Stat Lv

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds valuable context: return format (per-model score, confidence, signals, raw_response + combined view), default model (free), and key usage. No contradictions.

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

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

Concise, well-structured, front-loaded with purpose. Each sentence adds value without redundancy.

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

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

Covers main purpose, parameters, return format, and usage scenarios. Lacks details on how the score is computed or what 'signals' means, but adequate for a probe tool. No output schema, so description compensates well.

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 description adds examples for entity, lists supported models, explains _apiKey pass-through, and provides context disambiguation example — enhancing meaning beyond schema.

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

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

The description clearly states the tool probes LLMs for knowledge and scores visibility (0-100). It distinguishes itself from siblings like deep_research and entity_profile by focusing on AI visibility checks for brands/products.

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 usage context (AI-marketing audits, pre-launch checks, competitive monitoring) and explains parameter behaviors (default model, BYO Anthropic key). However, it doesn't explicitly state when not to use it or offer direct 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds context about routing, returning structured answers with citation URIs, and being a default entry point. 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?

Somewhat long but front-loaded with 'PREFER OVER WEB SEARCH'. Contains examples and usage guidance. Well-structured but could be more concise.

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

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

Given complexity (many siblings, no output schema), the description covers usage, alternatives, and examples. Complete enough for an agent to understand when to use it.

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

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

Schema description coverage is 100% with all parameters described. The description adds no additional parameter notes beyond what schema provides. Baseline 3.

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

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

The description clearly states it routes questions to one of many tools/sources, using specific verb 'routes' and resource '4,476 tools'. It differentiates from siblings like ask_pipeworx_grounded and deep_research.

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

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

Explicitly says 'PREFER OVER WEB SEARCH', lists when to use (factual questions), when not (broad/multi-part use deep_research), and alternatives (ask_pipeworx_grounded, deep_research). Includes 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 indicate read-only, idempotent, open-world, non-destructive. Description adds behavioral details: extra LLM call cost, specific refusal reasons (not_in_source, no_tool_match, etc.), and return structure with evidence and confidence.

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 paragraphs: first defines purpose and mechanism, second provides usage guidance and return structure. Efficiently conveys all necessary information without redundancy.

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

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

Given no output schema, description fully explains the return format, refusal reasons, and use cases. Covers all relevant aspects for an agent to correctly invoke and interpret results.

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

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

Schema description coverage is 100% (all parameters are aliases for question). Description lists the aliases but 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?

Description clearly states it is a hallucination-resistant answer mode for high-stakes reads, explicitly contrasts with ask_pipeworx, and details the process of routing, fetching, and extracting answers.

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 answers will be quoted, cited, or acted on; high-stakes domains like legal, medical) and when not to (prefer ask_pipeworx for casual lookups). Also explains cost trade-off.

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

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

Annotations declare read-only and idempotent behavior, and the description adds extensive detail: low-confidence resolution short-circuits, closed market handling, wide-spread warnings, resolution-rule risk, and fan-out examples. 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 comprehensive but lengthy; it front-loads the purpose well but then jumps into many details (classifiers, fan-out examples, response shapes, safety) without clear sectioning. Every sentence provides useful info, but it could be more structured for agent 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?

For a complex tool (classifiers, fan-outs, multiple response parts, safety behaviors), the description covers every relevant aspect: how to use, what returns, edge cases (low confidence, closed markets, wide spreads, rescission rules), and even how to interpret results (inspect market_match_confidence). Very complete.

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

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

Schema coverage is 100% with decent descriptions, but the tool description adds valuable context: the market parameter accepts slug/URL/question text, depth parameter explains 'quick' vs 'thorough', and include_raw explains when to use. This goes beyond mere schema repetition.

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

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

The description states a specific verb ('Research') and resource ('Polymarket bet'), explains inputs (slug, URL, question text), and the tone and examples differentiate it from sibling tools like 'polymarket_edges' or 'polymarket_arbitrage'.

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

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

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"'. While it doesn't mention when not to use or alternatives, the context is clear enough.

