Dryad

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that probes can be from multiple models, requires BYO key for Anthropic, and returns per-model scores with confidence and signals. 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 two sentences plus a return summary, front-loading the core action. No extraneous words, every sentence adds value.

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

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

Given the tool's complexity (multi-model, optional API key, no output schema), the description covers the main functionality, default behavior, and return structure. It could include prerequisites like API key handling but is sufficient for agent decision-making.

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

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

Schema description coverage is 100%. The description adds context beyond the schema: default model, conditional need for _apiKey, and the role of context in disambiguation. It does not repeat schema but provides usage guidance.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100). It specifies the default model and optional models, distinguishing it from sibling tools like ask_pipeworx and scan_competitor_ai_presence.

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

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

The description lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to provide an API key for Anthropic. However, it does not explicitly contrast with similar siblings or state when not to use 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 indicate read-only, non-destructive, idempotent. Description adds that it returns structured answers with stable URIs and routes questions. No contradiction with annotations.

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

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

Description is compact (5-6 sentences), front-loaded with key guidance, includes examples, and no extraneous text.

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

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

Given the tool's complexity and lack of output schema, the description covers input format, use cases, and return type (structured answer with citations). No gaps.

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

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

Schema coverage is 100% with aliases and examples. Description does not add specific parameter details beyond natural language question, so baseline of 3 is appropriate.

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

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

Description clearly states the tool answers questions about current/historical data from specific authoritative sources (SEC, FDA, etc.), distinguishes from web search, and mentions routing among 3,567 tools. Verb+resource is specific and differentiates from siblings.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and lists when to use ('what is', 'look up', etc.) with multiple examples. 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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description reveals behavioral traits such as an extra LLM call, explicit refusal reasons, and that the answer is derived solely from tool results. 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 paragraph of about six sentences, front-loaded with the core purpose. While dense with useful information, it could be more structured (e.g., bullet points) for easier scanning, but no sentences are wasted.

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 covers the return format ({answer, evidence, confidence...}) and possible refusal reasons. It also explains the extra LLM call cost, making it complete for a single-query grounded answer tool.

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

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

With 100% schema coverage, the description adds minimal parameter-specific meaning beyond what the schema already provides. The in-schema descriptions for question and its aliases are sufficient.

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

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

The description clearly defines the tool as a 'Hallucination-resistant answer mode' for high-stakes reads, specifying that it extracts answers only from tool results and returns evidence. It distinguishes itself from the sibling 'ask_pipeworx' by detailing the grounded extraction and refusal mechanism.

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

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

The description explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when to prefer the sibling tool ('prefer ask_pipeworx for casual lookups'), including a 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?

The description provides extensive behavioral details beyond the annotations, including parallel fan-out, response shapes, resolver contract (market_match_confidence, alternatives), parent event extraction, news fallback handling, and safety mechanisms for low-confidence and illiquid markets. This far exceeds the minimal information from annotations (readOnlyHint, idempotentHint).

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

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

The description is long but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). It front-loads the core purpose and usage in the first few sentences. While verbose, every sentence adds necessary detail for a complex tool, earning it a 4 rather than a 5.

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 (3 parameters, no output schema), the description is exceptionally complete. It explains response fields, all edge cases (closed markets, low-confidence matches, illiquid spreads), resolver contract, parent events, news fields, and safety measures. Without an output schema, the description carries the full burden and does so thoroughly.

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

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

The input schema covers all 3 parameters with descriptions, and the tool description adds meaningful context: examples of market input types, explanation of depth values (quick vs thorough), and guidance for include_raw (when to use true vs false). This adds significant value 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's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It provides specific verb-resource pairs (pass a slug, URL, or question text) and includes usage examples, distinguishing it from sibling tools like polymarket_arbitrage or ask_pipeworx by focusing on comprehensive single-bet 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?

The description explicitly says to use for 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'. It also describes blocking conditions (low-confidence resolutions, closed markets) and advises to inspect market_match_confidence before trusting analysis. However, it does not directly compare with sibling tools or state when not to use it, which would make it a 5.

