Podatki Si

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it probes LLMs and passes the API key to Anthropic, implying non-destructive behavior. It also mentions cost implications (BYO key). No contradictions.

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

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

The description is a single paragraph that front-loads the purpose and output. Every sentence adds value: default model, optional parameter, use cases. No wasted words.

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

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

Given 4 parameters and no output schema, the description covers the return format (per-model fields and combined view), usage context, and parameter behavior. It does not detail edge cases or error handling, but is complete for an experienced user.

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

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

Schema coverage is 100%, so baseline 3. The description adds meaning: 'models' list is optional and if omitted only workers-ai is used; '_apiKey' is passed to Anthropic; 'context' helps disambiguate. This exceeds 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 it probes LLMs for knowledge about an entity and returns visibility scores. It specifies the default model and optional Anthropic probe, differentiating it from sibling tools like ask_pipeworx (general Q&A) and entity_profile (entity lookup).

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

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

The description lists explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and when an API key is needed. While it doesn't explicitly say when not to use it, the guidance is clear and sufficient.

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

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

Annotations (readOnlyHint, idempotentHint, etc.) are present and convey safety. The description adds value by explaining the routing behavior to thousands of tools, citation format, and works on all tiers. It doesn't contradict annotations. Minor omission: could mention potential latency or rate limits, but overall transparent.

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

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

The description is well-structured with a clear front-loaded directive ('PREFER OVER WEB SEARCH'). While somewhat lengthy, each sentence serves a purpose, providing examples and usage context 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 complexity and lack of output schema, the description is quite complete: it covers the range of data sources, provides example queries, and explains when to use alternatives. It could briefly mention output format details but is largely sufficient.

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

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

The input schema has 100% coverage, with all six parameters (including aliases) described. The description reinforces this by listing aliases and providing example questions, adding pragmatic value 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?

The description clearly states the tool's purpose: routing questions to a vast array of data sources for authoritative structured data. It uses specific verbs like 'routes', 'fills arguments', and 'returns structured answer', and distinguishes from sibling tools by explicitly recommending it as the default entry point over ask_pipeworx_grounded and deep_research.

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

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

Excellent usage guidance: explicitly states 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and provides specific example queries. It also tells when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistant single answers, deep_research for broad multi-part questions), giving clear exclusion criteria.

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

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

The description adds significant behavioral details beyond annotations, including refusal reasons, evidence extraction, and the fact that it only uses tool results to answer. Annotations already indicate read-only and idempotent, but the description enriches with refusal mechanism and output structure.

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 and front-loaded with purpose. While detailed, every sentence contributes valuable information. Slightly verbose but justified given complexity.

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

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

The description fully explains the return structure (answer, evidence, confidence, source, refusal reason) and possible refusal reasons, which is essential since there is no output schema. It also covers process and cost, making it complete for an agent to decide usage.

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

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

Schema coverage is 100% (all parameters are aliases for 'question'), so baseline is 3. The description does not add parameter-level details beyond the schema, but it implicitly clarifies the use of the question parameter through the overall purpose.

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

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

The description clearly states the tool is a 'Hallucination-resistant answer mode for high-stakes reads,' specifying the verb (answers), resource (high-stakes queries), and differentiating it from sibling 'ask_pipeworx' by its refusal mechanism and evidence extraction.

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 defines when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' Also provides when not to use: 'prefer ask_pipeworx for casual lookups,' and notes the cost difference.

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 extensive behavioral details: market resolution, classification, fan-out logic, safety mechanisms (low-confidence short-circuit, closed market handling), tradeability warnings, cancellation rule parsing, and response shapes. No contradictions with annotations.

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

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

The description is comprehensive but very long (likely >500 words). It is well-structured with sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.) and front-loads the core purpose. However, brevity is sacrificed for completeness, making it less concise than ideal for quick agent parsing.

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 classifiers, fan-out logic, safety checks, detailed response fields), the description covers all necessary context. It explains edge cases (closed markets, wide spreads, cancellation rules), provides examples for different bet types, and addresses how to interpret uncertainty. Without an output schema, the description fully compensates.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds value by explaining acceptable inputs for 'market' (slug, URL, question) with examples, clarifying the 'depth' enum values and default, and detailing when to set 'include_raw' to true. This enhances 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 researches Polymarket bets by pulling Pipeworx data, with specific verb ('research') and resource ('Polymarket bet'). It distinguishes from sibling tools like polymarket_arbitrage or polymarket_edges by explicitly stating use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'.