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

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

Annotations declare safe, non-destructive, idempotent. Description adds: handles off-calendar fiscal years correctly (AAPL Sep, NVDA Jan), sorts results by primary metric, returns paired data + pipeworx:// citation URIs per entity, replaces 8-15 sequential lookups. 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 dense with examples and parentheticals (AAPL Sep, NVDA Jan, etc.). Front-loaded with trigger phrases, but structure is somewhat cluttered. Could be more organized while retaining 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?

No output schema, but description explains output: 'paired data + pipeworx:// citation URIs per entity.' Covers input, behavior, and output for both entity types. Adequately complete given tool 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 parameters are already documented. Description adds value by explaining what data each type pulls (SEC EDGAR/XBRL for company, FAERS for drug) and specifying that values are tickers/CIKs vs names, and count range (2-5).

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

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

Description starts with trigger phrases and clearly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies the verb 'compare', the resources (companies/drugs), and distinguishes from siblings by explicitly advising preference over sequential lookups.

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

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and gives example queries. Does not detail when not to use, but context implies it's for comparison tasks. Sibling differentiation is present 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?

The description adds substantial behavioral context beyond the annotations: account required, paid tier, parallel decomposition, returns findings packet with verbatim evidence and gaps array, and limitations for topics not in structured catalog. It does not contradict the readOnlyHint, openWorldHint, idempotentHint, or destructiveHint 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 critical information (account requirement, alternatives) and is fairly concise for the complexity covered. A few sentences could be trimmed, but overall every sentence serves a purpose for agent decision-making.

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 two parameters and no output schema, the description fully explains the return format (findings packet with evidence, confidence, source, citation, gaps array) and behavior (15-60s, gaps when topic not in catalog). It provides complete context for correct invocation and interpretation.

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

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

With 100% schema coverage, the description still enhances understanding by explaining the depth enum values (quick=3, standard=5, thorough=8 for paid), and clarifying that questions can be broad/multi-part since decomposition is the point. This adds practical meaning beyond the schema descriptions.

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

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

The description clearly states that the tool performs grounded multi-source research across 1127 structured data sources, decomposing questions into facets, and explicitly distinguishes it from siblings like ask_pipeworx (single lookup) and siblings for live news. The verb+resource are specific and actionable.

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

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

The description provides explicit guidance on when to use this tool (broad/multi-part questions over structured data) and when not (single lookup: use ask_pipeworx; breaking news: prefer ask_pipeworx). It also mentions account requirements and paid plan for thorough depth, offering clear 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 readOnlyHint=true and idempotentHint=true. The description adds behavioral context: it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' It could further clarify that it does not execute tasks, but this is sufficient.

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

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

The description is a single paragraph but effectively front-loads purpose, then usage, then output. It is not overly long but could be slightly more concise. However, it contains no filler sentences.

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

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

Given the tool's complexity (meta-discovery across many domains), the description covers the return value, usage context, and critical constraints (call FIRST). The schema is rich and annotations present. Without an output schema, the description adequately explains what the response contains.

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

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

Schema coverage is 100%, with each parameter documented in the schema. The description adds value by explaining the intended use of the 'query' parameter ('Natural language description of what you want to do') and listing aliases, complementing the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It specifies the verb 'find' and the resource 'tools', and distinguishes itself from sibling tools by acting as a meta-tool for discovery across many domains (SEC filings, FDA, etc.).

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 is provided: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This also implies when not to use (when the agent already knows the tool).

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

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

Annotations already declare read-only, idempotent, non-destructive. Description adds significant behavioral details: parallel call, USPTO sunset and soft-fail, fallback GDELT→GNews, specific return fields. 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 thorough but slightly verbose; however, every sentence provides value and it is front-loaded with example queries. Could be marginally more concise 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 data sources) and lack of output schema, the description is highly complete. It specifies all outputs, error conditions (USPTO soft-fail), input constraints, and fallback behavior, leaving minimal ambiguity.

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

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

Schema coverage is 100% with descriptions. The description adds meaning beyond the schema: clarifies accepted formats (ticker or zero-padded CIK), explains that 'type' only supports 'company', and reiterates that names are not supported.

