Opencages

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

The description adds rich behavioral details beyond the annotations: it specifies the return format (per-model score, confidence, signals, raw_response, combined view), explains default model and API key handling, and notes costs. Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature, aligning 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 a single, well-structured paragraph with front-loaded purpose. It covers all key points without redundancy, though it could be slightly more concise. Overall efficient and informative.

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

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

Given the tool has 4 parameters and no output schema, the description fully explains the return format and the purpose of each parameter, including optional context. It provides a complete picture for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the default model (Workers AI Llama), that _apiKey enables Anthropic with direct billing, and provides example values for entity. This extra context justifies 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 probes LLMs for knowledge about an entity and returns a visibility score, using specific verbs and resources. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on general AI visibility across multiple models.

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

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

The description provides explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It does not provide explicit when-not-to-use guidance, but the context is clear and sufficient for appropriate tool selection.

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

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description adds that the tool routes to a specific number of tools and sources, fills arguments, and returns citations with stable URIs. This provides useful behavioral context beyond the annotations.

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

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

The description is front-loaded with the key directive 'PREFER OVER WEB SEARCH' and lists specific use cases. It is somewhat lengthy but every sentence adds value, and the structure is 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?

The description explains the tool's functionality and appropriate use cases but does not detail return value format or limitations such as authentication or rate limits. Given the absence of an output schema, some gaps remain.

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

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

Schema description coverage is 100% with aliases documented. The description provides examples but does not add semantic meaning beyond what the schema already offers. 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: routing factual questions to a vast set of verified sources and returning structured answers with citations. It explicitly contrasts with web search and lists specific data domains, making its scope very clear.

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

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

The description provides strong guidance on when to prefer this tool over web search, including example queries and intent phrases. However, it does not distinguish from the sibling 'ask_pipeworx_grounded', which may be a close 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?

Describes return format with explicit refusal reasons, the extra LLM call cost, and the process. Adds value beyond annotations which already indicate safety (readOnlyHint, idempotentHint). 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?

Eight sentences, each adding relevant detail. Well-structured but slightly verbose for an AI agent; could be more terse while retaining 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, description fully explains return values (including refusal scenarios) and behavioral differences from sibling. Covers all contextual needs 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 100%, but description adds that the parameters are aliases for question, clarifying flexible input. This adds meaning beyond the schema's individual property 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?

Clearly identifies as 'Hallucination-resistant answer mode for high-stakes reads' with specific verb+resource: extracts answer using ONLY tool result. Distinguishes from sibling ask_pipeworx by mentioning extra LLM call cost and use case.

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 answer will be quoted, cited, or acted on' and when not: 'Prefer ask_pipeworx for casual lookups.' Provides examples (financial verdicts, legal claims, medical lookups, public statements).

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=true, idempotentHint=true) indicate safety, and the description adds extensive behavioral details: market resolution, classification, fan-out, safety mechanisms (low-confidence short-circuits, closed markets, wide spreads, resolution-rule risk), response shapes, and error handling. This far exceeds what annotations provide, making agent behavior fully transparent.

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

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

The description is extremely lengthy (nearly 500 words) and contains redundant details (e.g., repeating classifier lists, multiple examples). While thorough, it sacrifices brevity; key information could be condensed into a more structured format 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?

Despite complexity (classifiers, fan-outs, safety checks, response shapes), the description covers all aspects comprehensively. No output schema exists, so the description fully explains return values, edge cases, and blocking behaviors, making the tool complete 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%, but the description adds significant meaning beyond property definitions: 'market' accepts slug, URL, or text; 'depth' enum is explained; 'include_raw' details behavior and size implications. This provides agents with practical guidance not evident from schema alone.

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

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

The description explicitly states the tool's function: '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). The purpose is clear and actionable, distinguishing it from siblings like polymarket_edges or polymarket_arbitrage, even if not explicitly contrasted.

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

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

The description provides explicit usage scenarios: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also gives fan-out examples and classifiers. However, it does not explicitly state when not to use this tool or mention alternatives like polymarket_edges for edge discovery, which would improve 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?