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

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

The description goes beyond annotations by explaining specific behavioral traits: it pulls the latest 10-K data for companies (with correct handling of off-calendar fiscal years), FAERS counts for drugs, returns paired data with citation URIs, and sorts results by the primary metric. 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 detailed but well-structured, with front-loaded examples and clear sections. It conveys essential information without unnecessary verbosity. A few minor repetitions could be trimmed, but overall it is concise enough.

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 describes the output format (paired data + citation URIs) and sorting behavior. It also implicitly sets expectations for what the tool can and cannot do, which is sufficient for its read-only nature.

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

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

Schema coverage is 100%, and the description adds significant semantic value by explaining what each type entails (company vs drug data sources) and edge cases like off-calendar fiscal years. It also clarifies the sorting behavior, which the schema does not capture.

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

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

The description specifies the tool's purpose: side-by-side comparison of 2–5 companies or drugs. It provides examples of natural language triggers ('compare X and Y', 'X vs Y') and clearly distinguishes from single-entity lookups, which is important given sibling tools like entity_profile.

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

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

The description explicitly advises to prefer this tool over sequential single-pack lookups when comparing entities. It also details what data is pulled for each type (company vs drug) and notes the maximum count (2–5). While it doesn't provide explicit exclusions, the usage 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?

Describes what the tool returns: top-N relevant tools with names, descriptions, and full input schemas with examples. Adds value beyond annotations by detailing output and that no second schema lookup is needed.

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

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

Extremely concise: two sentences plus a list of example domains. Front-loaded with purpose and usage, every sentence adds value.

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

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

Despite no output schema, the description explains exactly what is returned and why to use it first. Fully covers the tool's role in the agent's workflow.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds that aliases like task, q, description, search are accepted, but doesn't add significant new semantics beyond the schema.

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

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

The description clearly states the tool finds tools by describing data or task. It distinguishes itself from sibling tools by being the discovery tool to call first.

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: browse, search, look up, discover tools. Says 'Call this FIRST' when many tools are available, making the usage context and alternatives clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context: fans out across multiple sources, details return fields, and notes USPTO API sunset soft-fail.

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

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

The description is verbose but well-structured: starts with example queries, followed by usage guidance, then details on fan-outs and parameters. It could be more concise but is not wasteful.

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, no output schema), the description thoroughly explains what is returned (cik, recent_filings with URIs, fundamentals, patents, news, LEI) and covers limitations and fallbacks.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds clarity on value format (ticker vs zero-padded CIK) and that names are not supported, exceeding schema information.

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

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

The description uses specific verbs like 'tell me about', 'research', 'brief' and clearly states the tool provides a 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes from sibling tools like resolve_entity and 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 advises 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also clarifies when not to use: names not supported, use resolve_entity first.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. Description adds context about clearing stale/sensitive data but does not significantly extend beyond annotations.

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

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

Two sentences, front-loaded with action, no unnecessary words.

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

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

Simple one-param tool with no output schema. Description, combined with annotations, provides complete context for usage and behavior.

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

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

Schema coverage is 100%, with the 'key' parameter described. Description mentions 'by key' but adds no 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 states 'Delete a previously stored memory by key' which is a specific verb and resource. It also mentions pairing with 'remember' and 'recall', distinguishing it as the deletion 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?

Explicitly says when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. It implies alternatives by referencing 'remember and recall' for storage and retrieval.

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

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

Annotations already indicate safe read-only, idempotent behavior. The description adds behavioral details: fetches page, extracts title/description/key links, emits llms.txt markdown. 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 compact two-sentence paragraph that front-loads the tool's purpose and output. Every sentence adds value with no redundancy.

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

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

For a simple tool with 2 parameters and no output schema, the description covers input (url, max_links), process (fetch, extract), and output (llms.txt blob). It is fully sufficient for understanding and using the 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 descriptions already explaining url and max_links. The description does not add new parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the exact output format and purpose. It distinguishes itself from siblings like ai_visibility_check which focuses on visibility analysis, not 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?