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 provides a full cross-source profile of a US public company in one parallel call, listing data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF) and example queries. It distinguishes from siblings by explicitly advising preference over chaining single-pack lookups.

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

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

Explicitly states when to use (holistic view) and when not to (use resolve_entity for names). Provides input constraints: only ticker or CIK, not names. Gives clear directives like 'ALWAYS PREFER over chaining'.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. Description adds usage context but no new behavioral traits beyond annotations (e.g., what happens if key missing). It does not contradict annotations.

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

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

Two concise sentences with no wasted words. First sentence states purpose, second gives usage guidance.

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 deletion tool with no output schema, the description covers purpose and usage adequately. Idempotency is implied by annotation.

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 'Memory key to delete'. The tool description does not add further meaning to the parameter beyond what the schema provides.

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

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

Description clearly states the verb 'delete' and resource 'previously stored memory by key'. It distinguishes from sibling tools 'remember' and 'recall' by being their delete counterpart.

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 contexts provided: stale context, task done, clear sensitive data. Pairs with remember and recall. No explicit when-not-to-use or alternatives beyond siblings, which are 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?

Describes the internal process (fetches page, extracts title/description/key links) and output format, adding value beyond annotations which already indicate read-only, idempotent, non-destructive behavior.

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

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

Two sentences plus bullet list of use cases, front-loaded with main action. 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 2 parameters (fully documented), no output schema, and clear annotations, the description fully covers what the tool does, its process, and output. 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%, so baseline is 3. Description does not add new parameter-level details beyond schema, but contextualizes max_links in 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 generates an llms.txt file for any URL, specifying the verb 'generate', resource 'URL', and output format. It distinguishes from sibling tools like 'ai_visibility_check' by focusing on file generation.

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

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

Explicitly lists use cases (getting client site indexed, drafting own llms.txt, auditing competitor). Lacks explicit when-not-to-use or alternative tools, but context is clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: it returns specific fields (id, type, params, etc.) and defaults to active subscriptions. No contradictions. Score 4 for adding value beyond annotations.

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

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

Two efficient sentences: first states purpose and return fields, second gives usage guidance. No wasted words. Front-loaded with critical information.

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

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

Given the tool's low complexity (one optional param, no output schema), the description covers purpose, return fields, and usage guidance. Annotations cover safety. Complete for the tool's simplicity.

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 one parameter 'include_inactive' described. The description only implies the default behavior (active only) but does not add new semantic detail 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 'List the caller's active subscriptions' with specific verb and resource. It lists returned fields, but does not explicitly differentiate from sibling tools, though siblings like 'subscribe' and 'unsubscribe' are distinct. Score 4 for clear purpose but missing explicit sibling differentiation.

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

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

The description provides explicit usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use the tool, but does not mention when not to use or alternatives. Score 4 for clear context with no exclusions.

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

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

The description discloses behavioral traits beyond annotations: rate limit of 5 per identifier per day, free usage not counting against tool-call quota, and that 'signal directly affects roadmap.' 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 with no wasted words. Every sentence serves a purpose: stating the tool's action, providing usage scenarios, giving behavioral notes, and setting constraints.

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 3 parameters (2 required, 1 with enum, and a nested object) and no output schema, the description fully covers what the agent needs to know: how to format feedback, what types to use, and behavioral constraints (rate limit, free usage).

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining each enum value for 'type' and providing formatting guidance for 'message' (e.g., '1-2 sentences typical, 2000 chars max') and context for 'context' parameter.

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

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

The description clearly states the tool's purpose: sending feedback about Pipeworx tools. It explicitly lists valid feedback types (bug, feature, data_gap, praise, other) and distinguishes itself from sibling tools which are about research, queries, and 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?

The description provides explicit guidance on when to use each feedback type (e.g., 'bug = a tool returns wrong/stale data') and what not to do ('don't paste the end-user's prompt'). It also notes that feedback is free and rate-limited, setting expectations.