Adds significant context beyond annotations: specifies data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs), data points (revenue, net income, etc.), sorting by primary metric, and citation URIs. No contradiction with annotations.

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

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

Single paragraph covers all key points efficiently, front-loaded with usage examples. Slightly dense but every sentence adds value. Could be broken into shorter sentences for readability.

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

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

For a complex tool with two entity types, multiple metrics, sorting, and citations, the description provides all necessary details including number of entities (2-5), data sources, and return format. No output schema, but description compensates.

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

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

Schema coverage is 100% with descriptions, but the description adds essential meaning: explains what each type returns (revenue metrics vs adverse events) and gives examples for values (tickers/CIKs for company, names for drug), which is 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 uses specific verbs like 'compare' and 'side-by-side comparison', clearly identifies the resource (entities of type company or drug), and distinguishes from siblings by stating 'ALWAYS PREFER over 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 says when to use: for comparing entities and preferring over sequential lookups. Does not explicitly say when not to use, but implies single-entity lookups are handled by other tools.

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

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

Discloses that it decomposes questions, routes to 3,751 tools, extracts grounded answer with verbatim evidence, confidence, source, and citations, returns gaps for unanswered facets, does not invent, and mentions expected execution time (15-60s). This adds context beyond the annotations which already indicate readOnly and idempotent.

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

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

The description is comprehensive but not overly verbose. Every sentence serves a purpose, providing key functional details, usage guidance, and behavioral context. Could be slightly streamlined 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 (multi-source, no output schema), the description fully explains what it returns (structured findings with evidence, gaps), prerequisites, expected time, and limitations. 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 description coverage is 100%, and the schema already describes both parameters (question and depth) with sufficient detail. The tool description adds no new information beyond the schema.

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

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

The description clearly states the verb 'researches' and the resource 'multi-source research'. It distinguishes from sibling 'ask_pipeworx' by noting that deep_research handles broad or multi-part questions, while ask_pipeworx is for single lookups.

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

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

Explicitly states when to use (broad/multi-part questions) and when not (single lookups use ask_pipeworx). Also notes prerequisites (Pipeworx account) and limitations (depth:thorough requires paid plan).

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds behavioral details: returns top-N relevant tools with full schemas and curated examples, ready to call directly, no second lookup needed. No contradictions.

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

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

Well-structured with front-loaded main action, domain examples, output description, and usage guidance. Slightly long but every sentence is informative; minor redundancy in listing aliases again.

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

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

No output schema, but description explains return format (top-N tools with schemas) adequately. Parameters fully covered. Missing details on pagination if many results, but overall sufficient.

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

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

Schema coverage is 100%, but description adds value by listing multiple aliases for query (task, q, description, search) and specifying default/max for limit, enhancing understanding beyond schema.

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

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

The description clearly states the tool finds tools by describing data or task, lists specific domains (SEC filings, FDA drugs, etc.), and distinguishes itself from sibling tools by being for browsing/discovery.

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 'Call this FIRST when you have many tools' and 'when you need to browse, search, look up, or discover what tools exist,' providing clear when-to-use guidance.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destruction. The description adds valuable behavioral details: that it fans out across multiple sources, returns specific data fields with URIs, notes a soft-fail for patents, and describes parallel execution. No contradictions.

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

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

The description is a single dense paragraph that front-loads examples and the key directive. While slightly long, every sentence provides essential information. Structuring into bullet points for output fields could improve scannability, but it remains clear and efficient.

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

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

Given the complexity (multi-source profile) and lack of output schema, the description covers return fields (cik, company_name, filings with URIs, fundamentals, patents, news, LEI), failure handling (patents soft-fail), and input constraints. It is comprehensive for a single-shot profile, though an example output structure would be even better.

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 nuance beyond the schema by clarifying that 'type' must be 'company' (reiterating the enum) and that 'value' must be ticker or CIK, explicitly calling out that names are not supported and advising use of a sibling tool. This enhances understanding.

