Pubchem

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false. The description adds details about output format ({score, confidence, signals, raw_response}) and the BYO key arrangement for Anthropic, which is beyond annotations.

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

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

Four sentences, each serving a distinct purpose: action, usage/parameters, return format, use cases. No fluff, well-organized.

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

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

Given the moderate complexity and no output schema, the description covers all essentials: purpose, parameters, output, and use cases. No gaps identified.

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: default model, condition for using _apiKey, and purpose of context parameter. This goes beyond schema descriptions.

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

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

The description clearly states the verb 'probe', resource 'LLMs', and scope 'visibility (0-100) per model'. It explicitly lists what can be probed (business, brand, product, topic), distinguishing it from siblings like 'scan_competitor_ai_presence' which may have different focus.

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 default model and when to pass _apiKey for Anthropic, and lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). However, it does not explicitly contrast with sibling tools or state when not to use.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds that it routes to 3,756 tools, fills arguments, and returns structured citations. 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?

Description is front-loaded with key instruction ('PREFER OVER WEB SEARCH') and provides examples and trigger phrases. Though lengthy, each sentence adds value.

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

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

For a single-parameter tool with good annotations, the description covers purpose, usage, and output format (citation URIs). Adequate overall.

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 required parameter 'question' with multiple aliases. Description includes examples but does not add semantic detail 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?

Clearly states the tool is for answering factual questions with authoritative structured data, listing many domains and examples. Distinguishes from web search but not from sibling tools like 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 says to prefer over web search and provides trigger phrases ('what is', 'look up', etc.). Gives examples. Does not compare to 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds details about refusal mechanism, return format, and extra LLM cost. 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?

Description is appropriately sized and front-loaded with key information. Every sentence adds value, though slightly verbose but justified by complexity.

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

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

Given complexity and lack of output schema, description fully explains return shape, refusal reasons, and cost. Complete enough for an agent to use correctly.

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

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

Schema description coverage is 100%, so baseline is 3. Description does not add significant meaning beyond schema for parameters; focuses on behavior instead.

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 is a 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes itself from ask_pipeworx. It specifies the tool extracts answers using only tool results, providing a clear verb and 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?

Explicitly tells when to use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when to prefer ask_pipeworx ('casual lookups'). Provides clear context and 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?

Annotations (readOnlyHint, idempotentHint, destructiveHint) provide a safety baseline. The description adds extensive behavioral context: fan-out patterns, response shapes, resolver contract, safety mechanisms (low-confidence blocking, closed market handling, wide-spread warnings), resolution-rule risk, and cancellation rule parsing. 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 clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). It front-loads the core purpose and gradually provides details. While some details could be condensed, the complexity justifies the length.

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

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

Given no output schema, the description thoroughly documents the response structure (result.market, result.analysis, result.evidence, resolver contract, parent_event extractor, news fields, safety mechanisms). It covers all necessary context for agent usage, including edge cases and error conditions.

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 (market, depth, include_raw) are described in the schema (100% coverage). The description adds value by explaining the depth enum ('quick' vs 'thorough'), what include_raw controls (response size), and gives examples for market input. It goes beyond the schema to clarify real usage.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and output (evidence packet + comparison). It distinguishes from sibling tools by focusing on bet-specific research, and no sibling directly overlaps.

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

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

Explicit use cases are given: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also details blocking conditions (low confidence, closed markets, wide spreads) and when to check fields. However, it does not explicitly contrast with alternative tools like ask_pipeworx or deep_research.

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 context beyond annotations: details data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles off-calendar fiscal years, sorts by primary metric, and mentions return format with 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 thorough but slightly lengthy. It front-loads example queries and usage preference, which is effective. Every sentence adds value, though some minor repetition could be trimmed. Still, it is well-structured for agent understanding.

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

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

Despite no output schema, the description explains return values (paired data + citation URIs). It covers all necessary aspects: entity types, data sources, sorting, and performance notice. Complete enough for agent to use 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?

Input schema covers both parameters with descriptions, but the description adds practical examples (tickers, drug names), constraints (min/max items), and explains how each type is interpreted. Schema coverage is 100%, so baseline is 3; but description adds meaningful extra guidance, earning 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 function: side-by-side comparison of 2-5 companies or drugs in one call. It uses specific verbs like 'compare', 'rank', and provides example queries. It distinguishes itself from sequential 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?