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 annotation hints (readOnly, idempotent), the description discloses data source (CF analytics-engine), no PII, and caching behavior (5min-1h). This adds significant 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?

The description is concise, front-loaded with key output, followed by use cases and behavioral details. No superfluous sentences; every part 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 one parameter and no output schema, the description fully explains what the tool returns, how it works, caching, and privacy. It is complete for an agent to decide to invoke.

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 window parameter has 100% schema coverage with enum and description. The description adds nuance: shorter windows surface hot trends, longer windows show steady-state demand, enhancing semantic 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 clearly states the tool returns top tools, top packs, and total call volume over a given window. It uses a specific verb ('Returns') and resource, and distinguishes from siblings like 'discover_tools' by focusing on trending usage by other AI agents.

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 three use cases (discovering hot data sources, confirming canonical tool, aligning use case). While it doesn't detail when not to use or alternatives, the context 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 indicate read-only, open-world, idempotent, non-destructive behavior. The description adds extensive behavioral details: partition check, fill check against order book, Jaccard similarity threshold, placeholder filter, and response structure.

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

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

The description is dense but well-organized. It starts with the requirement then alternates between modes, methods, and edge cases. Every sentence adds value, though some restructuring could improve readability.

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

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

Given the complexity of the tool and the absence of output schema, the description covers all critical aspects: required parameters, algorithmic methods, response fields, and practical constraints (similarity, placeholder, fill check).

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

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

Schema covers both parameters with descriptions, but the description provides richer context: examples of slugs, accepted URL format, and detailed mode semantics.

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

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

The description specifies the tool's function: finding arbitrage opportunities via monotonicity violations and partition-sum checks. It clearly distinguishes two modes (event and topic) and differentiates from sibling tools like polymarket_edges and 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 states that one of `event` or `topic` is required, and explains when to use each mode. Also directs users to polymarket_fill_risk for custom 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds extensive behavioral context: response structure (by_segment, _diagnostics), edge calculation details (model families, Kelly sizing, slippage), data sources (FRED, GDELT), and caching. It also explains that Fed bets are excluded from ranking.

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 and detailed, front-loading the purpose. However, it includes technical specifics (e.g., Kelly formulas, model details) that could be shortened. Still well-structured for a complex tool.

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

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

Given 9 parameters and no output schema, the description explains response segments and fields in detail (e.g., by_segment, _diagnostics). It lacks exact output format (JSON structure) but is otherwise complete for the complexity.

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

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

Schema description coverage is 100%, so the schema already documents parameters well. The description adds minimal extra context (e.g., calling min_liquidity and max_spread_pp 'tradeable-edge filters') but mostly repeats 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?

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It distinguishes from siblings like polymarket_arbitrage or polymarket_edge_tracker by focusing on Pipeworx disagreement and providing unique model families.

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 'what should I bet on today' and guides agents to discover opportunities without paging. It mentions tradeable-edge knobs and caching but does not explicitly state when not to use this tool or list alternative tools for specific use cases.

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

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

The description adds substantial behavioral details beyond annotations: explains response fields (tracked, expired, snapshot_dates), sign convention for edge_pp_net, snapshot creation triggers, and TTL constraints. Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, which are consistent.

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 purpose first, then response details, then limits. It is somewhat lengthy but each sentence adds necessary information, earning 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?

Given the complexity of time-series telemetry and lack of output schema, the description thoroughly explains all response fields and constraints, making it fully 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?

Schema coverage is 100% and the description repeats default and max values for 'days' and 'window'. It adds minor value by explaining 'snapshot family' context, but does not significantly enhance understanding beyond the schema.

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

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

The description clearly states 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots' and answers a specific question about edge duration. It distinguishes itself from sibling 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 context on when to use the tool (e.g., different trading significance for fresh vs old edges) and includes limits (60-day TTL), but does not explicitly state when not to use it or list alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant behavioral context: it walks the order book ladder, returns verdicts like clean/degraded/cannot_fill, and warns about the dominant loss mode (partial basket fills converting arb to unhedged directional position). No contradictions.