The description lists specific use cases (getting client's site indexed, drafting for own project, auditing competitors) which guide when to use. However, it does not explicitly mention when not to use or provide alternatives, but 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 already cover safety (readOnly, idempotent). Description adds 'Keyless' (auth req), lists return fields (title, authors, etc.) and download URL, going beyond annotations.

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

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

Two concise sentences, front-loaded with purpose, no fluff. Every sentence adds value.

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

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

Simple tool (1 param, read-only). Description covers return fields despite no output schema. Adequately distinguishes from siblings.

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 doi parameter 100%. Description adds value by showing example DOI formats with/without prefix, adding clarity beyond schema.

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

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

Description clearly states action ('Fetch') and resource ('single Dryad dataset by its DOI'). Distinguishes from sibling 'search_datasets' by specifying single dataset lookup.

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

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

Implies usage for fetching one dataset by DOI; mentions 'Keyless' indicating no auth needed. Lacks explicit alternatives 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 provide readOnlyHint=true and destructiveHint=false. The description adds the specific return fields but does not add significant behavioral context beyond what annotations convey. No contradictions.

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

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

Two sentences, no wasted words. The first sentence states the action and return fields; the second provides usage context. Efficient 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?

For a simple list tool with one optional parameter and no output schema, the description adequately covers return fields and use cases, making it complete for the agent.

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

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

Schema coverage is 100% with the 'include_inactive' parameter fully described in the schema. The description does not add new parameter meaning beyond what's in the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states it lists the caller's active subscriptions, specifying the return fields (id, type, params, etc.), which differentiates it from sibling tools like subscribe and unsubscribe.

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

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

The description explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', providing clear when-to-use guidance. It lacks explicit when-not-to-use statements but the context is sufficient.

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

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

Disclosures include rate limit of 5 per identifier per day, that it's free and doesn't count against quota, and that the team reads digests daily affecting roadmap. Annotations are generic false values, so description carries full burden and exceeds expectations.

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 well-structured sentences with no redundancy. Front-loads purpose, then usage guidelines, then behavioral details. Every sentence earns its place.

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

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

Covers all necessary aspects: purpose, when to use, how to write, behavioral traits, and limitations. No output schema is fine for a feedback tool; description adequately explains impact and process.

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, but description adds value by explaining how to write the message (describe in terms of Pipeworx tools/packs, avoid pasting prompts) and clarifying enum values with examples. This meaningfully augments 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 is for providing feedback (bug, feature, data_gap, praise) using specific verbs like 'tell' and 'broken, missing, or needs to exist'. It distinguishes from sibling tools like ask_pipeworx by focusing on reporting issues rather than asking questions.

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 the tool: for bugs, features, data_gaps, or praise. Provides exclusions: 'don't paste the end-user's prompt'. Implicitly guides away from using this for general questions, and mentions rate limits and quota exemption.

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

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

The description adds significant behavioral context beyond annotations: it reveals the data source (CF analytics-engine), confirms no PII, specifies caching duration (5min-1h), and clarifies the data is self-aggregating. Annotations already show readOnly and idempotent hints, but description enriches the agent's understanding of data freshness and provenance.

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

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

The description is front-loaded with the core function in the first sentence. Subsequent sentences add structured value (use cases, technical details). It is not overly verbose; each sentence contributes unique information. A slight improvement would be to group the caching note more tightly.

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

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

For a simple tool with one optional enum parameter and no output schema, the description covers all necessary aspects: purpose, use cases, behavioral traits (caching, no PII), parameter semantics, and data source. An agent has sufficient information to decide when and how to invoke this tool.

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

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

The single parameter 'window' is well-documented in the schema with enum values and a baseline description. The tool description adds value by explaining the trade-offs between shorter and longer windows ('Shorter windows surface what's hot right now; longer windows show steady-state demand.'), which helps agents select the appropriate value.

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

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

The description explicitly states it returns top tools, top packs, and total call volume over recent windows. It uses specific verbs ('Returns') and clearly identifies the resource (trending data from Pipeworx). This distinguishes it from sibling tools like 'discover_tools' which likely list all tools rather than trending ones.