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

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

The description provides explicit usage guidance with phrases like 'Use for...' and lists three typical queries. It also gives context on when results should be trusted (e.g., inspect market_match_confidence) and warns about resolution-rule risk. However, it does not explicitly exclude use cases or name alternative tools for different 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 provide readOnlyHint and idempotentHint, but the description adds valuable behavioral context: it explains data sources (SEC EDGAR, FAERS), special handling of fiscal years, result sorting by primary metric, and citation URIs. 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 information-dense but not overly verbose. It front-loads key trigger phrases and every sentence contributes meaningfully. Minor redundancy (e.g., repeating 'largest/most/biggest') but overall well-structured.

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

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

Given the tool's complexity (2 params, different data sources, sorting, citations) and the absence of output schema, the description covers all essential aspects: purpose, usage, parameter details, behavioral nuances, and result format. It leaves no critical 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 coverage is 100%, so parameters are already defined. The description adds value by providing concrete examples (e.g., '["AAPL","MSFT"]') and explaining how each type behaves (company financials vs drug adverse-event counts), which aids 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 opens with clear trigger phrases like 'Compare X and Y' and explicitly states 'side-by-side comparison of 2-5 companies or drugs in ONE parallel call.' It effectively distinguishes from siblings like entity_profile by emphasizing parallel vs sequential 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 includes an explicit directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also clarifies when to use each type (company vs drug) by describing the data sources and metrics.

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, etc. Description adds value by specifying return scope (full metadata including resources and resource_ids) beyond annotations.

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

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

Single concise sentence with front-loaded action and resource, zero 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?

No output schema, so description carries full burden. Clearly states return value includes full metadata, all resources, and resource_ids. Adequate for a simple retrieval 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 100% of param 'id' with description. Description reaffirms 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?

Description clearly states verb (show), resource (full metadata for one dataset), and distinguishes from sibling search_datasets by id or name. It is specific and unambiguous.

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

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

Explicitly instructs to use id or name from search_datasets, implying post-search usage. Does not explicitly exclude alternatives but provides clear context for when to invoke.

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, etc.), the description discloses account/payment requirements, execution time (15-60s), behavior of returning gaps[] for unanswered facets, and that it uses parallel decomposition. 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 long but well-structured with critical info upfront (account requirement, fallback tool). Every sentence adds value, covering purpose, usage, details, and performance. Minor redundancy 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?

No output schema, but the description fully explains the return format (findings packet with evidence, confidence, source, timestamp, stable citation, gaps[]) and expected latency. This provides complete context for agent usage.

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

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

Schema coverage is 100%, so the description does not need to compensate for missing parameter docs. However, it adds valuable context: explains depth enum values with defaults (quick=3, standard=5, thorough=8) and clarifies the question parameter accepts broad/multi-part natural language.

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

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

The description states the tool performs 'grounded multi-source research across Pipeworx's 1129 structured data sources', decomposes questions, routes to 4,482 tools in parallel, and returns a findings packet. It clearly distinguishes from siblings like ask_pipeworx and specifies it is not open-web search.

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

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

Explicitly advises use for broad/multi-part questions over structured data, and directs to ask_pipeworx for single lookups or breaking/news topics. Also notes account requirement and provides a fallback if not signed in. Covers when to use, when not, 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 indicate readOnlyHint, idempotentHint, and destructiveHint false. The description adds that it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This discloses important behavioral traits beyond annotations.

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

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

The description is front-loaded with the core purpose and includes many examples. While it is longer than minimal, every sentence adds value—listing domains, explaining output, and providing usage guidance. It is well-structured and efficient.

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

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

No output schema exists, but the description explains the return structure (names, descriptions, schemas with examples) and hints that results are ready to call. It does not detail pagination or sorting, but for a discovery tool, the coverage is adequate.

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

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

Schema description coverage is 100%, so the schema already documents parameters thoroughly. The description provides examples and reiterates natural language usage, but does not add new semantic information beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many example domains (SEC filings, financials, FDA drugs, etc.), making it distinct from sibling tools like search_datasets or search_within, which search data rather than 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 advises: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This tells the agent when to use it and implicitly when not to (if the tool is already known).

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 rich detail: parallel fan-out across multiple sources, specific return fields, and a known limitation (USPTO soft-fail). 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?

