Pypi Stats

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it explains the default model (free Workers AI), the BYO key model for Anthropic, and the return structure (per-model score, confidence, signals, raw_response). 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, front-loaded with the main purpose, and every sentence adds value. It avoids redundancy and is 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 no output schema, the description compensates by detailing the return format (per-model object + combined view). It covers the default behavior, optional parameters, and pricing. The tool's 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%. The description adds meaning beyond the schema: it explains default model, free vs paid, and the purpose of the 'context' parameter for disambiguation. This enriches the agent's understanding of how to use parameters.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and returns a visibility score (0-100). It specifies the verb 'probe', the resource 'LLMs', and the output format. This differentiates it from sibling tools like 'scan_competitor_ai_presence' which likely have different scopes.

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 calls out use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly state when not to use, but it provides clear context. It could be improved by mentioning alternative tools for different purposes, but the guidance is sufficient.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety and idempotence are covered. The description adds context about routing to sub-tools and returning citation URIs, which is useful but not critical given the annotation richness.

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

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

The description is well-structured, front-loading the key instruction ('PREFER OVER WEB SEARCH'), then listing data types, explaining the routing mechanism, and providing usage triggers and examples. 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 tool's complexity as a meta-query tool, the description covers its scope, use cases, and output format (structured answers with citations). It lacks details on response structure or error handling, but is sufficient for typical factual queries.

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 documentation covers 100% of parameters, including multiple aliases for the question field. The description does not add further parameter-specific meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description explicitly states the tool's purpose: to handle factual questions about structured data across many domains, routing to over 3,000 verified sources. It distinguishes itself from web search and provides specific examples, making its role very clear.

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

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

The description strongly advises preferring this tool over web search for factual queries, listing trigger phrases and example questions. While it doesn't explicitly state when not to use it, the positive guidance is robust and covers many 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?

The description adds significant behavioral context beyond annotations: it describes the extraction process, return format with confidence and evidence, and enumerates refusal reasons. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description aligns with these.

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, then efficiently covers routing, extraction, return format, refusal reasons, usage guidance, and comparison. 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 specifies the return format (fields, types, refusal reasons) and covers behavioral expectations, usage, and comparison with sibling. It is complete for the tool's complexity.

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

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

Schema description coverage is 100% for the 6 parameters (all aliases for 'question'). The description does not add additional parameter semantics beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It specifies that it extracts answers using only tool result contents and distinguishes it from the sibling 'ask_pipeworx' by noting the extra LLM call and extraction process.

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

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

The description provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts...' and when-not-to-use: 'prefer ask_pipeworx for casual lookups.' It also mentions the cost trade-off.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds extensive behavioral detail: resolution pipeline, classifier system, fan-out patterns, response shapes, resolver contract, parent_event extractor, news fallback, safety modes (low-confidence, closed markets, wide spreads), and status handling. No contradiction with annotations; consistent with read-only, idempotent behavior.

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

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

The description is verbose (multiple paragraphs) but well-structured with sections, examples, and headings. It could be more concise; some details like exhaustive fan-out examples and response field lists add length. While every sentence provides value, the overall length may hinder quick parsing by an agent.

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

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

Given no output schema and high complexity, the description comprehensively covers response shapes, resolver contract, parent_event extraction, news fields with fallback info, safety behaviors, and status handling. It leaves minimal gaps for an agent to understand expected outputs and edge cases.

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%, but the description significantly enhances parameter meaning: it explains depth enum values ('quick' vs 'thorough' with defaults), market parameter accepts multiple formats with examples, and include_raw explains when to use true/false and the size implications. This goes well beyond the schema descriptions.

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

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

The description explicitly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and provides classifiers and fan-out examples, clearly distinguishing its focused research role from sibling tools like polymarket_edges or 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 clearly states when to use the tool ('should I bet on X', 'what does the data say about Y') and provides multiple usage examples. It does not explicitly compare with alternatives or state when not to use, but the context is clear enough 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?