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

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

The description uses concrete example queries ('Tell me about X', 'research Acme') and states the core action: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes from siblings by explicitly preferring this tool over chaining single-pack lookups.

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

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

The description explicitly states when to use this tool ('ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups') and when not to ('Names not supported — use resolve_entity first'). This provides clear context and alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true, so the description's mention of 'clear sensitive data' adds modest context but does not significantly expand 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 concise sentences covering action, purpose, and usage guidance with no unnecessary words. Front-loaded for 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?

For a simple single-parameter tool with no output schema, the description covers the core functionality, usage context, and relationship to siblings. Could note permanence but not required.

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

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

Schema coverage is 100% with a clear description for 'key'. The tool description does not add any additional parameter details beyond what the schema provides, so baseline score of 3 applies.

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

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

The description clearly states the action ('Delete'), the resource ('memory'), and the parameter ('by key'). It also distinguishes itself from siblings like 'remember' and 'recall' by implication.

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 scenarios (stale context, task done, clear sensitive data) and mentions pairing with related tools, offering clear guidance on alternatives.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, indicating safety and idempotence. The description adds valuable context: it fetches the page, extracts title/description/key links, and emits markdown output, going beyond the annotations.

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

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

The description is three sentences, front-loaded with the main purpose, and contains no redundant 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?

With only two simple parameters (both documented) and no output schema, the description explains the output format (single text blob, markdown, filename) and purpose. It is fully complete for the tool's complexity.

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

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

Schema coverage is 100% with both parameters (url, max_links) fully described. The description does not add new parameter information beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's action: generate an llms.txt file for a given URL. It specifies the output format and mentions specific use cases. It is distinct from sibling tools, which are unrelated.

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

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

The description explicitly lists when to use the tool (getting a client's site indexed, drafting for own project, auditing competitor) and the context. It does not mention when not to use it or alternative tools, but the provided use cases are clear.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which convey safety and idempotency. However, the description adds no behavioral details beyond these annotations, such as API usage, rate limits, or response characteristics. The agent gains minimal extra insight.

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

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

While extremely short at two words, this is under-specification rather than conciseness. The description fails to convey essential information, making it insufficient for an AI agent to understand tool behavior.

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

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

Despite having an output schema, the description does not mention what the tool returns (e.g., coordinates). It ignores the tool's complexity (7 parameters, sibling tools) and provides no usage context, leaving the agent without a complete understanding.

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

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

With only 29% schema description coverage and 7 parameters, the description 'Forward geocode' provides no parameter help. Parameters like 'abbrv', 'no_annotations', and 'language' remain completely undocumented, leaving the agent with no contextual clues for their 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 'Forward geocode' is a tautology that merely restates the title. It does not specify that the tool converts addresses to coordinates, nor does it differentiate from the sibling 'reverse' tool. The purpose is vague and uninformative.

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 usage guidelines are provided. There is no mention of when to use this tool versus alternatives like 'reverse', no context about prerequisites or limitations, and no exclusion criteria.

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

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

Annotations already provide read-only, idempotent hints. Description adds value by listing return fields and the include_inactive parameter behavior, enhancing transparency.

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

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

Two concise sentences that are front-loaded with the main purpose and provide immediate utility 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?

For a simple, well-annotated tool with one parameter, the description covers usage, return fields, and parameter behavior. No output schema, but fields are listed.

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

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

Schema covers the only parameter fully (100% coverage). Description does not add extra meaning beyond the schema description.

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

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

Description clearly states it lists the caller's active subscriptions and specifies return fields. Distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Explicitly says to use it for reviewing monitoring and finding IDs to cancel. Provides clear context, though doesn't explicitly 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?

Beyond annotations (all false), description adds critical info: rate-limited to 5/identifier/day, free, doesn't count against quota. 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?

Very concise: 4 sentences, each serving a purpose. Front-loaded with purpose, then usage, then restrictions. No filler.

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?

Completely describes the tool's behavior, constraints, and usage. No gaps given no output schema needed for a feedback submission tool.