Dense but well-organized: starts with example queries, then purpose, then details. Every sentence adds value. Could be slightly more structured but is 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?

Despite no output schema, the description comprehensively explains return fields (cik, company_name, recent_filings, fundamentals, patents, news, LEI) and covers inputs, sources, and limitations, making it complete.

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

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

Schema covers both parameters fully (100% coverage). Description adds context: 'type' must be 'company', 'value' is ticker or zero-padded CIK, and clarifies that names are not supported, pointing to resolve_entity.

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 creates a 'full cross-source profile of a US public company in ONE parallel call.' It lists specific sources and return fields, and distinguishes from alternatives by advising preference over chaining single-pack lookups.

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

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

Provides explicit guidance: use when user asks for a holistic view, and prefer over chaining. It also advises to use resolve_entity if only a name is available. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds 'previously stored' implying the key must exist, but does not clarify behavior for non-existent keys or provide additional side effects. With annotations present, this is adequate but not exceptional; no contradiction.

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

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

Two clear sentences, no wasted words. Front-loaded with action and resource, followed by usage guidance and sibling references.

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, schema covers it, annotations cover safety, and description provides usage guidance. No output schema needed. Complete and sufficient.

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 describes 'key' as 'Memory key to delete.' The description adds 'previously stored' which adds minimal context. Baseline of 3 is appropriate as schema already documents the parameter.

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

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

The description clearly states 'Delete a previously stored memory by key.' It specifies the verb 'delete' and the resource 'memory' with a key identifier. Distinguishes from sibling tools 'remember' and 'recall' by implying it is the complementary operation.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also advises pairing with 'remember and recall,' providing clear context for usage versus alternatives.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds the process: fetches page, extracts title/description/key links, emits markdown. This explains internal behavior beyond annotations, but 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, efficiently worded, front-loaded with action. Every sentence adds value without redundancy.

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

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

Given the simplicity (2 params, no output schema, annotations present), the description fully covers purpose, output format, and use cases. No gaps.

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

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

Schema coverage is 100% with descriptions for both parameters. The tool description does not add specific parameter details beyond that, so baseline 3 is appropriate.

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

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

Description clearly states it generates a llms.txt file for a URL, specifying the output format and use cases. It distinguishes itself from nearby tools like ai_visibility_check, which focus on checking AI presence rather than generating a file.

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 contexts: getting a client's site indexed, drafting own project, or auditing competitors. Lacks explicit 'when not to use' or alternatives, but the use cases are well-defined.

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

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

Annotations already declare readOnlyHint and idempotentHint, so description adds value by listing return fields and parameter behavior, but does not contradict annotations.

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

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

Two sentences, front-loaded with main purpose, then fields, then usage—no wasted words.

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

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

Despite no output schema, description lists return fields and parameter; covers all needed context for a simple list 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?

With 100% schema coverage for the single parameter, the description adds no additional meaning beyond the schema's description of include_inactive.

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,' providing a specific verb and resource. It distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' giving concrete when-to-use guidance.

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

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

The description adds significant behavioral context beyond annotations, including rate limits (5 per identifier per day), the fact that it doesn't count against tool-call quota, and that the team reads digests daily. There is no contradiction with annotations.

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

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

The description is concise (a few sentences), front-loaded with the core purpose, and structured into clear sections (when to use, what to avoid, behavioral notes). Every sentence adds value without redundancy.

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

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

Despite no output schema, the description fully explains the tool's effect (team reads digests, roadmap impact) and constraints (rate limits, quota-free). This is complete for a feedback submission 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?

The input schema has 100% coverage, but the description adds valuable instruction on how to write the message ('Be specific', '1-2 sentences typical', '2000 chars max') and explains the enum values. This goes beyond the schema's descriptions, justifying a score above baseline 3.

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

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

The description clearly states the tool's purpose: to send feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It uses specific verbs ('tell', 'use when') and distinguishes itself from sibling tools, which are primarily query and research tools.

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

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

The description explicitly outlines when to use the tool for each feedback type (bug, feature/data_gap, praise) and provides clear instructions on what not to do (avoid pasting end-user prompts). It also mentions rate limits and that the tool is free, guiding appropriate usage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by revealing the data source (CF analytics-engine), no PII, and caching details (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?