Explicitly states when to use ('ALWAYS PREFER over sequential single-pack lookups') and provides context for different entity types (company vs drug) and what data they retrieve. No alternative tools mentioned, but the guidance is clear and actionable.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds significant behavioral details: parallel decomposition, pre-verified facts with citations, explicit gaps for unanswered facets, expected 15-60s runtime, and the fact that it never invents answers.

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 provides all necessary information. While it includes some numeric details (3,756 tools, 887 sources) that add flavor, every sentence contributes to understanding the tool's behavior, usage, and output.

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

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

Given no output schema, the description thoroughly explains return format: structured findings packet with verbatim evidence, confidence, source, fetched_at, citation, and gaps. It also covers authentication, timing, and parallelism. Comprehensive for a complex research 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 parameters with descriptions. The description adds value by explaining the depth levels (quick=3, standard=5, thorough=8) and linking 'thorough' to paid plans, plus clarifying that the question should be in 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 clearly states the tool performs grounded multi-source research in a single call, decomposing questions into sub-questions and extracting evidence. It distinguishes itself from sibling 'ask_pipeworx' by specifying use for broad/multi-part questions vs single lookups.

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

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

Explicitly provides when to use (broad/multi-part questions) and when not to (use ask_pipeworx for single lookups). Also includes prerequisites (Pipeworx account, paid plan for 'thorough' depth) and examples.

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 valuable behavioral context: it 'Returns the 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 goes beyond annotations to explain what the output contains and how it can be used.

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 about four sentences. It front-loads the purpose clearly and lists many domains for context. While the list of domains is somewhat lengthy, it is helpful for clarity and does not make the description excessively long. The structure is logical and efficient.

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

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

Given that the tool has no output schema, the description adequately explains what is returned: 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples).' It also covers the tool's role as a discovery tool, making it complete enough for an agent to understand its function and 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 description coverage is 100%, so the baseline is 3. The description mentions that 'query' accepts aliases and gives examples in the text, but the schema already documents all parameters thoroughly with descriptions and aliases. The description does not add significant new 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 has a specific verb ('discover', 'browse', 'search', 'look up') and resource ('tools'), and explicitly lists domains like SEC filings, FDA drugs, etc. It distinguishes itself from siblings by being a meta-tool that helps navigate many other tools, as indicated by 'Call this FIRST when you have many tools available.'

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

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

The description explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for:' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use context, though it does not mention when not to use or provide 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, idempotentHint, openWorldHint, and destructiveHint false. Description adds valuable context: fans out across multiple sources, patents API soft-fails after May 2025, and returns specific fields. 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?

Description is well-structured with examples first, then purpose, usage guidance, and details. Every sentence adds value, though slightly verbose. Could be trimmed but remains effective.

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

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

Given the tool's complexity (multiple sources, no output schema, 2 params), the description covers all key aspects: what data is returned (filings, fundamentals, patents, news, LEI), how to call (ticker or CIK), and caveats (patents sunset, names not supported).

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% (baseline 3). Description adds beyond schema by explaining type is 'company' only, value can be ticker or CIK (not names), and provides examples like 'AAPL' and '0000320193'.

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

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

The description clearly states the tool provides a 'full cross-source profile of a US public company in ONE parallel call', listing example queries and specific data returned (cik, filings, fundamentals, patents, news, LEI). It distinguishes from sibling tools like resolve_entity and chaining single-source 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?

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' when holistic view is needed, and warns that names are not supported, directing users to use resolve_entity first.

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 confirms destructive behavior (delete) matching destructiveHint=true. Adds context about clearing sensitive data and pairing with related tools, 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 the main action, no wasted words.

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

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

For a simple one-parameter tool with no output schema, the description fully covers purpose, usage, and relationships to sibling tools.

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

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

Schema has 100% coverage with description for 'key'. Description does not add additional meaning beyond the schema, so baseline 3 is appropriate.

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

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

Description explicitly states 'Delete a previously stored memory by key', using a specific verb and resource. It distinguishes from siblings like 'remember' and 'recall'.

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

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

Provides clear when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. Mentions paired tools '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?