The description richly details behavioral traits beyond annotations: it pulls specific financial data from SEC EDGAR/XBRL for companies, handoffs off-calendar fiscal years, and drug data from FAERS/FDA. It also states results are sorted by primary metric and returns citation URIs. No contradiction with annotations (readOnlyHint, etc.).

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 use-case phrases and gets to the point. It is moderately lengthy but every sentence adds value. It uses clear formatting with line breaks. Minor room for trimming, but overall efficient.

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

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

Given the complexity (two data sources, no output schema), the description covers return values (paired data + citation URIs) and mentions sorting. It could be slightly more explicit about the response format, but it is sufficient for an agent to understand the tool's output.

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

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

Schema coverage is 100% with descriptions, but the description adds substantial meaning: for 'type', it explains what each enum value retrieves (company vs drug data). For 'values', it gives concrete examples (tickers/CIKs and drug names) and clarifies constraints (2-5 items). This goes beyond the schema.

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

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

The description clearly states it performs side-by-side comparison of 2-5 companies or drugs, with specific verb phrases like 'Compare X and Y'. It distinguishes itself from sequential single-lookups, which is a key differentiator from sibling tools like entity_profile.

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

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

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing strong usage guidance. It also distinguishes between 'company' and 'drug' types. However, it does not explicitly mention when not to use, e.g., for a single entity or if more than 5 entities, but the context clearly implies those cases.

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. The description adds that it returns top-N tools with full schemas and curated examples, and that no second schema lookup is needed. This provides additional behavioral context beyond annotations.

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

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

The description is a single dense paragraph that front-loads the purpose. Every sentence adds value (domains, return format, call-first advice). It is slightly long for a very simple tool, but the density of information justifies 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 no output schema, the description sufficiently describes the return value (top-N relevant tools with names, descriptions, full input schemas). It covers when to use, what the tool does, and the output format, making it complete for agent decision-making.

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

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

Schema description coverage is 100%, so the schema fully documents all parameters. The description mentions aliases (task, q, description, search) and gives examples, but adds only marginal value beyond the schema definitions. Baseline 3 is appropriate.

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

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

The description starts with 'Find tools by describing the data or task' (specific verb+resource) and lists many concrete domains (SEC filings, financials, FDA drugs, etc.). It clearly differentiates from siblings by being a discovery tool that returns the option set, whereas siblings are targeted 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 says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use guidance and implies when not to use it (if you already know the tool).

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

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

The description discloses key behavioral traits beyond annotations: it returns specific data fields (cik, company_name, recent_filings, fundamentals, patents, news, LEI), notes that patents may soft-fail due to API sunset, and states that names are not supported. This adds significant context to the read-only, idempotent behavior hinted by annotations.

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

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

The description is somewhat lengthy but every sentence provides useful information. It front-loads the purpose and includes examples. While it could be more structured (e.g., bullet points), it remains clear and efficient for an AI agent.

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

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

Given the tool's complexity (multiple data sources, fallback mechanisms, input constraints), the description is remarkably complete. It covers all return fields, notes about soft-failing patents, and addresses input limitations. No output schema exists, but the description adequately describes what the tool returns.

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

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

Schema coverage is 100%, so the schema fully documents the parameters. The description adds practical context: type must be 'company', value is ticker or CIK, and explicitly states that names are not supported. This slightly exceeds the baseline 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 clearly states the tool's purpose: generating a full cross-source profile of a US public company in one parallel call. It lists example queries and explicitly distinguishes itself from chaining single-pack lookups, making its purpose unambiguous.

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

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

The description provides explicit guidance on when to use this tool (when user asks for a holistic view) and when to avoid it (if only a name is provided, directing to resolve_entity first). It also mentions the preferred scenario over alternative approaches.

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

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

Description adds context about clearing sensitive data beyond annotations which already indicate destructive and idempotent nature. No contradiction.

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

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

Two sentences, no wasted words, front-loaded with primary action.