Two well-structured paragraphs: first summarizes output and windows, second lists use cases and technical details. Every sentence adds value, no redundancy.

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

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

Despite no output schema, the description covers return items (top tools, packs, volume), aggregation method, privacy, and caching. Complete for a simple trending tool with one parameter.

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 schema description already explains the window enum and its behavior. The description repeats similar guidance but does not add new parameter semantics beyond what the schema provides.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a configurable window, with specific verb 'returns' and resource. It distinguishes from siblings by focusing on aggregated trending data rather than individual queries.

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

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

Three explicit use cases are provided (discovering hot data sources, confirming canonical tools, aligning use cases). Window selection guidance is given. No direct sibling alternates, so when-not-to-use is implied by the unique purpose.

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

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

Annotations already indicate read-only, idempotent, open-world. Description adds extensive behavioral context: walks child markets, checks ordering, computes partition_check, uses Jaccard similarity, filters placeholder partitions, and exposes realizable vs theoretical edge via fill check. 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?

Description is well-structured with headers (event mode, topic mode, SEMANTIC ANCHOR, etc.) and front-loads the requirement. Though lengthy, the structure aids readability. Could be slightly trimmed, but organized effectively.

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

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

Despite no output schema, the description thoroughly explains the response structure (opportunities[], partition_check, fill check details). Covers modes, parameters, edge cases, and related tools. 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 100% with descriptions, but description adds meaningful context: explains the two modes, usage scenarios, and the mutual exclusivity (though schema says both optional). The description contradicts schema by requiring one of event or topic, but overall adds value.

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 precisely states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes event mode (specific market) from topic mode (cross-event scanning), and contrasts with sibling tools like polymarket_fill_risk and polymarket_kalshi_spread.

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

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

Explicitly requires one of event or topic, recommends event for specific markets and topic for cross-event scanning. Warns about failing with no args, explains when to use polymarket_fill_risk for custom sizing, and describes fill check logic to avoid bad trades.

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 provides extensive behavioral context beyond annotations: model families, caching, edge calculations, slippage, and tradeable-edge filters. Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, and the description is consistent and adds value.

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

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

The description is well-structured with sections and bullet points but is very long. While detailed, it could be more concise to meet the needs of an AI agent scanning 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?

Given the complexity (9 optional params, no output schema), the description thoroughly explains the output structure, diagnostics, and rationale for exclusions. It is complete for an agent to understand what the tool returns and how to use it.

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

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

Schema coverage is 100% with detailed parameter descriptions. The tool description summarizes knobs but adds little new meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'scan' and 'return', the resource 'Polymarket markets', and the specific purpose 'where Pipeworx data disagrees with market price'. It distinguishes the tool from siblings like polymarket_arbitrage and polymarket_edge_tracker by focusing on Pipeworx data discrepancies.

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 positions the tool for 'what should I bet on today' and explains caching and knob usage. However, it lacks explicit exclusions for when to use alternatives like polymarket_edge_tracker for historical data, though it does note that Fed bets are excluded from ranking.

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

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

Annotations already indicate safe, idempotent read operations. The description adds critical behavioral details: data sourced from daily snapshots, 60-day TTL, decay computed from daily closes (not intraday), and gaps when no scans occurred. 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 efficiently packed with essential information. It is front-loaded with the core question. Every sentence adds value, though it could be slightly more structured (e.g., bullet points for response fields).

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 details the response structure: tracked[], expired[], snapshot_dates[], including field meanings like edge_pp_net time-series, trend, decay, lifespan. This makes the tool fully understandable 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 both parameters. The description reinforces defaults and adds context (days max 30, window families). Baseline is 3, and the description provides marginal added 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 labels the tool as 'Edge persistence and decay telemetry' and answers the specific question 'how long has this edge existed and is it shrinking?' It uses a specific verb (track) and resource (edge persistence), distinguishing it from siblings like polymarket_edges (current edges) and polymarket_arbitrage.

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: to assess edge persistence and decay, contrasting fresh vs. old edges. It doesn't explicitly list when not to use or name alternatives, but the context is strong enough to guide appropriate use.

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

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

Annotations already mark the tool as readOnly, idempotent, non-destructive. The description adds rich behavioral details: walks the ladder, returns specific fields (top_of_book, vwap_fill_price, slippage, etc.), explains basket leg fills, thin legs, forced directional risk, and the dominant loss mode. 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 well-structured with clear sections for single-market and basket modes. Every sentence adds value, though it could be slightly more terse without losing clarity. The key purpose is 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?