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

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

Schema coverage is 100% so baseline is 3. Description adds extra value by instructing users to describe issues in terms of Pipeworx tools/packs, which is not in 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: to send feedback (bug, feature, data_gap, praise) to the Pipeworx team. It distinctly separates it from sibling tools like ask_pipeworx which are for questions, not feedback.

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 usage guidance provided: when to use for bugs, features, praise, and what to avoid (don't paste end-user prompt). No ambiguity.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable context: data source (CF analytics-engine), privacy (no PII), content (pack, tool, count), and caching behavior (5min-1h). 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?

Four well-structured sentences with bullet-style use cases. Every sentence adds value without redundancy. Efficient and front-loaded with the core functionality.

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

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

No output schema, but the description sufficiently explains the return values (top tools, top packs, total call volume). Low complexity with one optional parameter and good annotations make this complete.

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

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

Schema coverage is 100% with a clear enum description. The description adds nuance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand,' which goes beyond the schema.

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

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

The description clearly states it returns top tools, top packs, and total call volume for a recent window, distinguishing it from sibling tools like ask_pipeworx or discover_tools. The verb 'returns' and specific resource 'trending' make the purpose unmistakable.

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

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

Three explicit use cases are provided, covering discovery, validation, and alignment. While it doesn't explicitly state when not to use or compare to siblings, the use cases give strong guidance for 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?

Adds extensive behavioral context beyond annotations: required argument constraint, internal checks (Jaccard similarity, placeholder filter, fill check with CLOB depth), and trade recommendations. 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?

Dense but well-structured, every sentence adds value. Slightly lengthy due to complexity, but front-loaded with requirement and main purpose. No repetition.

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?

Even without output schema, the description details output structure (opportunities with gap_pp, suggested_trade, etc.), constraints, and decision logic (realizable edge check). Complete for a tool of this 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% but description greatly enriches parameter meaning: explains format of event (slug or URL), purpose of topic, and how each mode drives different analysis logic.

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?

States specific verb 'find' and resource 'arbitrage opportunities' on Polymarket, distinctly differentiates two modes (event vs topic), and contrasts with sibling tools via mention of custom sizing tool 'polymarket_fill_risk'.

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 recommends event mode for a specific market and topic for cross-event scanning, explaining when each is appropriate. Does not explicitly list when not to use, but provides clear context for selection.

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

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

Beyond the annotations (readOnly, openWorld, idempotent), the description details internal models (e.g., lognormal barrier, GDELT ratio), segment logic, caching frequency (1h at KV level), and response fields including diagnostics and warnings. 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 highly detailed and technically dense, covering models, segments, parameters, and diagnostics. While every sentence adds value, its length may overwhelm quick scanning. Front-loading the purpose and use case helps, but the volume of detail reduces conciseness.

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

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

Given the tool's complexity (9 parameters, no output schema), the description compensates thoroughly: it explains the three response segments, diagnostics, fed market note, caching behavior, and the effect of each knob. The response structure is fully described, making the tool self-contained for an agent.

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

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

With 100% schema description coverage, the description adds value by explaining how parameters like min_partition_leg_kelly interact with the internal segmentation logic. The contextual explanation of knobs (e.g., min_liquidity, max_spread_pp) goes beyond the basic schema descriptions.

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

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

The description clearly identifies the tool's purpose: scanning Polymarket markets to find opportunities where Pipeworx data disagrees with market price. It explicitly states the use case 'what should I bet on today' and differentiates from siblings like polymarket_arbitrage by focusing on model-driven edges and structural arbitrage detection.

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

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

The description provides strong usage context: 'agents discover opportunities without paging hundreds of markets' and explains the three response segments and tradeable-edge knobs. However, it does not explicitly mention when not to use the tool or name direct alternatives, though the sibling list suggests polymarket_arbitrage may be a related 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?

The description adds significant behavioral context beyond the annotations (which declare read-only, open-world, idempotent, non-destructive). It explains the 60-day snapshot TTL, that decay is computed from daily closes not intraday, and that data gaps occur when no scan ran.

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

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

The description is verbose but well-structured with sections for response and limits. It front-loads the key question. Some details could be trimmed, but it remains clear and scannable.