Description discloses key behaviors: fetches the page, extracts title/description/key links, and emits markdown format. Annotations already declare safe, non-destructive, idempotent traits. The description adds useful behavioral context beyond annotations, though it could detail error handling or rate limits.

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

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

Two concise sentences: first states the action and benefit, second lists use cases. No unnecessary words, front-loaded with key information. 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 the tool's simplicity (2 parameters, no output schema, rich annotations), the description fully covers purpose, behavior, output format, and use cases. No gaps for agent decision-making.

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

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

Schema coverage is 100%, with both parameters described in the schema. The description restates the parameter meanings without adding new semantics. Baseline of 3 is appropriate as the schema does the heavy lifting.

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

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

Description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb (generate), resource (llms.txt), and purpose (AI crawler indexing). It distinguishes from sibling tools like scan_competitor_ai_presence by focusing on file generation rather than scanning or visibility checks.

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

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

Description lists three explicit use cases: getting a client's site indexed, drafting for own project, auditing competitor indexing. It provides clear context but does not mention when not to use or alternatives, which would elevate to 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, and destructiveHint false. The description adds behavioral context by stating the output is a 'tree' with 'tags', and that it covers pharmacological classification. No contradictions. The description complements the annotations well.

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 exceptionally concise: two sentences. The first sentence defines the output, the second gives a practical use case. Every word is meaningful, no redundancy. Perfectly front-loaded.

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

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

The tool has only one parameter, full schema description coverage, and an output schema (likely well-defined). The description explains the purpose and usage, while annotations cover safety. No gaps remain; the description 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?

The input schema has one parameter (cid) with description 'PubChem Compound ID'. Schema description coverage is 100%, so the description adds no additional parameter information beyond stating 'for a CID'. Baseline 3 is appropriate; the description does not need to elaborate further.

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: returning a pharmacological classification tree for a CID, including drug class, mechanism of action, and biological role tags. It also provides a concrete use case ('what kind of drug is X'), making it easy to understand and distinguish from sibling tools like get_compound or get_synonyms.

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 it is useful for 'what kind of drug is X' questions, giving clear context for when to use. It does not explicitly mention when not to use or provide alternatives, but the sibling list implies that if you need more comprehensive compound data, you would use get_compound. The guidance is strong but not exhaustive.

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 description need not repeat these. The description adds value by listing all returned properties, giving the agent a clear picture of what to expect beyond a simple 'get properties' statement.

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

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

The description is a single sentence that lists many properties, which is information-dense but still readable. It could be slightly more structured (e.g., bullet points) but is not overly long. Front-loaded with the main action.

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

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

Given the output schema exists, the description need not detail return values. It covers the input (CID) and the scope of output properties. For a simple tool with one parameter, this is complete. The list of properties is thorough.

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

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

Schema has 100% coverage with a description for the only parameter (cid). The description does not add additional semantics about the parameter beyond listing it, but the schema is already sufficient. Baseline of 3 is appropriate.

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

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

The description clearly states the tool retrieves standard properties for a PubChem CID, listing specific properties (molecular formula, weight, IUPAC name, etc.). It uses a specific verb ('Get') and resource ('properties for a PubChem CID'), differentiating it from siblings that cover other domains like predictions or 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?

No explicit instructions on when to use or avoid this tool, nor alternatives. The usage is implied: provide a CID to get properties. Since there are no sibling tools with overlapping functionality, the lack of exclusions is acceptable but still leaves room for improvement.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds context about the types of synonyms included but does not disclose behavioral aspects like pagination, rate limits, or error handling. With annotations present, the description adds moderate 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 a single, efficient sentence with no wasted words. It is front-loaded with the verb 'List', making the purpose immediately clear.

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

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

Given the tool's simplicity (2 parameters, no nested objects, output schema exists), the description covers the core functionality well. It misses a brief mention of the output format, but overall it is complete enough for an agent to use correctly.

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

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

Schema coverage is 100%, so both parameters are fully documented in the schema. The description does not add new meaning beyond the schema; it mentions 'list all names' but the schema already documents the 'max_results' cap. No extra 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 clearly states the action ('List') and the resource ('all names (synonyms) for a CID') with specific examples ('common names, IUPAC, trade names, CAS numbers'). It distinguishes the tool from siblings like 'get_compound' or 'search_by_name' by focusing on synonyms.