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

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

The description is well-structured with clear sections for single-market and basket modes. Each sentence provides unique value, but the length is justified by the tool's complexity. Could be slightly more concise, but all information is 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?

Given the complexity and absence of an output schema, the description fully covers the return fields for both modes (top_of_book, vwap_fill_price, slippage_pp, verdict, etc.) and explains the risks. It is complete for an agent to understand 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%, and the description goes beyond by explaining parameter interpretation in both modes (e.g., size_usd as settlement notional in basket mode, default values and clamps). This adds meaning beyond the schema alone.

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

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

The description clearly states the tool performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It explicitly says to use it before acting on polymarket_arbitrage signals or large trades, differentiating it 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?

Provides explicit when-to-use guidance (before arb signals or trades >$500) and mode selection (requires market or event). It also explains the default behavior for side and size_usd in each mode, leaving no ambiguity about 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 and idempotent behavior. Description adds extensive behavioral details: response structure, safety fields, temporal alignment, skipped comparisons. 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 key info front-loaded. Slightly verbose in later sections but each sentence adds value for complex 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?

No output schema, yet description covers response fields, safety warnings, temporal alignment, and skipped comparisons. Comprehensive for a complex tool.

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

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

Schema coverage is 100% with clear descriptions. Description repeats but adds context on mode behavior and override logic, reinforcing semantics.

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

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

The description clearly states the tool's purpose: computing cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue 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?

Explicit guidance on two modes (topic shortcuts and explicit tickers), plus warnings about compatibility and when spreads are meaningless. States that most pre-mapped topics return warnings, setting realistic expectations.

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 the tool as readOnly, idempotent, and non-destructive, so the safety profile is clear. The description adds minor behavioral context by stating the body is a PxWeb query object and that the path should be bare. It does not contradict annotations.

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

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

Two short sentences. No redundant information. The verb is front-loaded. Every word serves a 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?

The tool has no output schema, but annotations (openWorldHint) and schema describe the request format. The description doesn't explain the response format or behavior, but the schema's body parameter description mentions json-stat2 format. Overall, with good annotations and schema, it is fairly complete, though a brief note on response would improve.

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 both parameters with descriptions. The description adds value by clarifying that body is a PxWeb query object, which is more specific than the schema's generic 'object' type. It also provides an example for path. Schema coverage is 100%, so the description 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 a specific verb 'Pull data' and identifies the resource 'table'. It clearly indicates the tool is for retrieving table data. However, it does not distinguish from sibling tools like table_meta, which likely provides metadata, but this is not stated.

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 usage tip about the path format (no .px suffix). It does not specify when to use this tool vs alternatives like table_meta for metadata or other search tools. The context of when to use is implied but not explicit.

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

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

Annotations already indicate read-only and idempotent. Description adds scoping to identifier and behavior of listing vs retrieving, providing useful context beyond annotations.

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

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

Description is a single paragraph but contains all necessary info without redundancy. Could be slightly more structured, but it's concise 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 no output schema and one optional parameter, description fully covers behavior, scoping, and relationship to sibling tools. No gaps remain.

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

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

Schema has 100% coverage with description for the single parameter. Description elaborates on parameter use with examples, fully clarifying when to provide a key vs omit.

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 saved values or lists keys, with specific examples like ticker or notes. It distinguishes from sibling tools '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?

Description advises when to use (look up stored context) and implies alternatives (save with remember, delete with forget). Could be more explicit about 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?

The description states that mark_read:true flags events as read, implying a mutation. However, annotations declare readOnlyHint=true and destructiveHint=false, a direct contradiction. This misleads the agent about side effects.

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

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

Three sentences, front-loaded with purpose, no wasted words. Efficiently delivers key 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 tool with no output schema, the description covers return fields, parameter usage, and side effects. Missing details on limit behavior and unread_only, 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%, so baseline is 3. The description adds slight value by providing an example filter type ('sec_8k') and explaining mark_read, but does not cover all parameters (limit, unread_only).