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

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

Despite no output schema, the description thoroughly explains the response fields (tracked, expired, snapshot_dates) and their semantics. It covers edge cases like gaps and TTL, making it complete for a tool with complex 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?

The description adds defaults and ranges for 'days' (default 14, max 30) and 'window' (default '1wk') that are not fully detailed in the schema (schema only says clamp 2-30). It also explains what the window parameter represents (snapshot family).

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 'edge persistence and decay telemetry' and answers a specific question about edge duration and shrinkage. It distinguishes from siblings like polymarket_edges by focusing on historical trends and decay rather than current 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?

No explicit when-to-use or when-not-to-use guidance is given. The context implies it's for historical analysis, but no comparison to sibling tools like polymarket_edges or polymarket_arbitrage is provided.

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

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

The description details the tool's behavior: it walks the order-book ladder, computes top-of-book and VWAP fill prices, and returns slippage, shares filled, and a verdict. It also warns about thin books and directional risk. Annotations (readOnlyHint, etc.) are consistent and the description adds significant behavioral context beyond 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 lengthy (nearly 300 words) but well-structured with clear section headers (SINGLE-MARKET, BASKET) and bolded key terms. It front-loads the core purpose and uses whitespace effectively. While some redundancy exists (e.g., repeated defaults), the complexity of the tool warrants the detail. A minor reduction could improve conciseness.

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

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

Given the tool's complexity (two modes, 4 parameters, no output schema), the description covers all necessary aspects: modes, parameter usage, return fields, and risk warnings. An AI agent can fully understand how to invoke the tool and interpret results without additional documentation.

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 significant value by explaining parameter behavior in both modes, such as side defaults, size_usd interpretation (spend vs. proceeds), and the distinction between 'market' and 'event'. This clarification exceeds what the schema alone provides, justifying a score of 4.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It then distinguishes two modes (single-market and basket) and lists the returned fields. This is specific and differentiates from sibling tools 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 advises when to use the tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains the risks of partial fills and directional exposure, providing clear context for decision-making.

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 extensive behavioral context beyond the annotations: detailed explanation of modes, response structure, safety fields (compatibility_warning, temporal_alignment, skipped_cross_type), and edge cases. It does not contradict any annotation; the readOnlyHint and idempotentHint align with the read-only nature of the tool.

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

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

The description is long but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded with the core purpose. Every sentence adds information, though some redundancy could be trimmed. It earns high marks for organization but loses a point for verbosity.

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 many edge cases), the description is remarkably complete. It explains response structure despite no output schema, covers when spreads are invalid (temporal misalignment, shape mismatch), and details counters for dropped comparisons. The description fully equips an agent to use the tool correctly.

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

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

All three parameters have descriptions in the input schema (100% coverage), so the baseline is 3. The description adds value by explaining the interaction between parameters (topic shortcuts vs. explicit overrides) and providing examples in the schema's examples array. It clarifies the purpose of each parameter beyond the schema's concise descriptions.

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

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

The description clearly states it is a 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It explains two modes ('topic' and explicit tickers) and what the tool outputs. While sibling tools like 'polymarket_arbitrage' exist, the description's specificity and detail make the purpose unmistakable.

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

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

The description provides explicit guidance on when to use the tool: when bet shapes are equivalent the spread is a signal, and when not the tool says so. It explains the two modes and warns that most pre-mapped topics return compatibility warnings today, thus guiding against misuse. The safety fields and temporal alignment discussion further clarify when spreads are meaningful.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds scoping information (based on identifier) and clarifies that it retrieves without side effects. No contradiction.

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

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

Description is four sentences that efficiently convey the action, usage context, and pairing. No wasteful language, well-structured.

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

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

For a simple key-value retrieval tool with no output schema and comprehensive annotations, the description covers purpose, scope, and pairing. Minor gap: does not explain behavior when key is not found, but this is acceptable.

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 the parameter similarly. Description repeats the two modes (retrieve vs list) already in the schema, adding minimal 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?