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

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

No explicit guidance on when to use this tool versus alternatives. The description implies its use for listing synonyms but does not state when not to use it or mention sibling tools that could be alternatives for different purposes.

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 context about the scope (caller's subscriptions) and return fields, but does not disclose additional behavioral traits like pagination or rate limits.

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

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

The description is concise, containing only two sentences. The first sentence states the purpose and output, the second provides usage guidance. No unnecessary words.

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

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

Given the simplicity of the tool (one optional parameter, no output schema), the description is reasonably complete. It covers purpose, return fields, and usage context. Minor gaps like pagination are not critical for this tool.

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

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

The input schema has full coverage (100%) with a description for the only parameter 'include_inactive'. The tool description does not add extra meaning beyond the schema, so 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 clearly states the verb 'List', the resource 'the caller's active subscriptions', and specifies the return fields. It is distinct from sibling tools like subscribe or 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 provides explicit guidance on when to use the tool: 'before adding more' subscriptions or 'to find an id to cancel'. It does not explicitly exclude other contexts, but the guidance is clear and practical.

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 (which are neutral), description reveals rate limits (5 per identifier per day), zero cost, and that feedback is reviewed daily. 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?

Three concise sentences that front-load purpose, followed by usage guidance, then constraints. No unnecessary words; 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, the description covers all key aspects: purpose, usage, behavioral constraints, parameter semantics, and rate limits. Complete for a feedback tool.

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

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

Schema coverage is 100%. Description adds meaning: explains the 'type' enum values in detail, clarifies 'context' is optional and its fields, and specifies 'message' length and specificity requirements.

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 distinguishes from siblings by focusing on feedback rather than queries or actions.

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

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

Explicitly instructs when to use (bug, feature, data_gap, praise) and what to avoid (pasting user prompts, describing in terms of tools/packs). Provides clear context for selection among siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint (false). The description adds valuable behavioral context: data source (CF analytics-engine), no PII, caching with TTL (5min-1h), and self-aggregating nature. No contradiction with annotations.

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

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

The description is fairly concise, organized into functional overview, use cases, and metadata. One sentence could be shortened slightly, but overall it's well-structured and front-loaded with the primary function.

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 does not detail exact return format but states it returns top tools, packs, and call volume. It covers caching, data source, and privacy, which is sufficient for a simple read tool with comprehensive annotations.

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

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

The single parameter 'window' has full schema coverage with an enum and description. The description adds meaningful context: short windows surface hot trends, long windows show steady-state demand, which aids parameter selection.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over specified time windows. It uniquely positions itself as an aggregation tool among many siblings, making its purpose distinct.

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 three explicit use cases for when to use this tool (discovering hot data sources, confirming canonical tools, aligning use cases). While it doesn't directly mention alternatives, the context signals and sibling list imply other discovery tools like discover_tools or search_by_name.

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 detailed algorithm behavior including monotonicity checks, partition-sum checks, fill check against live CLOB depth, semantic anchor with Jaccard similarity threshold, and partition filter with placeholder fraction. Annotations already indicate readOnly, openWorld, and idempotent hints, and the description adds substantial context 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 lengthy and contains some redundancy (e.g., partition_check explained twice). However, it is front-loaded with the requirement and purpose, and the structure is logical. Minor improvements could tighten it.

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 explains the response structure (opportunities array, partition_check details, fill_check outcome). It covers edge cases like failing on no args, placeholder filtering, and thin legs. The tool is complex, and the description 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% with descriptions for both parameters. The description adds context beyond the schema, such as accepting full Polymarket URLs for `event`, and explaining that `topic` is a seed question that triggers cross-event search. This adds meaningful guidance.

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 between 'event' (single-event mode) and 'topic' (cross-event mode). It also references a sibling tool, polymarket_fill_risk, for custom sizing.

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 that calling with no args fails. Provides guidance on when to use each mode: `event` recommended for a specific market, `topic` for cross-event scanning. Identifies alternative tool for sizing.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds extensive behavioral details: caching (KV-level 1h), three model families with specific formulas, response structure including diagnostics, and tradeable-edge knobs. This far exceeds what annotations 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 lengthy but well-structured with clear sections (model families, response structure, knobs). It is front-loaded with the core purpose. While verbose, every sentence adds value. A slightly more concise version could exist, but the trade-off for completeness is reasonable.