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

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

The description clearly states the verb 'Pull fired events' and the resource 'subscription feed alerts'. It distinguishes from siblings like 'list_subscriptions' and 'recent_changes' by specifying it returns alerts with source, citation_uri, and raw payload.

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

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

The description mentions 'Polls work fine' indicating suitability for repeated calls, and provides an alternative access method (GET endpoint). It does not explicitly state when not to use, but context is clear.

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

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

Annotations already state readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable context: fan-out to multiple sources, fallback mechanism (GDELT→GNews), soft-fail for USPTO, and accepted date formats. No contradiction with annotations.

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

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

The description is a single paragraph but densely packed with information. It front-loads the purpose and then covers sources, parameters, and return format. Every sentence adds value, though a bit more structure (e.g., bullet points) could improve readability.

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

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

Given the complexity (multi-source, fallback, soft-fail) and absence of output schema, the description is remarkably complete. It explains return structure (changes[] grouped by source + total_changes + citation URIs), distinguishes from entity_profile, and covers all parameters comprehensively.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond schema: explains that 'type' only supports 'company', gives examples for 'since' (ISO date or relative shorthand), and specifies that 'value' can be a ticker or CIK. This elevates the score above baseline.

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

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

Description uses specific verbs and resources: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It lists three data sources (SEC EDGAR, GDELT→GNews, USPTO) and explicitly distinguishes from the sibling tool entity_profile, making the purpose crystal 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?

Description gives clear query examples for when to use ('What's new with X', 'latest on Y', etc.) and advises using entity_profile for static profile instead. It lacks explicit when-not-to-use for all siblings but provides a strong usage signal.

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 and destructiveHint=false, which description supports. Description adds context about scoping and retention (authenticated persistent, anonymous 24h). However, it does not explicitly state that storing the same key overwrites the 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 multi-sentence but efficiently conveys purpose, usage, and storage behavior. It is front-loaded with the main action, though slightly verbose with examples and retention details.

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 store with no output schema, the description effectively covers purpose, usage, storage scope, retention, and links to related tools. No gaps given 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 clear descriptions for 'key' and 'value'. The description adds no additional semantic meaning beyond the schema, meeting baseline expectations.

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 the verb 'Save' with the resource 'data' and clearly indicates it stores information for later reuse. It distinguishes itself from siblings like 'recall' and 'forget' by focusing on storage.

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: 'when you discover something worth carrying forward' and provides examples. It also references alternatives: 'Pair with recall to retrieve later, forget to delete.'

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 insight by disclosing that each call cascades through several internal endpoints, effectively replacing manual steps. It also mentions auto-disambiguation for company type, 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?

The description is well-structured: it opens with concrete examples, states the purpose, provides usage guidance, and details each supported type. Every sentence is informative and without redundancy, making it both concise and comprehensive.

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 moderate complexity (two entity types, internal cascading), the description covers all necessary aspects: when to use, input formats, return values, and even citation URIs. Although no output schema exists, the description effectively documents the outputs, achieving completeness.

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 significantly enriches parameter meaning by detailing the return values for each type (ticker, CIK, RxCUI, etc.) and clarifying acceptable input formats (e.g., ticker, CIK, or name for company). This goes well beyond the schema's minimal 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 'resolve' and resource 'entity', uses example queries to illustrate purpose, and distinguishes itself as the first step for obtaining IDs needed by other tools. It effectively differentiates from siblings like 'entity_profile' and 'compare_entities' by focusing on name-to-ID resolution.

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

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

Explicitly states 'Use FIRST whenever you have a name but need an ID.' This provides clear guidance on when to use the tool and implies alternatives when an ID is already known. It also notes that the tool replaces multiple manual lookups, adding practical 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 cover safety (readOnly, idempotent, non-destructive). The description adds that it probes each entity and returns a ranked list with score, confidence, signal density, which is useful behavioral context beyond annotations.

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

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

Three sentences with no wasted words. First sentence states purpose, second details process and output, third gives use case. Front-loaded with key 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?

No output schema, but description explains the returned ranked list with attributes. Parameters are well described. Given the tool's multi-entity probing complexity, the description is fairly complete.

