports filtering, but beyond the annotations (which already indicate read-only, idempotent, non-destructive behavior), it offers minimal additional insight. It does not elaborate on pagination or rate limits, but is not contradictory.

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

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

The description is concise with two sentences. The first sentence states the core purpose, and the second adds optional filters. No extraneous text, making it easy to parse quickly.

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 has 5 parameters, an output schema, and clear annotations. The description covers the core functionality and optional filters. It could mention that results are paginated (given page and per_page parameters) and that it uses the Packagist API, but overall it is complete enough for a search tool.

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

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

Since schema description coverage is 100% and each parameter is already described in the schema, the description's mention of 'optionally filter by package type and tags' merely reinforces existing information without adding new semantics. Baseline 3 is appropriate.

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

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

The description clearly states the tool searches Packagist by free text, with optional filtering by package type and tags. It specifies the resource (Packagist) and the action, distinguishing it from sibling tools like 'get_package' or 'search_within'.

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 does not provide explicit guidance on when to use this tool versus alternatives. While it mentions optional filters, it fails to mention when not to use it or suggest other tools for different use cases, leaving the agent without comparative context.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. Description adds rich behavioral details: truncation at 200K chars, BGE-base-en embeddings, cosine similarity over 500-char windows, return format with offsets and scores. 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?

Two well-structured sentences, front-loaded with purpose. Every sentence adds value; no redundancy or 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?

No output schema, but description fully explains return format (passages with offsets and similarity scores), input constraints (200K chars), embedding model, and pairing with sibling. 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%, but description adds meaning: explains 'text' as already pulled content, 'query' as natural language, and 'limit' as max passages with default. Also describes return format 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 semantic search inside a fetched record, using specific verbs and resource. Examples like SEC 10-K body and article distinguish it from sibling tools like 'search' which likely search external 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?

Explicitly states when to use: when the record is too big for the prompt. Compares with sibling 'ask_pipeworx_grounded' and explains pairing workflow, providing clear context and alternatives.

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

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

Annotations provide readOnlyHint=false (mutating), idempotentHint=true, destructiveHint=false. Description adds: returns subscription id, OAuth requirement, anonymous/BYO cannot persist, type-specific parameter examples, delivery details including webhook HMAC signing, one-time secret return, and auto-disable after 10 failures. Exceeds annotation coverage.

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

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

Despite length, every sentence adds value. Starts with purpose, then prerequisites, then type listing with examples, then delivery channels. Well-organized and front-loaded. 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?

Given complexity (3 parameters including nested objects, no output schema), description covers all parameters, return value, prerequisites, limitations, and special behaviors. No output schema, but return value is stated. Highly complete.

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

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

Schema coverage is 100% but description adds significant value: for 'type' enum, it explains each option with examples; for 'params', it gives detailed JavaScript object examples per type; for 'delivery', it explains email, SMS with verification and rate limit, and webhook with signing details. Adds meaning beyond schema.

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

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

Description clearly states 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' This identifies the verb (create), resource (subscription), and output. Title 'Subscribe to Alerts' aligns perfectly. Distinct 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?

Provides explicit context: requires Pipeworx OAuth account, identifies supported types with examples, explains delivery channels and their limitations (phone verification, 10/day cap, webhook auto-disable). Does not explicitly state when not to use or name alternative tools, but context is clear given siblings.

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

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

The description discloses that it returns 'category-bucketed example questions' and 'each with the exact tool + argument shape', which goes beyond the annotations (readOnly, openWorld, idempotent). No contradiction with annotations. It could mention any potential limits like data freshness or size constraints, but it's still informative.

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

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

The description is somewhat long but well-structured, starting with common user queries, then explaining the output, and ending with usage guidance. Every sentence adds value, though it could be slightly more concise without losing information.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description fully explains the return format (category-bucketed example questions with tool+argument shapes) and usage. It covers all necessary context for an agent to decide when and how to call this tool.

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

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

Schema description coverage is 100%, and the description adds context by listing example values for the topic parameter ('finance, pharma, economics, etc.') and clarifying that omitting it gives a full spread. This adds meaning 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?

The description clearly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions with exact tool+argument shapes. It distinguishes from sibling tools by emphasizing 'use this FIRST' and covers multiple use cases like 'what can you do?' and 'getting started'.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. It also explains when to pass the topic parameter to focus on a specific area, providing clear context for usage.

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

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

Adds behavioral context beyond annotations: ownership check, deactivation vs deletion, and availability of history via recent_alerts. 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?

Two sentences, front-loaded with main action and constraint, second sentence adds behavioral detail. 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?

Complete for a single-parameter tool with no output schema; covers action, constraints, effect, and impact on 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 already fully describes the 'id' parameter. The description adds minimal extra meaning ('by id' and ownership context).

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 'Cancel' and resource 'subscription' with additional detail on deactivation vs deletion, distinguishing it from 'subscribe' and 'list_subscriptions'.

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

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

Explicit ownership enforcement guides when to use; mentions deactivation (not deletion) but lacks explicit mention of alternatives among siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds significant behavioral context: it reveals the data sources (SEC EDGAR + XBRL), return values (verdict, structured form, actual value with citation, delta), and notes the tool handles specific financial claims. 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 usage cues and efficiently structured. While slightly verbose, every sentence adds necessary detail. It could be shortened slightly without losing clarity, but it remains highly effective.

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

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

Given the rich annotations and lack of output schema, the description fully covers what the tool does, its domain limitations, output structure, and when to use it. It provides a complete picture 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?

With 100% schema coverage, the description adds value by providing examples (e.g., 'Apple’s FY2024 revenue was $400 billion') and clarifying that the claim is natural-language. This goes beyond the schema's brief description, aiding the AI in understanding acceptable input format.

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

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

The description clearly states the tool verifies natural-language claims against authoritative sources, specifically company-financial claims for public US companies. It distinguishes itself from siblings by noting it replaces 4-6 sequential calls, and provides example phrasings (e.g., 'fact check', 'verify the claim that…').

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

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

The description explicitly says when to use it: 'whenever the agent needs to check whether something a user said is factually correct.' It also specifies the domain (company-financial claims) and mentions it replaces sequential calls, though it doesn't explicitly list alternative tools for non-financial claims.

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