Without an output schema, the description comprehensively explains what each mode returns and covers edge cases like thin books, partial fills, and directional risk. It positions the tool relative to siblings (arbitrage, edges) and gives concrete thresholds (~$500, ~$1000 default).

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

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

Schema coverage is 100% with parameter descriptions. The description adds significant context: explains the mutual exclusivity of market and event, defaults for side and size_usd, and different interpretations of size_usd per mode (max spend vs target proceeds vs settlement notional). 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 the tool's purpose as a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It differentiates between single-market and basket modes and explicitly mentions use cases like checking before arbitrage actions or large trades, distinguishing it from sibling tools.

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

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

Explicit guidance is given: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains when to use single-market vs basket mode, default behaviors, and warns about partial fills converting an arb into an unhedged position.

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 goes far beyond by detailing safety fields (compatibility_warning, temporal_alignment, skipped_cross_type/subtype), explaining edge cases (non-equivalent bet shapes, temporal gaps), and describing the response structure (leg-by-leg prices, top_spreads_pp). This provides rich behavioral context that annotations alone do not cover.

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

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

The description is somewhat lengthy but well-structured: opens with core purpose, then explains modes, safety fields, and caveats. Each sentence contributes useful information. Minor redundancy could be trimmed (e.g., 'pre-mapped ≠ tradeable' at end is already implied), but overall it is efficient for the complexity.

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

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

Given the complexity of cross-venue spread detection, the description covers input modes, response fields, edge cases, and when to avoid the tool. With no output schema, it sufficiently describes return values and safety indicators. The tool has 3 optional params and the description provides enough guidance for correct invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for topic values (e.g., 'fed', 'btc') and explaining overriding behavior when explicit tickers are provided. This enhances understanding beyond the schema descriptions.

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

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

The description clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question, distinguishing it from intra-venue tools like polymarket_arbitrage. It provides a specific verb ('compute spread') and identifies the two venues, 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 covers two usage modes (topic shortcuts and explicit event pairing) and warns that most pre-mapped topics yield a compatibility_warning, implying they may not be tradeable. It also explains when spreads are meaningless (temporal misalignment, shape mismatch). However, it does not explicitly name sibling tools as alternatives, e.g., suggesting polymarket_arbitrage for intra-venue arbitrage.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the constraint about DataStore-enabled resources, which is useful but does not disclose potential errors or performance characteristics.

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

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

The description is two sentences long, front-loaded with the main action and resource, then optional parameters and a key constraint. No filler or redundancy.

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

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

The tool has no output schema, so the description should explain return format. It mentions 'pulling rows' but does not specify the structure or pagination details beyond limit/offset. Annotations cover safety but not output.

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

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

All 4 parameters have descriptions in the input schema (100% coverage). The description adds context by stating that 'resource_id' comes from dataset/search_datasets, slightly enhancing schema meaning.

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

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

The description clearly states the verb 'pull rows' and the specific resource 'tabular Slovenia Open Data resource (CKAN DataStore) by resource_id'. It distinguishes this tool from siblings like 'search_datasets' by focusing on querying data within a specific resource.

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 specifies a key prerequisite: 'only resources with the DataStore enabled are queryable'. It also implies that resource_id comes from dataset/search_datasets. However, it does not explicitly state when to use this tool versus alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds important behavioral context: the scope (identifier-based) and the dual behavior (key present vs. omitted). It does not contradict annotations. Some details like error handling or rate limits are omitted but not critical.

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

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

The description is two sentences that convey the core function, usage context, and relationships. It is front-loaded with the primary action. The second sentence is slightly verbose with examples but still efficient. No wasted words, but could be tighter. Still good for its length.

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

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

Given the tool's simplicity (1 optional param, no output schema, clear annotations), the description provides complete context: what it does, how to use it (both modes), scope, and sibling relationships. The return values are evident from the described behavior. No gaps noted.

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 enhances understanding by explaining both usage patterns: 'omit the key argument' to list all keys, or provide a key to retrieve a specific value. The examples in the schema are complemented by the description's use cases (ticker, address, notes). The description fully satisfies the need for parameter semantics.

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

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

The description precisely states the tool's function: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It clearly distinguishes from sibling tools 'remember' and 'forget' by explicitly pairing with them, ensuring the agent knows exactly what this tool does.