Description clearly states the tool retrieves values saved via remember, and lists all keys when omitted. It distinguishes from sibling tools (remember saves, forget deletes) by specifying the retrieval and listing behavior.

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 context: 'Use to look up context the agent stored earlier... without re-deriving from scratch.' It mentions pairing with remember and forget, but does not explicitly state when not to use this tool or offer alternatives.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds important detail about the mark_read side effect and return fields (source, citation_uri, payload). 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 front-loading purpose, each sentence adds value: return format, filtering, mark_read behavior, polling advice. 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 simple parameters and no output schema, description provides sufficient context: return fields, filter options, side effect, alternative access method. Could mention pagination but not required.

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

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

Schema covers all 5 parameters with descriptions (100% coverage). Description mentions a few parameters but doesn't add significant meaning beyond schema; 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 it pulls fired events from the subscription feed, with specific verb 'Pull' and resource 'subscription feed'. It distinguishes itself from sibling tools like subscribe/unsubscribe by focusing on retrieval.

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

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

Provides clear context: polling works fine, alternative REST endpoint for scripts/dashboards. No explicit when-not-to-use, but adequate guidance for usage.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description discloses fan-out to multiple sources with fallback logic (GDELT preferred, GNews on rate-limit/5xx), USPTO soft-failure, and return structure (changes grouped by source, total_changes, 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 front-loaded with example queries and packs substantial detail about fallback behavior and return format. While long, every sentence is informative; slight reduction in length could improve conciseness 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 3 required params, no output schema, and multi-source complexity, the description fully covers when to use, how parameters work, return format, and fallback behavior. Sufficient for an agent to invoke correctly without additional knowledge.

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

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

Schema coverage is 100% with each parameter described. The description adds value by explaining 'since' with examples and recommended defaults, and clarifies 'value' accepts both ticker and CIK. Goes beyond schema.

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

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

The description clearly states the tool's purpose as a 'change feed for a company in the last N days/weeks/months' using multiple data sources. It explicitly distinguishes itself from the sibling tool 'entity_profile' by contrasting dynamic changes vs. static profile.

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

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

The description provides explicit example queries ('What's new with X', 'latest on Y') and directs the agent to use 'entity_profile' instead when a static profile is needed. It also offers guidance on the 'since' parameter with recommended values like '30d'.

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 idempotentHint=true (safe to repeat) and destructiveHint=false. The description adds important context: key-value scoping by identifier, persistence rules (24h for anonymous, permanent for authenticated), and that it stores data for later use. 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?

Every sentence serves a purpose: purpose, when to use, storage details, pairing with other tools. No fluff. Well-organized and front-loaded.

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

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

Given the tool's simplicity (2 string params, no output schema), the description fully covers what the agent needs: storage scope, persistence, usage context, and relationship to siblings recall/forget.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the purpose of key-value pairs with examples (e.g., 'subject_property', 'target_ticker'), going beyond the schema's generic descriptions.

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

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

The description clearly states the tool's purpose: saving data for later reuse across conversations or sessions. It provides specific examples of what to save (ticker, address, preference) and distinguishes it from sibling tools like recall and forget.

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

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

The description explicitly tells when to use this tool ('when you discover something worth carrying forward') and what not to do (pair with recall/forget). It also clarifies behavior for authenticated vs anonymous sessions.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint. The description adds value by explaining internal behavior: 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.' It also details specific return data for each entity type.

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

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

Well-structured with clear sections: general purpose followed by supported types. Each sentence adds information, but the description is somewhat lengthy and could remove redundancies like 'auto-disambiguated' to improve conciseness.

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

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

Given moderate complexity (two entity types) and no output schema, the description adequately covers return values (ticker, CIK, RxCUI, etc.) and mentions citation URIs. However, it does not address edge cases like ambiguous inputs or error handling.