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

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

Schema coverage is 100%, but description adds nuance: first entity is the 'subject', models default to free, and _apiKey is conditional. This adds value beyond the schema descriptions.

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

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

The description clearly states it compares AI visibility across multiple entities, probes each with ai_visibility_check, and ranks by score. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general 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 a concrete use case ('competitive AI-marketing audits') and an example question. However, it does not explicitly state when not to use or mention 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 readOnly and idempotent. The description adds crucial behavioral details: partial failures, bundlephobia timeout (5-30s), graceful degradation with sources_failed field. 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?

Front-loaded with core purpose in first sentence. Every sentence adds value, though the description is verbose. Could be slightly tighter, but structure is logical 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?

Despite no output schema, the description fully specifies return fields (summary block with exact field names), per-advisory details, links, and alternative versions. It covers edge cases like partial failures and timeouts, making it complete for agent use.

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

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

Schema coverage is 100%. The description adds practical context: scoped packages accepted (not in schema), version defaults to latest. This enriches beyond schema baseline of 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 composite function: a single call to evaluate npm packages using deps.dev and bundlephobia. It uses specific verbs ('fans out','returns') and distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on dependency 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?

Explicitly instructs when to use: 'when an agent asks is X safe / popular / small or what does adding lodash cost me'. It also notes ecosystem limitations (npm only v1) and directs to deps.dev:version for other ecosystems, providing clear 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 mark readOnlyHint and idempotentHint, but the description adds significant behavioral details: uses BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char cap with truncation flag, and returns offsets for verification. This goes well beyond annotations.

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

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

The description is front-loaded with purpose, then usage, then technical details. Every sentence adds value; no wasted words. Approximately 5 sentences covering all necessary aspects.

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 mentions return format (passages with offsets and similarity scores). Parameter count low (3) with clear descriptions. Completely covers what an agent needs to use the tool effectively.

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

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

Schema coverage is 100%, so baseline 3. The description adds examples for query (e.g., 'supply-chain risk') and clarifies text is the document to search and limit is max passages. This adds meaning 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 tool does semantic search inside a fetched record, with specific examples (SEC 10-K, article) and distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded. It uses a specific verb ('search inside') and resource ('a fetched record').

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

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

The description explicitly says when to use: when the record is too big for the prompt, to save context. It mentions pairing with ask_pipeworx_grounded as an alternative workflow. However, it does not explicitly state when not to use or list exclusions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context about the hierarchy (folders vs. tables) and how to interpret responses, 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?

Two concise sentences front-loading the essential information. Every word earns its place with no repetition or 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?

Despite lacking an output schema, the description adequately explains return types ('l' and 't'). The tool is simple with one optional parameter, and the context covers necessary aspects for navigation.

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 for the 'path' parameter matches the schema exactly (both mention 'Sub-path under the API base...'). The description adds no new meaning beyond the schema, so baseline 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 tool navigates a subject tree, identifies root behavior (listing databases like OSP_PUB), and explains entry types ('l' for folders, 't' for tables). This distinguishes it from siblings like 'query_table' which targets specific tables.

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

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

The description provides clear context for usage (empty path for root, sub-paths for drilling), but does not explicitly state when not to use this tool or compare it to alternatives. Usage is implied rather than explicit.

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

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

Annotations already declare idempotentHint true and readOnlyHint false. The description adds significant behavioral details: account requirement, SMS cap (10/day), webhook auto-disable after 10 failures, and return of subscription id. No contradictions.

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

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

The description is well-structured, starting with the main purpose, then prerequisites, type details, and delivery channels. While somewhat verbose, every sentence provides unique value. Could be slightly tightened.

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 all major aspects: purpose, requirements, type-specific parameters, delivery channels, and return value. Lacks explicit error handling or default behavior, but given the schema richness and annotation coverage, it is complete enough for effective tool use.