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

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

Complete for a simple deletion tool: one required parameter, annotations present, sibling context provided. No output schema needed.

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

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

Schema coverage is 100% for the single parameter 'key' with description 'Memory key to delete'. Description does not add additional meaning beyond schema.

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

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

Description clearly states 'Delete a previously stored memory by key' with a specific verb and resource. Distinguishes from sibling tools 'remember' and 'recall' by nature of deletion.

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 conditions for use: stale context, task completion, or clearing sensitive data. Mentions pairing with remember and recall.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds behavioral details: fetches page, extracts title/description/key links, emits standard markdown. No contradictions.

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

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

Two well-structured sentences, front-loaded with action and purpose, every word earns its place. No superfluous text.

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

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

No output schema but description explains output format ('single text blob ready to drop at site-root/llms.txt'). Combined with annotations covering safety, this is complete for a simple data-generation tool with 2 params.

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 descriptions are already clear. Description restates default/max for max_links but adds no additional meaning beyond what 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?

Description states specific verb 'generate' and resource 'llms.txt file for any URL', clearly differentiating from siblings like ai_visibility_check or scan_competitor_ai_presence which have different purposes.

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

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

Explicitly lists three use cases (indexing client site, drafting own, auditing competitor), providing clear context for when to use. Missing explicit when-not to use but sufficient for most agents.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false. Description adds that it returns specific fields and mentions the include_inactive parameter indirectly by stating 'active subscriptions,' but does not elaborate on behavior 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 that front-load the main action and key 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?

Tool has one optional parameter and no output schema. Description sufficiently covers the return fields and usage context, making it complete for an AI agent to use effectively.

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

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

Schema coverage is 100% with a clear description of the include_inactive parameter. Description mentions 'active subscriptions' but does not add extra semantics 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?

Description clearly states it lists the caller's active subscriptions and enumerates the return fields (id, type, params, created_at, last_fired_at, fire_count), leaving no ambiguity about tool purpose.

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

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

Provides explicit guidance on when to use: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This is helpful context, though it does not explicitly contrast with sibling tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that the tool returns a 'timeseries' of daily data, which is useful but does not disclose pagination, date range handling, or other 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 very concise at two words, but it sacrifices important information. It is front-loaded but not sufficiently informative for correct tool invocation, earning a middle score.

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 parameter count of 2 and the existence of an output schema, the description should at least mention the required 'package' parameter or the optional 'mirrors' flag. It lacks essential context for an agent to use the tool correctly without relying on sibling tool assumptions.

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

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

Schema description coverage is 0%, so the description must compensate. However, the description is only two words and does not explain the meaning of the 'package' (required) or 'mirrors' (optional) parameters, offering no semantic help beyond the schema itself.

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 'Daily downloads timeseries.' indicates the tool provides a time series of daily download counts, but it does not specify the resource (e.g., a Python package) or the action explicitly. It is not a tautology but remains vague and does not distinguish from siblings like 'python_major' or 'recent_changes'.

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

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

No guidance is provided on when to use this tool compared to sibling tools. There is no mention of prerequisites, specific use cases, or alternative tools, leaving the agent to infer context from the name and schema alone.

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

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

Annotations are minimal (readOnlyHint false, etc.), but the description adds important behavioral traits: rate-limited to 5 per identifier per day, free (no quota impact), and that team reads digests daily affecting roadmap. Also explains the expected message format and context usage. 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 4-5 sentences, front-loaded with purpose, then usage guidelines, then behavioral details. Every sentence adds distinct value with no fluff. Well-structured and easy to parse.

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

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

Given the tool's simplicity (3 params, no output schema), the description covers all necessary aspects: purpose, usage, behavioral constraints, rate limits, and recommendations for message content. No gaps remain for agent decision-making.

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

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

Schema description coverage is 100% (all params documented in schema), baseline 3. The description adds extra value by explaining the context objects optional fields, the enum values for 'type' with their meanings, and message length constraints (1-2 sentences, 2000 chars). This goes beyond schema, justifying a 4.