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 significantly extends meaning by providing concrete examples for 'value' (e.g., ticker, CIK, brand/generic name) and detailing the return characteristics for each 'type' enum 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 uses specific verbs like 'resolve' and gives concrete query examples ('What's the ticker for...', 'find the CIK for...'), clearly stating the tool's function. It distinguishes itself from sibling tools by noting that it returns canonical identifiers required 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 says 'Use FIRST whenever you have a name but need an ID,' providing clear context for when to use the tool. However, it does not mention when not to use it or any alternatives, though the 'first' instruction implies it is the starting point.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds no behavioral information beyond the name, such as return format or constraints.

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

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

Extremely concise but at the expense of informativeness. Two words do not provide enough value for an agent to understand 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?

Given 5 parameters, no schema descriptions, and an output schema present, the tool description is woefully incomplete. It fails to provide needed context.

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

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

Schema has 0% description coverage and the description does not explain any parameter. All 5 parameters (lat, lon, abbrv, language, no_annotations) are left for the agent to infer.

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 'Reverse geocode' which is a specific operation and distinguishes from sibling 'geocode'. However, it lacks any additional detail beyond the name.

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

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

No guidance on when to use this tool vs alternatives. The context is only implied by the name and sibling list.

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

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

Annotations already declare read-only, idempotent, and non-destructive. The description adds behavioral details: it calls ai_visibility_check per entity, ranks results, and returns score/confidence/signal density. 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 concise sentences with front-loaded purpose, action details, and usage example. Every sentence adds value; no redundancy.

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

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

All 4 parameters are described in schema and enriched in description. Output is explained (ranked list with three fields). Annotations cover safety. No gaps 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, and the description adds extra semantics: first entity treated as subject, models default to workers-ai, context disambiguates, entities limited to 2-8. These enrich 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 compares AI visibility across multiple entities side-by-side, using ai_visibility_check, ranking by score. It distinguishes itself from sibling tools like ai_visibility_check (single entity) by focusing on competitive comparison.

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

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

The description provides use context ('competitive AI-marketing audits') and an example question, but does not explicitly state when not to use it or list alternatives beyond the sibling hint.

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 significant behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. 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?

Description is slightly long but well-structured, front-loaded with the composite purpose. Each sentence provides useful information without waste. Could be slightly more concise but still effective.

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

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

No output schema exists, so description must detail return structure. It accurately describes the summary block, per-advisory detail, links, and alternative versions. Handles edge cases like partial failures and timeouts, ensuring agent knows what to expect.

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

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

Schema coverage is 100% and includes descriptions for both parameters. The description adds value by mentioning that scoped packages are accepted and defaults for version (though schema already states default). Provides helpful context beyond raw schema.

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

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

Description clearly states it's a composite check for npm packages covering license, advisories, version history, and bundle size. Distinguishes from sibling tools by being a comprehensive one-call tool for npm packages, with explicit mention of alternatives for other ecosystems.

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

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

Explicitly says to use when an agent asks about safety, popularity, size, or cost, and provides clear scope (NPM only in v1) and alternatives for other ecosystems (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?

Discloses internal mechanics: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).' This adds significant detail beyond the readOnlyHint and idempotentHint 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 compact (3 sentences), front-loads purpose and usage, then provides technical details. Every sentence earns its place—no redundancy.

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

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

Given 3 parameters and no output schema, the description comprehensively covers when to use, how it works, return format (passages with offsets and scores), constraints (200K char limit, truncation), and pairing with 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 coverage is 100%, so baseline is 3. Description adds example queries for the 'query' parameter but does not provide new information beyond the schema for 'text' or 'limit'. The context added is helpful but not essential.

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+resource. It gives examples (SEC 10-K, article) and distinguishes from siblings like ask_pipeworx_grounded by explaining the pairing workflow.

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

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

Explicitly states when to use: 'Use when the record is too big to cram into the prompt.' Also provides alternatives and pairing guidance with ask_pipeworx_grounded, giving clear context for decision-making.

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 contradicts the idempotentHint annotation. The annotation says idempotentHint=true, but the description states 'Create a new subscription' and 'Returns the new subscription id', implying each call creates a distinct subscription, which is not idempotent. This contradiction severely misleads the agent about the tool's behavior. Other behavioral details (OAuth requirement, SMS cap) are useful but cannot compensate for the contradiction.