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

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

The description explicitly states when to use: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' It also implies when not to use (if data not saved) and provides scope information ('Scoped to your identifier'). It names alternatives: 'Pair with remember to save, forget to delete.' This is exemplary 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 state readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral details: that mark_read:true flags events as read so next call shows newer ones, that polls work fine, and that the same feed is accessible via a URL. No contradictions.

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

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

The description is concise, consisting of 4 sentences. It front-loads the purpose and then provides additional details in a logical order. Every sentence adds value, and there is no redundant information.

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

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

Despite lacking an output schema, the description sufficiently explains the return structure (source, citation_uri, raw payload). It also covers polling behavior, an alternative access method, and filtering options. For a read-only tool with only 5 parameters, this is comprehensive.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra meaning beyond schema: it provides an example for the type parameter (e.g., 'sec_8k'), explains the effect of mark_read (flagging events read to avoid repeats), and implies the limit parameter via 'most recent alerts'. This additional context improves usability.

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 'Pull' and the resource 'fired events from your subscription feed', and specifies that it returns the most recent alerts with details like source, citation_uri, and raw payload. It distinguishes itself from sibling tools like list_subscriptions by focusing on alert retrieval.

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

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

The description explains when to use the tool (to pull alerts from the subscription feed) and offers an alternative (the same data is available via a GET endpoint for scripts/dashboards). It also mentions filtering options. However, it doesn't explicitly contrast with sibling tools like subscribe or unsubscribe, but the 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, idempotentHint, etc. The description adds behavioral context: fans out to multiple sources, fallback behavior (GDELT→GNews), soft-fail for patents, and return structure. 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?

Well-structured with front-loaded examples, parameter details, return structure, and sibling differentiation. Every sentence adds value, though slightly verbose; could be tightened slightly.

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

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

Despite no output schema, description thoroughly covers return format (changes grouped by source, total_changes, citation URIs), sources used, parameter behavior, and limitations (patents soft-fail). Fully adequate for a tool with 3 required params and moderate complexity.

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

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

Schema coverage is 100%, and the description enriches parameter meaning: explains 'since' accepts ISO date or relative shorthand with examples, 'value' accepts ticker or CIK, and 'type' is limited to 'company'. Provides usage hints like 'Use "30d" or "1m" for typical monitoring.'

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 aggregates recent changes (SEC filings, news, patents) for a company in a single call, with example queries like 'What's new with X' and explicitly distinguishes from sibling 'entity_profile' by stating when to use the alternative.

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

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

Provides explicit guidance on when to use this tool (change feed) vs. 'entity_profile' (static profile), and offers usage examples for the 'since' parameter ('30d' or '1m' for typical monitoring).

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

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

The description goes beyond annotations by disclosing that data is 'scoped by your identifier', that 'authenticated users get persistent memory' while anonymous sessions retain for 24 hours, and that it is a key-value store. This adds valuable behavioral context beyond what the annotations (readOnlyHint=false, idempotentHint=true) provide, and no contradictions exist.

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

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

The description is concise (5 sentences) and front-loaded with the main purpose. Each sentence adds meaningful information: purpose, usage scenarios, storage model, persistence details, and pairing with other 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?

For a simple store operation with no output schema, the description is complete. It covers purpose, usage, behavioral traits (key-value, scoping, persistence), and pairing with recall/forget. The agent has all necessary context 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% (both key and value have descriptions) so the baseline is 3. The description provides examples of keys (e.g., 'subject_property', 'target_ticker') which adds some context, but the primary meaning is already captured in the schema. No additional parameter semantics are needed beyond what schema provides.

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

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

The description clearly states 'Save data the agent will need to reuse later' with a specific verb and resource (key-value memory). It distinguishes from siblings 'recall' and 'forget' by mentioning pairing with them, and the examples (ticker, address, preference) further clarify the resource scope.

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

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

The description provides explicit 'when to use' guidance: 'when you discover something worth carrying forward' and gives concrete examples. It mentions pairing with recall and forget, but does not explicitly state 'when not to use' or alternative tools; however, the context is sufficient for an agent to infer appropriate usage.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description reinforces this by describing the tool as a look-up that cascades through multiple endpoints internally. It adds transparency beyond annotations, such as mentioning the internal cascade and the returned citation URIs. No contradictions.