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

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

The description clearly states the tool's purpose with specific verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It also lists the concrete types (bug, feature, data_gap, praise), making it distinct from siblings which are all non-feedback tools.

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

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

Explicitly states when to use each type (e.g., 'Use when a tool returns wrong/stale data (bug)') and what not to do ('don't paste the end-user's prompt'). Provides clear guidance on when to use this tool versus alternatives (none needed since it's unique).

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: data source (CF analytics engine), privacy (no PII), caching (5min-1h), and the aggregated nature. 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 well-structured and front-loaded, starting with the core purpose. Each sentence adds value, covering returns, use cases, data source, and caching without unnecessary 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?

For a simple tool with one parameter, rich annotations, and no output schema, the description is complete. It explains what is returned, caching, privacy, and use cases, leaving no gaps for an agent to understand when and how to use it.

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

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

Schema coverage is 100% and the schema description already explains the 'window' parameter. The tool description reinforces the trade-off between short and long windows, adding contextual 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 top tools, packs, and call volume from other AI agents on Pipeworx. It distinguishes itself from sibling tools like 'discover_tools' by focusing on trending data and provides three explicit use cases that clarify its role.

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 three specific use cases for when to use the tool, such as discovering hot data sources or confirming a canonical tool. However, it does not explicitly state when not to use it or name alternative tools, though the context implies its role for trend analysis.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. Description elaborates on algorithms, thresholds (3pp deviation, 0.30 Jaccard, 20% placeholder), and response structure, with no contradictions.

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

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

Well-structured with clear sections and front-loaded constraints, but slightly lengthy; still 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?

No output schema, but description details response fields and constraints comprehensively. Covers error cases and mode behaviors fully.

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

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

Schema coverage is 100%. Description adds example slugs, URL acceptance, and detailed mode semantics that significantly enrich the schema.

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

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

The description clearly states 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks' and distinguishes two modes (event and topic), which differentiates it from sibling tools like polymarket_edges.

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

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

Explicitly states that one of `event` or `topic` is required and explains when to use each mode, including recommendations and cross-event advantages.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint true, destructiveHint false. The description adds extensive behavioral context: three segments' inner workings, response structure, caching policy, and warnings about Fed bets. 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 thorough but verbose, with technical details that could be better organized (e.g., bullet points). It front-loads the main purpose but becomes dense. Adequate for such a complex tool, but not concise.

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

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

Given the complexity (9 parameters, no output schema), the description covers the three segments, response structure, diagnostics, caching, and parameter knobs. It explains return values and edge cases (Fed bets) well, though could be more explicit on exact fields.

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

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

Schema coverage is 100% with all parameters described. The description provides high-level context (e.g., 'tradeable-edge filters') but does not add new meaning beyond the schema's descriptions. 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 scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, with a specific purpose 'what should I bet on today'. It distinguishes from sibling tools like polymarket_arbitrage by focusing on Pipeworx data disagreement and providing three segments.

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 the tool is built for discovering opportunities without paging hundreds of markets and provides tradeable-edge knobs for filtering. It implies use cases but lacks explicit when-not-to-use or alternatives beyond mentioning 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 readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds extensive behavioral details: safety fields, compatibility_warning conditions, temporal alignment, and skipped types. 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?

Relatively concise for the complexity, with clear section headers (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence adds value, though slightly lengthy.

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

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

Given no output schema and moderate parameter count, the description fully compensates by detailing response structure, safety fields, and edge cases. It is complete and informative.

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

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

Schema coverage is 100% with all parameters described. The description adds value by explaining the topic list, the override mechanism, and provides examples, going beyond the schema.

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

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

The description clearly states it is a 'Cross-venue spread between Kalshi and Polymarket for the same resolving question', with specific verb+resource. It distinguishes from sibling tools like polymarket_arbitrage by focusing on cross-venue spreads.