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 three explicit use cases (discovering hot data sources, confirming canonical tools, checking alignment). It also explains how to choose between different window durations. However, it does not explicitly state when not to use this tool or mention alternatives when the user needs static lists rather than trends.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: explains two modes, semantic anchor (Jaccard similarity), partition filter, and response structure. No contradiction with annotations.

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

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

The description is thorough and well-structured with clear sections, but slightly verbose. Every sentence adds value, but could be trimmed for efficiency without losing essential 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 complex arbitrage detection tool with two modes and no output schema, the description covers algorithms (monotonicity, partition check), filtering criteria, and response fields. It is complete enough for an AI 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?

Schema coverage is 100% but the description significantly expands on each parameter: explains event mode with examples (e.g., 'fed-decision-may-2026'), topic mode with seed questions, and details the algorithmic behavior of each. This adds substantial meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes from sibling tools like polymarket_edges and polymarket_kalshi_spread by detailing specific detection methods and modes (single-event and cross-event).

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?

Specifies required arguments ('REQUIRES one of event or topic'), recommends event mode for specific markets and topic mode for cross-event scanning, and explains when each is appropriate. It explicitly states that calling with no arguments fails, providing clear usage guidance.

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

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

The description extensively details behavioral traits beyond annotations, including caching (1h KV-level), slippage modeling, edge calculation, segment logic (e.g., overround partitions), and diagnostic outputs. It adds significant transparency about internal mechanics and limitations, with no contradiction to 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 lengthy and dense with technical details, which is necessary for a complex tool but reduces conciseness. It is structured with section headers (segments, knobs, response) but could be more succinct 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 no output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, diagnostics) and covers all input parameters. It provides enough context for an agent to understand inputs, outputs, and edge cases without missing critical information.

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

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

Schema coverage is 100% with detailed parameter descriptions. The tool description adds marginal value by explaining inter-parameter relationships (e.g., min_partition_leg_kelly vs min_kelly) and providing context like slippage assumptions, but does not fundamentally expand 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 verb ('Scan', 'return') and resource ('opportunities where Pipeworx data disagrees with market price'). It explicitly positions the tool for daily betting decisions, distinguishing it from siblings like 'polymarket_arbitrage' and 'polymarket_kalshi_spread' by focusing on Pipeworx data disagreements.

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 usage context: 'Built for 'what should I bet on today'' and explains when to avoid (Fed bets excluded due to data unreliability). It does not explicitly name sibling tools as alternatives but implies differentiation by detailing unique outputs like model families and segments.

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 extensive behavioral details: how spreads are calculated, compatibility warnings, temporal alignment, skipped cross types, and response structure. No contradiction with annotations.

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

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

The description is front-loaded with the core purpose and modes. However, it is verbose, with detailed explanations of safety fields and counters. Could be slightly more concise, but structure is logical.

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

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

Given no output schema, the description fully explains return values (leg-by-leg prices, matched spread, compatibility_warning, temporal_alignment, skipped types). It covers edge cases and warning scenarios, making the tool's behavior complete 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% with descriptions for all three parameters. The description adds meaning by explaining the topic parameter lists the 10 shortcuts, and explicit overrides allow custom pairings. This goes beyond the schema's simple property descriptions.

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

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

The description clearly states it is a cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on specific venues and outcome equivalence. The two modes (topic shortcuts and explicit ticker/slug) are explicitly described.

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

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

The description explains when to use this tool (to find spreads for identical outcomes across venues) and provides guidance on interpreting results (compatibility_warning, temporal alignment). It warns that most pre-mapped topics are not tradeable. However, it could explicitly contrast with siblings like bet_research or compare_entities.

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 behavior. Description adds scoping to anonymous IP or account ID, and the ability to list all keys by omitting the argument, which is beyond annotation info.

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 extraneous words. Front-loads purpose, then adds usage context and pairing instructions. Every sentence adds value.

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

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

Tool is simple with one optional parameter, good annotations, and no output schema. Description covers retrieval mechanism, listing behavior, scoping, and sibling tools. No gaps for typical 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% with description for the 'key' parameter. Description restates the omit-to-list behavior already in schema, adding no new semantic depth 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 uses specific verbs 'retrieve' and 'list', clearly names the resource (saved values via remember), and distinguishes from sibling tools like remember and forget by stating its role.