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

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

The description is well-structured: it starts with example phrases, states the primary use, then details each supported type. It is informative without being overly verbose. A slight cut could be made, but it remains focused and front-loaded with the key purpose.

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

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

With only 2 parameters and no output schema, the description fully compensates by detailing the return values for both company and drug types. It covers all needed context: supported input formats, what identifiers are returned, and citation URIs. The tool's moderate complexity is fully addressed.

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

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

Schema coverage is 100%, but the description adds substantial value beyond the schema. For each type, it explains acceptable input formats (e.g., ticker, CIK, or name for company) and details the return structure (ticker+CIK+URI for company, RxCUI+ingredient+URI for drug). This is more informative than the schema alone.

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

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

The description clearly states the tool's purpose: resolving user-spoken names to canonical identifiers required by other tools. It uses specific examples like 'What's the ticker for...' and explicitly lists supported types (company, drug). This directly distinguishes it from sibling tools such as 'compare_entities' or '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?

The description gives explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains what each entity type requires and returns, and notes that using resolve_entity replaces 2-3 manual lookups. However, it does not explicitly state when NOT to use it or provide alternatives, which would earn a 5.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds value by explaining internal mechanism ('probes each entity with ai_visibility_check'), output structure ('ranked list with score, confidence, signal density'), and authentication nuance (API key for anthropic model). 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?

Two sentences, front-loaded with purpose, no redundant phrases. Every sentence adds value.

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

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

Given no output schema, description explains return fields adequately. Annotations cover safety and idempotency. Missing details on error handling or rate limits, but overall sufficient for a non-destructive, read-only tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minor context: treats first entity as 'subject' for narrative, and mentions 'score, confidence, signal density' in returns, which maps to entities parameter. Does not elaborate on models or _apiKey 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 uses specific verb 'compare' and resource 'AI visibility across multiple entities', with concrete examples ('does Claude know about us as well as our competitors?'). Clearly distinguishes from sibling tool ai_visibility_check by explaining it probes each entity with that tool and ranks results.

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?

States explicit use case ('competitive AI-marketing audits') and provides an example question. Implies context for use, but does not explicitly state when not to use or name alternative tools beyond the sibling list.

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

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

The description adds significant behavioral context beyond annotations: partial failure handling (bundlephobia timeout, sources_failed), latency warning (5-30s for first measurement), and graceful degradation. Annotations already declare readOnlyHint, idempotentHint, etc., and description aligns perfectly.

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

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

The description is concise yet comprehensive, with each sentence serving a purpose. It is front-loaded with the core purpose, followed by usage guidance, output details, ecosystem scope, and behavior notes. 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?

Given the lack of an output schema, the description adequately lists the return fields and their meanings. It covers error handling, latency, and ecosystem scope. However, it could be slightly more precise about the output structure (e.g., the shape of per-advisory detail).

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 documented (package name with scoped packages accepted, version with default). The description does not add extra parameter semantics beyond the schema, but compensates partially by explaining the return structure and composite behavior. No contradiction or omissions.

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 composite check for npm packages, specifying the two data sources (deps.dev and bundlephobia) and the exact questions it answers. It distinguishes itself from sibling tools by stating the ecosystem scope and fallback approach.

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

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

The description explicitly states when to use the tool ('is X safe / popular / small' or 'what does adding lodash cost me'). It also mentions ecosystem limitations, but does not explicitly state when not to use it (e.g., for non-npm packages or when a single source is needed).

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful behavioral context by specifying the return structure and linking to query_resource, which is valuable 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?

Two concise sentences, front-loaded with action and scope, no wasted words.

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

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

Given the tool's simplicity and comprehensive annotations, the description fully explains the return format and purpose, making it complete 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 the description adds minimal extra meaning over the schema. It implies the query parameter is for keywords but does not elaborate on limit/offset beyond what the schema provides.

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

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

The description clearly states it searches for datasets by keyword in a specific data source (Slovenia Open Data) and details the return fields, distinguishing it from sibling tools like query_resource.

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

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

The description provides context by mentioning query_resource for subsequent use, but does not explicitly compare to alternatives or state when not to use this tool.