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

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

Schema coverage is 100%, but the description adds meaningful examples and clarifications for each parameter, especially for type-specific params (e.g., sec_8k, polymarket_edge) and delivery options (webhook signing secret, SMS verification). This greatly enhances agent 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 clearly states the tool creates a subscription ('Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.'), which distinguishes it from sibling tools like list_subscriptions (list) and unsubscribe (delete).

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 requires a Pipeworx OAuth account, and provides detailed examples of supported subscription types and delivery channels. It implicitly tells when to use (to subscribe to events) but lacks explicit 'when not to use' guidance. Overall clear context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: it is the onboarding entry point, returns category-bucketed examples, and can be focused via topic. No contradiction; description enriches understanding beyond annotations.

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

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

The description is concise, front-loaded with common trigger phrases, and every sentence adds value. It covers purpose, usage, and return format in two well-structured sentences 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?

Given no output schema, the description adequately explains the return type (category-bucketed example questions with tool+argument shapes). It also provides usage context and topic focus. For a simple tool with one optional param and clear annotations, this is fully 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 one optional parameter. The description adds value by explaining usage patterns ('Call with no arguments for the full spread, or pass `topic` to focus') and lists possible topic values (finance, pharma, etc.) which are not defined as enums in the schema.

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

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

The description clearly states the tool returns category-bucketed example questions with tool+argument shapes. It uses specific verbs ('returns') and resource ('example questions'), and distinguishes itself from sibling tools by being the onboarding entry point. No tautology or vagueness.

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 FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. It also mentions calling with no arguments or with a specific topic. While it doesn't explicitly list exclusions, the context is clear and the sibling list provides 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 readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds minimal behavioral info beyond that.

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 short, front-loaded sentences with zero waste. Every word serves a 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 one-parameter tool with no output schema, the description covers purpose and input format well, though it could hint at return structure.

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 an example. The description adds a crucial format constraint ('no .px suffix'), enhancing agent 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 clearly states the tool provides 'Table definition (dimensions, valid values)' and specifies input format, distinguishing it from querying tools like query_table.

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

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

The description gives a format hint ('Use the bare table path with no .px suffix') but does not explicitly state when to use vs. alternatives or provide exclusions.

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

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

Annotations indicate readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that ownership is enforced and the row is deactivated (not deleted), which aligns with and supplements the annotations.

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

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

Two sentences with no wasted words. The first sentence states the action, and the second adds key behavioral constraints. Highly 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?

For a simple tool with one parameter and no output schema, the description covers purpose, ownership, and side effect (deactivation). It is fully complete given the tool's complexity and the presence of 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 input schema has one parameter 'id' with description 'Subscription id (uuid) returned by subscribe.' Schema coverage is 100%, so the description does not need to add extra parameter info. The description adds no further semantics.

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

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

The description states 'Cancel a subscription by id' with clear verb and resource. It mentions deactivation, which distinguishes from delete operations, but does not explicitly differentiate from sibling tools like 'unsubscribe' vs 'delete'.

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

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

The description includes ownership enforcement ('you can only cancel your own subscriptions') as a usage constraint, and notes that the row is deactivated to preserve history. However, it does not mention when to use this tool vs alternatives like 'list_subscriptions' or 'subscribe'.

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, idempotentHint, openWorldHint, and destructiveHint false. The description goes beyond by detailing the return verdict types, extracted structured form, actual value with citation, percent delta, and domain limitations (US public company financials). 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 front-loaded with trigger phrases and structured logically. It is fairly concise given the amount of information conveyed, though slightly verbose with examples. It could be streamlined 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 lacking an output schema, the description fully explains return values and constraints. It covers the tool's behavior, domain, and efficiency benefits, making it complete for an agent to invoke correctly.

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

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

The input schema has 100% coverage for the single parameter 'claim', which already describes it as a natural-language factual claim. The description adds examples but does not significantly enhance 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: natural-language claim verification against authoritative sources, specifically for company-financial claims. It provides trigger phrases and distinguishes from siblings by specifying its unique function.

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 ('whenever the agent needs to check factual correctness') and provides example input patterns. However, it does not mention when not to use or list alternative tools from the sibling list, which would have improved clarity.

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