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 (9 parameters, no output schema), the description thoroughly explains the response structure (by_segment, fed_candidates, _diagnostics), caching, edge calculation, and filter logic. It compensates for the missing output schema by detailing top-level fields, making it complete for a useful 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 baseline is 3. The description adds value by explaining how parameters interact (e.g., min_partition_leg_kelly applies per-leg inside top_legs). It organizes tradeable-edge knobs in a separate section, enhancing understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the resource (Polymarket markets) and the action (finding edges), and distinguishes itself from siblings like polymarket_arbitrage which likely does not use Pipeworx data.

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

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

The description is built for the scenario 'what should I bet on today' and mentions that agents can discover opportunities without paging. It provides usage context by explaining tradeable-edge knobs and excluding Fed bets from ranking. However, it does not explicitly compare to sibling tools or state when not to use it.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds valuable behavioral details: snapshot TTL (60 days), data gaps from cache-misses, decay computed from daily closes not intraday, and output structure (tracked, expired, snapshot_dates). No contradictions.

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

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

The description is dense but well-structured: starts with the core question, details output fields, and ends with limitations. Every section adds value, though it could be slightly more compact without losing clarity.

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

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

Given no output schema, the description thoroughly explains the response structure (tracked, expired, snapshot_dates) and their fields. It also covers limitations like history depth and data gaps, making the tool's behavior fully understandable.

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 days and window with defaults and clamps. The description reinforces defaults and clamps but adds little new meaning 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: edge persistence and decay telemetry from daily polymarket_edges snapshots. It answers a specific question about edge age and distinguishes itself from similar tools like polymarket_edges by focusing on time-series analysis.

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

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

The description provides clear context on when to use (e.g., to understand edge longevity vs. raw edges) and includes a trade insight example. However, it does not explicitly state when not to use or mention alternatives, though the sibling tool list implies differentiation.

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

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it walks the order book ladder, returns specific fields like top_of_book, vwap_fill_price, slippage_pp, and for basket mode includes per-leg fill detail, thin_legs, and forced_directional_risk. It also discloses the dominant loss mode (partial basket fills leading to unhedged positions). 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 clear sections (SINGLE-MARKET, BASKET) and returns fields listed. It is front-loaded with the key purpose and includes warnings. Some redundancy exists (e.g., 'REQUIRES one of market or event' repeated in different forms), but overall it is concise given the tool's complexity.

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

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

Given the tool's complexity (two modes, 4 parameters, no output schema), the description provides complete context. It details return values for each mode, parameter constraints (e.g., size_usd clamp), and risk warnings (e.g., forced_directional_risk, thin_legs). An agent has sufficient information to decide when to use and what to expect.

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

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

Schema description coverage is 100%, but the description adds significant meaning beyond the schema. It clarifies that exactly one of `market` or `event` is required, explains the side parameter default and basket auto behavior, and describes how size_usd is interpreted differently for buys vs sells and in basket mode. This enhances understanding of how parameters interact and their expected values.

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

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

The description clearly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. The verb 'check' is specific and the tool's purpose is distinct from siblings like polymarket_arbitrage and polymarket_edges.

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

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

The description explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', providing clear when-to-use context. It also warns about theoretical overround on thin books not being capturable and the risk of partial basket fills converting an arb into an unhedged directional position, effectively guiding when not to use or what pitfalls to avoid.

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

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

Annotations already mark the tool as read-only, open-world, and idempotent. The description adds significant behavioral context beyond annotations, including safety fields (compatibility_warning, temporal_alignment), skipped counters, and caveats about spread meaninglessness when events are misaligned. This information is critical for correct invocation and interpretation.

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 relatively well-structured, with separate sections for modes, response fields, and safety warnings. While longer than necessary, every sentence adds distinct information. Front-loading the core purpose aids quick understanding.

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

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

Given the tool's complexity (cross-venue comparison with safety checks and no output schema), the description covers all essential aspects: modes, response structure (top_spreads_pp, compatibility_warning, temporal_alignment), and edge cases (skipped comparisons). It compensates for the lack of output schema by describing the response in 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?