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

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

The description is somewhat long but front-loaded with the primary purpose. It efficiently packs a lot of information without redundancy. A slightly more structured format (e.g., bullet points) could improve readability, but it remains 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 complexity of the tool (three parameters, nested objects, five subscription types, multiple delivery channels), the description provides almost all necessary details: types, parameter formats, delivery options, and requirements. The only missing piece is the absence of an output schema, but the description mentions the return value (subscription id).

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema with concrete examples and explanations for each subscription type (e.g., sec_8k with item codes, polymarket_edge with topics). This helps the agent understand how to populate the params object correctly.

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: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It specifies the resource (subscription), the verb (create), and what is returned. 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 clear context on when to use: requires Pipeworx OAuth account, not for anonymous/BYO. It lists all supported types with detailed examples and explains delivery channels. However, it does not explicitly state when NOT to use this tool or alternatives, but the details are sufficient for decision-making.

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

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

Annotations show read-only, open-world, idempotent, non-destructive. Description adds that it returns example questions with exact tool+argument shapes from a live catalog, and hints at meta-tools. 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 slightly long but well-structured with examples, usage instructions, and list of topics. Every sentence adds value, but could be tightened slightly.

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

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

No output schema, but description fully explains what is returned: category-bucketed example questions with exact tool+argument shapes. Complete for a discovery/onboarding 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?

Single optional parameter 'topic' with 100% schema coverage. Description adds examples of valid topic values and explains behavior: full spread vs. focused. Adds 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?

Description clearly states it is the onboarding entry point for Pipeworx, returning example questions by category. Distinguishes from siblings by being the first tool to use when the agent doesn't know Pipeworx's capabilities.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you'. Provides guidance on calling with no arguments or with a topic to focus. Lists siblings, helping differentiate usage.

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

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

The description adds value beyond annotations: it explains that the row is deactivated (not deleted), historical events remain available via 'recent_alerts', and ownership is enforced. This provides clear behavioral context that annotations (destructiveHint=false, idempotentHint=true) only partially convey.

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

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

The description is two sentences long, front-loaded with the primary action, and contains no unnecessary words. Every sentence adds meaningful information.

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

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

For a simple tool with one parameter and no output schema, the description covers ownership, action semantics, and side effects (soft-delete, impact on event history). It is complete for the agent to understand behavior and consequences.

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

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

Schema description coverage is 100% and already provides a clear explanation of the 'id' parameter. The description merely restates 'by id' without adding new semantic details, so the 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 'cancel' and the resource 'subscription by id'. The title 'Unsubscribe from Alerts' aligns. It also distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying ownership enforcement and soft-delete behavior.

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

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

The description explains when to use the tool (to cancel a subscription) and provides a key condition (ownership enforced). However, it does not explicitly mention when not to use it or list alternative tools for related actions (e.g., 'subscribe' for creation, 'list_subscriptions' for listing).

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, etc. The description adds valuable context: output includes verdict types (confirmed, refuted, etc.), extracted structured form, citation, and percent delta. It also explains why this tool is efficient (replaces 4–6 calls). No contradictions with annotations.

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

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

The description is concise and well-structured. It uses bullet-like intents, states usage, outlines domain, and summarizes output—all in a few sentences. Every sentence adds value, no fluff.

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

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

Given no output schema, the description fully explains the return value (verdict types, actual value, citation). It covers scope, limitations, and efficiency gains. Combined with annotations, it provides a complete picture.

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

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

Schema coverage is 100% with a good description. The tool description adds value with examples of claim formats and mentions the data source (SEC EDGAR + XBRL), enhancing semantic 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: natural-language claim verification against authoritative sources. It starts with user intent examples ('Is it true that...') and specifies the domain (company-financial claims for US public companies). This is specific and distinct from siblings.

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

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

Explicitly states when to use ('whenever the agent needs to check factual correctness') and implies scope via 'v1 supports company-financial claims'. Missing explicit alternatives for non-financial claims, but the usage guidance is clear enough.

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