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

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

Annotations already indicate readOnly=true, idempotent, non-destructive. The description adds significant behavioral details: returns top-N passages with character offsets and similarity scores, uses BGE-base-en embeddings with cosine similarity over 500-char windows, and has a 200K char cap with truncation flag. 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 efficiently structured: first sentence states purpose and scope, then usage guidance, then technical details. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description thoroughly explains the output format (passages with offsets, scores), embedding model, windowing strategy, and truncation limit. For a tool with 3 parameters, this is 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?

All three parameters are fully described in the schema (100% coverage). The description adds practical examples for the 'query' parameter and clarifies the 'text' max length. It marginally enhances understanding beyond the schema.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' using a specific verb and resource. It distinguishes itself from sibling tools by mentioning its use case for large records and pairing with 'ask_pipeworx_grounded'.

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

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

Explicitly states when to use: 'Use when the record is too big to cram into the prompt.' It also names an alternative/partner tool ('Pairs with ask_pipeworx_grounded'), providing clear context for selection.

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

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

The description discloses required authentication, return value, delivery channel details, and rate limits (10/day for SMS). Annotations indicate mutation and idempotency, which the description supports 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 somewhat long but well-organized, starting with purpose, then requirements, types, and delivery. Each sentence adds value, though minor trimming could improve conciseness.

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

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

Given the tool's complexity and lack of output schema, the description covers return values, type-specific parameters, and delivery options. It misses error handling or duplicate behavior, but annotations cover idempotency.

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

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

All parameters are fully described in the schema (100% coverage). The description adds significant meaning with examples, type-specific filter explanations, and delivery channel details, going 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 the tool creates a subscription to a live-data event stream and returns the subscription ID. It uses a specific verb and resource, and distinguishes from siblings like list_subscriptions and unsubscribe.

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

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

The description specifies that a Pipeworx OAuth account is required and lists supported types and parameters. It provides clear context for when the tool should be used, but does not explicitly mention alternatives or when not to use it.

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

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

Adds behavioral details beyond annotations: returns example questions, can be called with no arguments or a topic, and draws from the live catalog. 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?

Reasonably concise given the complexity; front-loaded with common user queries. Every sentence adds value, though slightly long but justified by the tool's role.

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?

Fully adequate for an onboarding tool with no output schema. Covers purpose, usage, parameter, and guidance, making it complete for the 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 has 100% coverage, and the description elaborates on the topic parameter by listing example values ('finance | pharma | ...') and explaining that omitting it gives a cross-category spread, adding meaning beyond the schema.

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

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

The description clearly states the tool returns category-bucketed example questions with tool+argument shapes, positioning it as the onboarding entry point. It distinguishes from siblings like discover_tools by focusing on guiding new users on what to ask.

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

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

Explicitly tells when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' Also explains the optional topic parameter for focus.

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 show destructiveHint=false, and description adds 'deactivated (not deleted)' confirming no data loss. Also explains ownership enforcement, adding value beyond annotations.

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

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

Two sentences, front-loaded with action, no redundant information. Every sentence earns its place.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, ownership, and deactivation. Adequate for the 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% with a single parameter 'id' adequately described. Description does not add further parameter details, but baseline is appropriate.

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

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

The description clearly states the verb 'cancel' and resource 'subscription by id', distinguishing it from siblings like 'subscribe' and 'list_subscriptions'.

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

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

Explicitly states ownership enforcement (only cancel own subscriptions) and deactivation behavior. No explicit when-not-to-use, but context is clear given sibling tools.

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

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

Annotations already indicate read-only, open-world, and idempotent behavior. Description adds value by detailing return verdict types, extracted structure, data sources (SEC EDGAR + XBRL), and mentioning it replaces multiple sequential calls. 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?

Description is front-loaded with usage examples and specific details. It is slightly verbose but every sentence adds value. Structure is logical: examples, domain, technical details, and performance benefits.

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 the description fully explains the return value (verdict, structure, actual value, citation, delta). It also states domain limitations (v1 company-financial). All necessary information for an agent to invoke correctly is present.

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?

Single parameter 'claim' has a clear description with examples (e.g., 'Apple's FY2024 revenue was $400 billion'). Schema coverage is 100%, and the description adds natural-language context beyond the schema's technical 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 performs natural-language claim verification against authoritative sources, specifically for company-financial claims. It provides examples of input phrasing and distinguishes its purpose from other tools by being a specialized fact-checking tool.

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

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

Explicitly says to use when checking factual correctness of user statements. Provides domain specificity (company-financial) but does not list when not to use or alternative tools among siblings.

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