The input schema has 100% coverage with descriptions for all three parameters. The description adds value beyond the schema by explaining the two modes (topic vs explicit ticker/slug) and how parameters interact (overrides). This clarifies the mode-switching logic and relationship between 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's purpose: computing cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies the verb (get spread) and resource (cross-venue bets), and distinguishes it from siblings like polymarket_arbitrage by focusing on cross-venue comparison. The description also details two modes and response structure, leaving no ambiguity.

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 implies when to use the tool (when comparing Polymarket and Kalshi spreads) and explains the two modes, but it does not explicitly contrast with sibling tools such as polymarket_arbitrage or bet_research. There is no 'when not to use' or guidance on alternatives, though the caution about real spreads being rare provides some usage context.

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

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

Annotations already indicate safe read-only, idempotent behavior. Description adds context about scoping and pairing with remember/forget, providing useful additive detail.

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

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

Two efficient sentences, no redundant phrasing, front-loaded with core purpose.

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

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

For a simple tool with one optional parameter and no output schema, the description fully covers purpose, usage, scoping, and sibling relationships.

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. Description restates the key's purpose (omit to list) but adds little beyond schema, earning a 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 action (retrieve or list) and resource (saved values/keys), distinguishing it from siblings like 'remember' and 'forget'.

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

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

Explicitly describes when to use (look up stored context) and scoping (by identifier), but lacks explicit 'when not to use' or alternatives beyond referencing remember/forget.

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, non-destructive, idempotent behavior. The description adds important behavioral context (mark_read flag advances the read cursor, feed is persistent) and explains parameter effects without contradicting annotations.

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

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

The description is concise (three sentences) and well-structured: purpose, returned data, then parameter explanation and alternative access. Every sentence adds value with no wasted words.

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

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

Covers what is returned (source, citation_uri, payload), filtering by type/since, mark_read feature, and alternative HTTP endpoint. Lacks explicit mention of unread_only parameter and ordering, but still sufficiently complete given no output schema.

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

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

Schema coverage is 100% with descriptions, but the tool description adds extra value by explaining mark_read's effect on subsequent calls and noting the filter options. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool retrieves fired events from a subscription feed, specifying the returned data (source, citation_uri, payload). It distinguishes itself from siblings like 'list_subscriptions' by focusing on fired events rather than subscription management.

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

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

The description advises that polls work fine and provides an HTTP alternative for scripts/dashboards, giving practical context. However, it does not explicitly compare to sibling tools 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?

The description goes well beyond annotations by detailing the fan-out behavior across multiple sources (SEC EDGAR, GDELT→GNews fallback, USPTO), error handling (soft-fail for USPTO, rate-limiting fallback), and the return format (structured changes[] with citations). This provides rich behavioral context.

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

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

The description is well-structured with example queries at the start and key details following. Every sentence contributes meaningful information, though it could be slightly more concise without losing 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 the tool's complexity (multiple data sources, fallback logic, various parameter formats) and lack of output schema, the description thoroughly covers behavior, input, and output. It leaves no critical gaps for an AI agent to make correct decisions.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by explaining the `since` parameter accepts both ISO dates and relative shorthand (e.g., '7d', '3m'), with examples. It also clarifies that '30d' or '1m' is typical for 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 specifies the tool's purpose clearly: it returns a change feed for a company (SEC filings, news, patents) in a given time window. It distinguishes from the sibling `entity_profile` by stating 'Use entity_profile instead when you want the static profile... regardless of window.'

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 examples of when to use the tool (e.g., "What's new with X", "latest on Y") and explicitly mentions an alternative (`entity_profile`) for static profiles. It does not explicitly state when not to use, 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?

Adds persistence details beyond annotations: authenticated users get persistent memory, anonymous 24-hour retention. Annotations are consistent (idempotentHint=true, 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 concise sentences, no wasted words, front-loaded with purpose and usage.

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 covers purpose, usage, behavioral nuances, and parameter examples. No output schema, but not needed for this simple tool.

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

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

Schema coverage is 100% with good descriptions for key/value. Description provides example keys but does not add substantial meaning beyond schema; baseline 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?

Clear verb 'Save' with specific resource 'data the agent will need to reuse later', and distinguishes from siblings 'recall' and 'forget' by stating pairing.

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

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

Explicitly states when to use: 'when you discover something worth carrying forward', with concrete examples. Does not include when not to use or alternatives beyond recall/forget, but 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. Description adds significant detail: it cascades through multiple endpoints, provides exact return fields (ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand for drug), and explains auto-disambiguation. No contradiction.