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

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

The description explains two modes (topic shortcuts and explicit tickers) and provides context on when results are meaningful via safety fields. It warns that pre-mapped topics often return compatibility warnings, but does not explicitly compare to siblings.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, but the description adds no behavioral context. It does not mention any side effects, authorization needs, or rate limits, leaving the agent without additional insights.

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

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

While extremely short, the description is underspecified rather than concise. It lacks front-loaded, actionable information and does not earn its place as a useful instruction.

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

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

Given the presence of an output schema but no description of return values or behavior, the tool is incomplete. It fails to explain what 'By Python major version' means in terms of input or output, leaving significant gaps for the agent.

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

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

The schema has 0% description coverage, and the description does not mention the single parameter 'package' at all. It provides no meaning beyond the raw schema, failing to compensate for the lack of parameter documentation.

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 'By Python major version.' is extremely vague. It does not specify what action the tool performs (e.g., 'Get', 'List', 'Filter') or the resource it operates on. It lacks a clear verb and resource, making its purpose ambiguous.

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

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

No guidance is provided on when to use this tool versus its sibling tools, such as 'python_minor'. There is no mention of context, prerequisites, or 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?

Description adds no behavioral details beyond annotations. Annotations indicate readOnly, idempotent, not destructive, but description does not explain non-obvious traits like what side effects or scope 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?

Extremely short, but not effectively concise—it sacrifices clarity. A single sentence is acceptable if informative, but here it is a fragment that fails to define the tool.

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

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

Given output schema exists, the description need not explain return values, but it fails to set any context about what the tool returns or how to interpret results. Incomplete for effective use.

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

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

Schema coverage is 0%, and description does not elaborate on the 'package' parameter. No examples, types, or constraints beyond the schema's bare minimum.

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 'By Python minor version.' is vague and lacks a verb or clear action. It does not state what the tool does (e.g., list, filter, compare). Given sibling 'python_major', one can infer it relates to Python minor versions, but the purpose is unclear.

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

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

No guidance on when to use this tool versus siblings like 'python_major'. No context about prerequisites or typical use cases.

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, so the agent knows it's safe. The description adds context about scoping to identifier and the dual behavior with/without key, but does not introduce substantial 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 two sentences long, front-loaded with the primary purpose, and contains no redundant information. Every sentence contributes meaning.

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

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

For a simple tool with one optional parameter and no output schema, the description fully explains usage, scoping, and relationship to siblings. It leaves 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?

With 100% schema coverage, the description adds value by explicitly explaining the effect of omitting the key (list all keys) vs. providing it (retrieve value). This clarifies the parameter's behavior beyond the schema's description.

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

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

The description uses specific verb 'retrieve' and clearly identifies the resource (values saved via remember). It distinguishes from siblings (remember, forget) and explains the two modes (get value or list all keys).

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 for when to use the tool: to look up previously stored context without re-deriving. It gives examples (ticker, address, notes) and mentions scoping. However, it does not explicitly state when not to use or list 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 and destructiveHint, so behavior is safe. The description adds no extra behavioral context beyond 'Total downloads'. 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?

Very concise at one sentence, but it is under-specified. Every word is used, but lacks necessary detail.

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?

Tool has output schema but description fails to clarify what 'recent period' means or how inputs map to outputs. Inadequate for a 2-parameter tool with 50% schema coverage.

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 50% (only 'period' has a description). The tool description does not add meaning for 'package' or clarify how parameters relate to the tool's function.

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

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

The description specifies a resource ('downloads') and a scope ('recent period') but lacks a clear verb. It vaguely states what the tool does but is not as specific as it could be, especially compared to sibling tools like 'recent_changes'.

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

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

No guidance on when to use this tool versus alternatives. The description does not provide context for usage or exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavior like returning specific fields, how mark_read affects subsequent calls, and the feed availability via REST. 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?

Concise at 4 sentences. Front-loaded with the main action, then details on returned fields, filtering, mark_read flag, and a note on alternative access. Every sentence serves a purpose with no redundancy.