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

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

Explicitly states when to use: to look up previously stored context. Mentions pairing with remember and forget, and scoping to identifier. Lacks explicit when-not guidance but sufficient for a retrieval 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 readOnlyHint and idempotentHint. The description adds context about return fields (source, citation_uri, raw payload), persistence of the feed, and polling behavior. 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?

Four well-structured sentences: purpose, contents, filtering/mark_read, alternative access. No unnecessary words, 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?

Given the tool's simplicity (5 params, no output schema), the description covers all needed aspects: what it returns, how to filter, how to use mark_read, and alternative access. Complete and self-contained.

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

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

Schema description coverage is 100%, so baseline is 3. Description partially repeats schema (filter by type and since), but adds context for mark_read behavior. Does not significantly increase understanding beyond what schema provides.

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

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

The description uses the verb 'Pull' and clearly specifies the resource: 'fired events from your subscription feed.' It distinguishes this tool from siblings like list_subscriptions by describing the content (source, citation_uri, raw payload) and filtering options.

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

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

Explicitly describes filtering by type and since, explains mark_read functionality for incrementally fetching newer events, and mentions polling works fine. Provides an alternative URL for scripts/dashboards. No explicit when-not-to-use, but context is clear.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral details: fans out to multiple sources, handles GDELT→GNews fallback, soft-fails for USPTO patents until reactivated, and returns structured output with citation URIs. This goes beyond annotations by explaining source-specific behaviors.

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 common query phrases, then details sources, parameters, output, and alternatives. It is well-structured and informative, though slightly lengthy. Could be more concise but efficiently conveys necessary 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 no output schema, the description adequately explains the return format (changes grouped by source, total_changes count, pipeworx:// citation URIs). It covers all parameter details, source fallbacks, and directs to an alternative tool. Complete for a tool with 3 required parameters and moderate complexity.

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

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

Schema coverage is 100%, and the description adds significant context: 'since' accepts ISO dates or relative shorthand (e.g., '7d', '30d', '3m', '1y'), 'value' can be ticker or CIK, and 'type' is limited to 'company'. This enhances understanding beyond the schema's basic descriptions.

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

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

The description clearly states the tool provides a change feed for a company across multiple sources (SEC, news, patents) over a time window. It explicitly distinguishes from the sibling 'entity_profile' tool, which focuses on static profiles.

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

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

The description gives explicit when-to-use guidance: 'Use entity_profile instead when you want the static profile...' It also explains the tool's parallel call nature and fallback behavior (GDELT→GNews), helping the agent decide contextually.

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. Description adds valuable context: scoping by identifier, persistence differences between authenticated (persistent) and anonymous (24 hours). 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 three sentences, front-loads the core purpose, and every sentence adds value. No wasted words.

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

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

For a simple key-value storage tool, the description covers purpose, usage, behavior, and parameter examples. Annotations cover safety. No output schema needed. Fully adequate.

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

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

Schema coverage is 100% with good descriptions for key and value. Description adds value by providing examples (ticker, address, preference) that illustrate usage beyond the schema's generic descriptions.

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

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

Description clearly states the verb 'Save' and the resource 'data' as key-value pairs for later reuse. It distinguishes from siblings 'recall' and 'forget' by mentioning them explicitly.

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 specifies when to use: 'when you discover something worth carrying forward'. Mentions pairing with recall and forget, but no explicit when-not scenarios. Clear enough for 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 read-only, idempotent, open-world, and non-destructive behavior. The description adds that it cascades through several lookup endpoints internally and auto-disambiguates for companies, providing 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?

The description is front-loaded with example queries and a clear purpose statement. It uses bold for 'SUPPORTED TYPES' which aids readability. While slightly verbose with examples, every sentence adds value and no information is redundant.

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 2 parameters and no output schema, the description fully compensates by detailing returned fields, citation URIs, auto-disambiguation, and that it replaces multiple lookups. It is complete and self-contained.