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

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

The description is a single dense paragraph but front-loaded with example queries. Every sentence adds unique value. Could be broken into bullet points for readability, but not necessary.

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

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

No output schema exists, but description thoroughly explains return values per type, including citation URIs. Also mentions internal cascading, making the tool's behavior fully predictable.

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

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

Schema coverage is 100%, but description adds meaning: explains that for 'company', value accepts ticker, CIK, or name; for 'drug', brand or generic name; and mentions auto-disambiguation. This goes 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 uses specific verbs ('resolve') and resources ('NAME to canonical/official identifier'), and clearly distinguishes from sibling tools like entity_profile or search_by_name by specifying it provides IDs needed by other 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 'Use FIRST whenever you have a name but need an ID.' It does not enumerate alternatives but states it 'replaces 2-3 manual lookups', effectively guiding when to use. Missing explicit when-not-to-use or alternatives, but clear enough.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context by explaining the internal process (calling ai_visibility_check per entity), ranking logic, and output fields (score, confidence, signal density). No contradictions.

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

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

The description is concise with four sentences, front-loaded with the core purpose. Every sentence adds value, and the structure is clear 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 complexity, comprehensive annotations, and full schema coverage, the description adequately explains the process and return values (ranked list with score, confidence, signal density). No output schema exists, so this explanation suffices.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds some meaning for the 'entities' parameter (first entry treated as subject) and 'context' (shared context), but does not significantly enhance understanding of 'models' or '_apiKey' 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 compares AI visibility across multiple entities side-by-side, with specific actions (probe, rank, surface). It distinguishes from sibling 'ai_visibility_check' which does single entity checks, 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 provides a clear use case ('competitive AI-marketing audits') and an example question ('does Claude know about us as well as our competitors?'). While it doesn't explicitly list alternatives or when-not-to-use scenarios, the context is strong 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts. This goes 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 dense paragraph that packs significant information without wasted words. Could be slightly improved with bullet points, but remains clear and front-loaded with 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?

Given no output schema, the description thoroughly explains what is returned: summary block, per-advisory detail, links, recent alternatives. Also covers partial failures and timing behavior. Complete for a composite tool.

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

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

Schema coverage is 100% with both parameters described. The description mentions scoped packages accepted and version defaults to latest, but these are already in schema. No additional semantic enrichment beyond schema.

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

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

The description clearly states it is a composite check for adding npm packages, covering license, advisories, version history, bundle size, etc. It distinguishes itself from siblings by focusing on npm dependency evaluation, which is unique among the sibling list.

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

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

Explicitly tells when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also specifies scope limitation: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under 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 declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds that the tool returns CIDs matched to a name, which is consistent with annotations. No new behavioral traits beyond what annotations provide, but the description does not contradict them.

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

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

The description is four sentences, starting with the core purpose and followed by usage guidance, output description, and next step. It is front-loaded and efficient, though could be slightly more concise without losing clarity.

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

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

For a simple name-to-CID lookup tool with an output schema (not shown but present), the description sufficiently explains the return value (CIDs) and suggests next steps (use get_compound). Given the low complexity and rich annotations, the description is complete.

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

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

Schema coverage is 100%, so the schema already describes both parameters (name and max_results). The description does not add new semantic meaning beyond examples that mirror the schema's examples. Baseline of 3 is appropriate given full schema coverage.

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

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

The description clearly states the tool resolves chemical/drug names to PubChem CIDs, with concrete examples ('what's the CID of ibuprofen?'). It distinguishes from siblings like get_compound by specifying that this is the name-to-CID step, then suggesting get_compound for properties.

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 provides use cases ('Use for "what's the CID of ibuprofen?" or to disambiguate') and mentions a follow-up tool (get_compound). However, it does not explicitly state when not to use this tool, though the sibling list implies alternatives for other queries.

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

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

While annotations already declare readOnlyHint, idempotentHint, etc., the description adds valuable context: returns top-N passages with character offsets and similarity scores, embedding model details (BGE-base-en, cosine, 500-char windows), and 200K char cap with truncation flag.

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: purpose, usage guidance, technical details. No fluff, well-organized, front-loaded with most important info.