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

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

Covers key aspects: what is returned, filtering options, mark_read behavior, and alternative access. Missing explicit structure of the return object, but the description mentions core fields (source, citation_uri, raw event payload). With no output schema, it is fairly 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 5 parameters are described in the schema, and the description adds further meaning: gives an example for type ('sec_8k'), explains 'since' as ISO timestamp, and clarifies that mark_read flags events as read. This goes 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 defines the tool's purpose: 'Pull fired events from your subscription feed.' It specifies what is returned (recent alerts with source, citation_uri, raw payload) and distinguishes from siblings like 'list_subscriptions' by focusing on event feed 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?

Provides useful usage guidance: mentions filtering by type and since, explains mark_read behavior, and notes that polling works fine. Also points to an alternative REST endpoint for scripts/dashboards. However, it does not explicitly state when not to use this tool versus sibling tools.

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

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

Discloses multi-source fan-out, fallback from GDELT to GNews, USPTO soft-failure after May 2025, and return structure. Annotations already declare safe read-only/idempotent, but description adds valuable operational details.

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?

Front-loaded with common queries, then efficient breakdown of sources, parameters, and alternatives. Every sentence adds value; no fluff.

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

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

Given three required params, no output schema, and rich annotations, the description fully covers behavior (data sources, failover, return structure) and provides a clear alternative. Complete for the tool's complexity.

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

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

Input schema has 100% coverage, but description adds context: 'type' is only 'company', 'since' examples include relative shorthand like '30d', 'value' accepts ticker or CIK. Enhances understanding beyond schema descriptions.

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

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

Description clearly states it's a change feed for companies across multiple sources (SEC, news, patents), with user-friendly examples like 'What's new with X' and explicit differentiation from entity_profile.

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

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

Explicitly advises using entity_profile for static profiles, providing a when-not-to-use alternative. Does not compare against other sibling tools but the guidance is clear for the primary alternative.

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

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

Discloses scoping by identifier, persistent for authenticated users, 24-hour TTL for anonymous sessions—beyond annotations which already show idempotentHint=true and destructiveHint=false. No contradiction.

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

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

Four sentences front-loaded with main purpose, then usage context, then behavioral 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?

Covers purpose, usage, persistence, and links to siblings. Lacks mention of return value but output schema is absent; acceptable for a simple key-value store.

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

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

Schema covers both parameters with descriptions. Description adds naming conventions (e.g., 'subject_property') and clarifies value as any text, enhancing meaning beyond schema.

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

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

Description clearly states the tool saves data for later reuse, with specific verb 'save' and resource 'key-value pair'. Distinguishes from siblings 'recall' and 'forget' by being the store 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 says 'Use when you discover something worth carrying forward' and mentions pairing with recall/forget. Lacks explicit when-not-to-use but provides strong contextual guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral context: it returns specific fields for each type (ticker, CIK, RxCUI), includes citation URIs, auto-disambiguates company names, and cascades through multiple lookup endpoints. 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 comprehensive but slightly verbose. It uses examples and a semi-structured format that is easy to parse. Every sentence adds value, but a few phrases (e.g., 'each call cascades through several lookup endpoints internally') could be trimmed without losing clarity. Still 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 has two distinct entity types and no output schema, the description provides complete context: it enumerates return fields for each type, mentions auto-disambiguation, and notes the use of citation URIs. No gaps in understanding what the tool does or returns.

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

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

Schema coverage is 100% with parameter descriptions, but the description adds significant meaning: it explains what each combination of type and value returns (e.g., for company: ticker + 10-digit CIK + company_name + citation URI; for drug: RxCUI + ingredient + brand + citation). It also clarifies the input format (ticker, CIK, or company name; brand or generic name).

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

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

The description clearly states the tool resolves user-spoken names to canonical identifiers, with specific examples ('ticker for...', 'CIK for...', 'RxCUI for...') and distinguishes the action from siblings by emphasizing it as the first step when an ID is needed. It specifies the verb 'resolve' and the resource 'entity names to official identifiers'.