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 by explaining that for company, value can be ticker, CIK, or name; for drug, brand or generic name. It also details the output fields for each type, which is not 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 resolves names to canonical/official identifiers, providing specific examples like ticker, CIK, RxCUI. It explicitly says 'Use FIRST whenever you have a name but need an ID.' This distinguishes it from sibling tools like entity_profile which provide more detailed profiles.

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

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

Explicit guidance is given: 'Use FIRST whenever you have a name but need an ID.' The description also mentions that it replaces 2-3 manual lookups, implying efficiency. It does not explicitly list when not to use or alternatives, but the context is clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it is safe and non-destructive. The description adds valuable context: it probes with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. No contradictions and no missing critical behavioral details.

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

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

The description is two substantive sentences plus a brief usage example. It is front-loaded with the main action, and every sentence adds value. No wasted words, and the structure is easy to parse.

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

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

Despite having no output schema, the description explains the return value structure (ranked list with score, confidence, signal density). It also describes the probing mechanism and the use of the sibling tool ai_visibility_check. For a tool that compares up to 8 entities, this is complete and actionable.

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

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

Schema coverage is 100%, baseline 3. The description adds meaning beyond schema by explaining that the first entity in the 'entities' array is treated as the subject for narrative, and that 'context' is a shared context applied to every probe. It also clarifies that '_apiKey' is optional and only needed if 'anthropic' is in models, which aids correct invocation.

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, ranks them, and surfaces most/least recognized. It includes a concrete use case ('does Claude know about us as well as our competitors?') and distinguishes from sibling tool ai_visibility_check by being a multi-entity 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 explicitly states it is 'useful for competitive AI-marketing audits' and specifies that the first entity is treated as the subject. While it does not list when not to use or explicitly name alternatives, the context makes the appropriate use case clear. Slight improvement would be to mention ai_visibility_check as an alternative for single-entity checks.

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

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

Discloses that bundlephobia first measurement can take 5-30s, partial failures list in sources_failed, and falls back gracefully. Adds significant context beyond annotations (readOnly, openWorld, idempotent).

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

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

Single dense paragraph that front-loads purpose and key details. Effective but could be broken into smaller chunks for easier scanning. No wasted 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?

No output schema, so description explains return format (summary block, advisories, links, alternatives). Covers limitations, timing, and fallback behavior. Adequate for 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% with descriptions. Description adds that scoped packages are accepted and version defaults to latest, but does not significantly enhance meaning beyond schema. Baseline 3 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?

Clearly states it is a composite check for npm packages, fanning out to deps.dev and bundlephobia, with a specific goal: 'should I add this npm package to my project'. Distinct from sibling tools like scan_competitor_ai_presence.

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

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

Explicitly advises use when agent asks about safety, popularity, size, or cost. Mentions NPM ecosystem only in v1 and graceful degradation, but doesn't provide explicit when-not-to-use scenarios.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by noting the tool is 'keyless' (no authentication) and details the return fields, which go beyond the annotations. No contradictions with annotations.

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

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

The description is concise with two sentences, front-loaded with the tool's purpose. Every sentence adds value without redundancy.

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

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

Despite lacking an output schema, the description explicitly lists return fields (DOI, title, authors, truncated abstract, etc.) and notes the keyless access, making it complete for a search tool. Moderate complexity is well-covered.

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

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

Schema coverage is 100% (all parameters described in schema). The description adds context by explaining that the query searches over specific fields (titles, abstracts, authors, keywords) and lists return fields, providing meaning beyond the schema's basic descriptions.

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

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

The description clearly states the tool searches Dryad, a curated repository of open research datasets, with full-text query over titles, abstracts, authors, and keywords. It specifies the return fields (DOI, title, authors, truncated abstract, etc.), making the purpose highly specific and distinguishable from sibling tools like 'search_within' or 'get_dataset'.