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 explains return format (passages with offsets and scores). Covers truncation behavior. Complete for a search tool with 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%, so baseline is 3. Description adds examples for query, clarifies limit range (1-20, default 5), and specifies max chars for text, exceeding basic schema info.

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 'Semantic search INSIDE a fetched record' with specific verb ('search'), resource ('record'), and distinguishes from siblings like 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 says when to use ('Use when the record is too big to cram into the prompt') and provides alternative ('Pairs with ask_pipeworx_grounded').

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: it mentions the return of subscription ID, delivery channel specifics (rate limits, verification, auto-disable after failures), and type-specific parameters. Annotations only indicate idempotency and non-destructive nature, but the description enriches understanding.

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 uses structured formatting with colons and line breaks. It is not overly verbose but could be slightly more concise. Every major aspect is addressed.

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 subscription creation tool with 3 parameters and no output schema, the description is comprehensive: it covers authentication, type selection, parameter format, delivery options with limitations, and notes that the feed is always on. There is no missing critical 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?

The input schema covers all parameters with descriptions, but the tool description only lists three of the five enum types (missing patent_grant and clinical_trial). The description adds concrete examples for those three and elaborates on delivery objects beyond schema, but the omission of two types reduces completeness.

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

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

The description begins with 'Create a proactive monitoring subscription' which clearly states the action (create) and resource (subscription). It distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides prerequisites (Pipeworx OAuth account) and usage context (supported types, delivery channels) but does not explicitly contrast with alternatives or state when not to use. It is clear but lacks exclusion guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds behavioral detail: returns category-bucketed example questions each with exact tool+argument shape, drawn from live catalog. No contradiction; description enriches 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?

Description is dense and front-loaded with common queries. Every sentence earns its place, but the length could be slightly compressed without losing information. Ideal structure for clarity.

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

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

Given a single optional parameter, no output schema, and robust annotations, the description fully covers what the tool does (returns examples), how to use it (with/without topic), and its context (onboarding). 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% for the single 'topic' parameter. The description adds semantics by providing example values ('finance', 'pharma', 'betting') and explaining what happens when omitted (full spread). This exceeds schema 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 explicitly states the tool is 'the onboarding entry point' for new connections, with a clear verb ('returns category-bucketed example questions') and resource (Pipeworx capabilities). It distinguishes itself from sibling tools by positioning itself as the first-use tool when the user doesn't know Pipeworx's offerings.

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 this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Also specifies call variants: 'Call with no arguments for the full spread, or pass topic to focus'. This clearly indicates when and how to use vs. alternatives.

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

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

Beyond annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true), the description adds that the subscription is deactivated (not deleted) and ownership is enforced. No contradiction with annotations.

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

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

Two sentences, no filler, front-loaded with the action verb. Every phrase 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 one-parameter tool with no output schema, the description covers purpose, ownership constraint, and behavioral nuance (deactivation vs deletion). Complete for context.

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

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

Schema coverage is 100% and already describes 'id' as a uuid from subscribe. Description adds minimal extra meaning by mentioning 'by id' but reinforces the source of the parameter. 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 'Cancel a subscription by id.' This directly specifies the verb 'cancel' and the resource 'subscription', 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?

Description notes ownership enforcement ('you can only cancel your own subscriptions') and explains the effect (deactivation, not deletion, keeping historical events). While it doesn't explicitly list when not to use it, the ownership constraint provides clear guidance.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details return values (verdict types, structured form, citation, delta) and explains the tool's efficiency advantage. No contradictions with annotations.

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

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

The description is a single, well-structured paragraph that is front-loaded with purpose and examples. It is efficient but could be slightly more organized (e.g., bullet points) for even easier scanning.

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 parameter, no output schema, strong annotations), the description is complete. It explains inputs, outputs, and use cases adequately, with no missing context.

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

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

Schema coverage is 100% (one parameter with description). The description adds meaning by specifying the type of claims supported (company-financial) and providing examples, which enhances understanding beyond the schema.

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

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

The description clearly states the tool does natural-language claim verification against authoritative sources, with specific examples and the supported domain (company-financial claims). It distinguishes itself from sibling tools by noting it replaces multiple sequential calls.

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

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

Explicitly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' It specifies the scope (company-financial claims), but does not explicitly exclude non-financial claims, hence slight deduction.

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