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

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

The description provides explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also notes that the tool replaces 2-3 manual lookups, indicating efficiency. However, it does not explicitly state when not to use it or mention alternative tools for different contexts.

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

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

Description adds significant behavioral context beyond annotations: it internally calls 'ai_visibility_check', ranks results, and surfaces most/least recognized. It also details output structure (score, confidence, signal density). No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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

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

Three sentences: first front-loads the core action, second explains mechanism, third gives use case and expected output. No extraneous text. 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?

Despite no output schema, the description sufficiently describes return values (ranked list with score, confidence, signal density). For a tool with 4 parameters and moderate complexity, the description covers all necessary aspects including internal tool call and use case.

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

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

Schema coverage is 100% (baseline 3). Description adds value by clarifying that the first element in 'entities' is treated as 'subject' for narrative, and explains conditional use of '_apiKey' when 'models' includes 'anthropic'. This enriches parameter understanding.

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

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

The description uses specific verbs ('compare', 'probe', 'rank', 'surface') and clearly identifies the resource ('AI visibility across multiple entities'). It distinguishes itself from sibling tool 'ai_visibility_check' by emphasizing side-by-side comparison versus single entity probe.

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

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

Explicitly states use case ('competitive AI-marketing audits') with a concrete example. Implies that for a single entity check, 'ai_visibility_check' should be used. Does not explicitly list 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context: bundlephobia's first call can take 5-30s and partial failures degrade gracefully with sources_failed listed. No contradictions with annotations.

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

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

The description is front-loaded with the primary purpose, then systematically covers sources, usage, return structure, and edge cases. Every sentence provides distinct value without redundancy, achieving conciseness for the complexity of the tool.

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

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

Given the absence of an output schema, the description thoroughly lists return fields (summary block with 8 fields, per-advisory detail, links, alternative versions) and explains graceful degradation. No gaps for the tool's functionality and behavior.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add new parameter-level information beyond what the schema already provides (package name, scoped packages, version optional with default). Context is integrated but not additional semantic 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 starts with a clear, specific purpose: 'Composite should I add this npm package to my project check in ONE call' and details the sources consulted (deps.dev, bundlephobia). It distinguishes from sibling tools by noting the NPM-only scope and referencing alternative usage for other ecosystems via deps.dev:version directly.

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 on when to use: 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me.' Also clarifies limitations ('NPM ecosystem only in v1') and points to alternatives for PyPI/Maven/Cargo/Go via deps.dev:version directly.

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, and non-destructive. The description adds significant behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag. No contradiction with annotations.

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

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

The description is a single paragraph with clear sentences and front-loaded main idea. It is dense but every sentence adds value. Minor room for improvement in brevity.

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 explains the return values (top-N passages with offsets and similarity scores). It covers truncation behavior and pairing with another tool, making it comprehensive for a search tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context about how the search works but does not provide new parameter-specific information beyond what the schema already conveys. It reinforces the purpose but doesn't add semantic depth to the parameters.

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

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

The description clearly states the tool performs semantic search inside a fetched record, using specific examples like SEC 10-K and articles. It distinguishes from siblings by emphasizing its role in reducing context usage 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?

The description explicitly advises using this tool when the record is too large for the prompt, and mentions pairing with ask_pipeworx_grounded. However, it does not explicitly state when not to use it or alternative tools for other 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?

Beyond annotations (idempotentHint=true, destructiveHint=false, readOnlyHint=false), the description adds crucial behavioral details: requirement for a Pipeworx OAuth account, phone verification, SMS cap (10/day), and warnings about patent_grant corporate form matching. These significantly aid the agent in understanding side effects and constraints.

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

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

The description is reasonably concise for the amount of information conveyed. It front-loads the core purpose and return, then elaborates on types and delivery. While some parts (like type details) could be formatted as bullet points, the current prose is functional and not overly verbose.