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

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

The description explains what the tool does and mentions it is 'keyless' (no authentication required), but does not explicitly state when to use it versus alternatives or when not to use it. It provides implicit context for usage but lacks exclusion criteria.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false. Description adds valuable behavioral details: underlying mechanism (BGE-base-en embeddings, cosine similarity, 500-char windows), limits (200K chars cap with truncation flagging), and output characteristics (passages with offsets for verification). 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 purpose, then methodically covers usage, pairing, and technical details. Every sentence adds value with no wasted words.

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

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

For a semantic search tool with 3 params and no output schema, the description fully explains inputs, mechanism, limits, and output format (passages with offsets and scores). It also provides integration context with sibling tools.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning by explaining truncation behavior and providing query examples, though the schema already describes parameters adequately. Slight enhancement justifies 4.

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

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

The description clearly states 'Semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes from siblings by explicitly pairing with ask_pipeworx_grounded and explaining when to use this tool (when record is too large for prompt).

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

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

The description gives explicit guidance: 'Use when the record is too big to cram into the prompt' and explains how it pairs with ask_pipeworx_grounded, providing clear context for when to use this tool over 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?

The description claims to 'Create' a subscription, implying a write operation, but annotations declare readOnlyHint=true, a direct contradiction. This undermines trust in the model's behavior.

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

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

The description is a single well-organized paragraph that front-loads the core purpose, then details type, prerequisites, and delivery options. No extraneous information.

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

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

Despite no output schema, the description notes the return value ('Returns the new subscription id'). It covers prerequisites, type details, default items behavior, and delivery channel specifics, making it complete for a creation tool.

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

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

The description adds significant value beyond the schema: it explains 'items defaults to all if omitted', provides concrete examples (e.g., items:['5.02'] = officer change), clarifies delivery channel usage (e.g., email sends templated alerts, SMS must match verified phone with daily cap). Schema coverage is 100%.

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

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

The description explicitly states 'Create a proactive monitoring subscription' and provides a concrete example for type 'sec_8k' with items. It distinguishes from sibling tools like 'list_subscriptions' and 'unsubscribe' by focusing on creation.

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

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

The description clearly states prerequisites (Pipeworx OAuth account) and explains delivery channel options (feed, email, SMS) with usage notes. It does not explicitly contrast with alternatives but the context is sufficient.

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

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

The description contradicts annotations: readOnlyHint=true suggests read-only, but 'cancel' is a mutation. Despite disclosing ownership and deactivation, the contradiction warrants a score of 1 per rules.

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

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

Two concise sentences, front-loaded with action and resource, no unnecessary words.

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

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

With one parameter and no output schema, the description covers ownership and deactivation. However, the contradiction with annotations reduces 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% with one parameter 'id'. The description adds value by clarifying that the id comes from 'subscribe' and explaining the effect of cancellation.

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

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

The description clearly states the action ('Cancel a subscription'), the resource ('subscription by id'), and distinguishes it from sibling tools like 'subscribe' and 'list_subscriptions'. It also specifies ownership enforcement and deactivation behavior.

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

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

The description implies when to use: when you want to cancel a subscription owned by you. It provides ownership constraint but does not explicitly mention when not to use or suggest alternatives for other scenarios.

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

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

Annotations declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds the specific output format (verdict types, structured form, citation, delta) and that it replaces multiple calls. It does not contradict annotations and provides helpful behavioral details.

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

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

The description is a single, well-structured paragraph that front-loads the core purpose and trigger phrases. Every sentence adds value: purpose, use case, domain, replacements, and output. No wasted words.

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

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

Given the tool's complexity and lack of output schema, the description fully covers the domain (company-financial claims), data source (SEC EDGAR + XBRL), output types (verdict, structured form, citation, delta), and notes it replaces multiple calls. It is complete and actionable.

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 ('claim') with a description. Schema coverage is 100%, so the description's mention of the parameter adds little beyond what is already in the schema. The examples in the description mirror the schema's description, so it meets the 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?

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It provides specific trigger phrases and explicitly distinguishes from siblings by noting it replaces 4-6 sequential calls, making the purpose unambiguous.

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

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

The description says when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and gives example trigger phrases. It does not explicitly state when not to use or list alternatives, but the guidance is clear enough for most agents.

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