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

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

For a creation tool with no output schema, the description specifies the return value ('Returns the new subscription id'). It covers all key aspects: prerequisites, allowed values, delivery channels, and limitations. Given the complexity (multiple types, nested params), it provides sufficient completeness for an agent to invoke correctly.

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

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

The input schema has 100% description coverage, providing basic meaning for each parameter. The description adds valuable context with concrete examples for each type (e.g., items, topic, series_id, applicant) and explanations of edge cases (e.g., patent_grant approximate match). This enhances understanding beyond 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 starts with 'Create a proactive monitoring subscription to a live-data event stream', which specifies the verb 'create' and the resource 'subscription'. It distinguishes from siblings like list_subscriptions and unsubscribe by focusing on creation. The return value (subscription id) is also stated, making purpose very clear.

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

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

The description provides strong usage guidance by listing supported subscription types with examples and delivery options. It implies when to use (proactive monitoring) but does not explicitly contrast with sibling tools like recent_alerts or ask_pipeworx for alternative use cases. However, the context of subscription management is clear, so it scores 4.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) provide a safety profile, but the description adds no behavioral context. It does not disclose what actions the tool performs, what data it accesses, or any constraints 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 extremely short, but conciseness is not beneficial when it sacrifices clarity. It fails to convey the tool's purpose efficiently, making it essentially useless.

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

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

Despite having an output schema, the description lacks any context about the tool's function. It is incomplete for even basic understanding, leaving the agent unable to determine when or how to use this tool.

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

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

Schema description coverage is 0%, and the description offers no explanation of the 'package' parameter. The example uses 'pytest', but without documentation, the agent cannot understand the parameter's meaning or format.

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

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

The description 'By OS/system.' is extremely vague and does not specify what the tool does. It borders on tautology, restating the name without adding any action or resource. Sibling tools have clear purposes (e.g., python_major, validate_claim), making this tool's purpose ambiguous.

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

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

No guidance is provided on when to use this tool or when to avoid it. There is no mention of alternatives or context, leaving the agent with no basis for selection from 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?

Adds value beyond annotations: describes ownership enforcement, deactivation vs deletion, and that historical events remain. Annotations (destructiveHint=false, idempotentHint=true) are consistent and description enriches them.

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

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

Two sentences, front-loaded with action, then constraints, then behavioral details. No wasted words.

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

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

Given single parameter, no output schema, and good annotations, the description fully covers purpose, usage constraints, and behavioral outcome.

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

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

Schema coverage is 100% with description for 'id' already present. Description does not add additional meaning beyond what schema provides, so 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?

Description clearly states 'Cancel a subscription by id' which is a specific verb+resource. It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by focusing on cancellation and ownership.

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

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

Explicitly mentions ownership enforcement ('you can only cancel your own subscriptions') and that the row is deactivated not deleted, giving clear context. Does not explicitly state when not to use, but the guidance is strong.

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

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

The annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context about the underlying data source (SEC EDGAR + XBRL), the possible verdicts, and the efficiency gain over multiple sequential calls, enhancing transparency 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 moderately concise; each sentence adds value. It starts with example queries to signal intent, then describes the tool's function and return format. Slightly longer than necessary but 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?

Despite lacking an output schema, the description fully specifies the return values (verdict types, citation, delta) and explains the data source. For a single-parameter tool, this is complete and provides sufficient context for the agent.

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

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

Schema coverage is 100%, so the baseline is 3. The description repeats the parameter's purpose and adds natural-language examples, which is helpful but not essential since the schema already describes the 'claim' parameter clearly.

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

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

The description clearly defines the tool as a natural-language claim verifier for company-financial claims, with specific examples of trigger phrases. It distinguishes itself from sibling tools like compare_entities or bet_research by focusing on fact-checking against authoritative sources.

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

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

The description explicitly states when to use the tool: whenever the agent needs to check factual correctness. It specifies the domain (company-financial claims) and provides example queries, but does not explicitly mention when NOT to use it or suggest alternatives.

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