Openaire

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral context: it probes multiple LLMs, returns per-model data, and notes that Anthropic requires a BYO key with direct payment. This supplements annotation information without contradiction.

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

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

The description is three sentences, each serving a distinct purpose: action/output, default/optional details, and use cases. It is front-loaded with the core functionality and contains no redundant 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 moderate complexity (4 parameters, no output schema), the description covers key aspects: input clarification, return format, and use cases. It does not explain components like 'signals' or compare to sibling tools like 'scan_competitor_ai_presence', but it is sufficient for an agent to understand the tool's role.

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 all parameters have descriptions. The description adds value by explaining the default model (Workers AI Llama-3.3-70b free), the role of _apiKey for Anthropic, and how context helps disambiguate. This goes beyond the schema descriptions.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100) per model. It specifies the default model and optional Anthropic probe, and distinguishes its output format (per-model and combined view) 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?

The description explicitly lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It does not explicitly state when not to use or provide alternatives, but the context is clear enough for an agent to infer appropriate usage scenarios.

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

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

The description adds significant behavioral context beyond annotations: it explains routing to 3,899 tools across 932 sources, returning structured data with stable citation URIs. Annotations already declare read-only/idempotent safety; description enriches without contradiction.

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

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

The description is longer but front-loaded with the key preference statement and examples. Every sentence adds value; could be slightly more concise but well-structured.

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

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

The description covers purpose, usage guidelines, examples, and output format (structured answer with URIs). No output schema exists, but the description sufficiently explains what to expect. It addresses the tool's complexity 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% with multiple aliases for 'question'. The description does not add parameter-specific details beyond what schema provides, but it contextualizes the parameter's purpose. 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 routes questions to a large set of verified sources and returns structured answers with citations. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by specifying when to use each.

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 provides detailed when-to-use guidance with examples. Directly specifies when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad queries).

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, openWorldHint=true, idempotentHint=true. The description adds: 'EXTRACTS the answer using ONLY what the tool result contains' and details explicit refusal reasons (not_in_source, no_tool_match, etc.). It also notes the extra LLM call cost. No contradictions with annotations.

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

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

The description is well-structured and efficient, but slightly long. All sentences add value, including return format, refusal reasons, and usage guidance. Could be slightly more concise, but the detail is justified given the tool's complexity.

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

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

For a complex tool with no output schema, the description fully covers return format, refusal reasons, and behavioral traits. It compares to ask_pipeworx and provides clear usage context. 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 coverage is 100%, so the schema already fully documents the parameters. The description does not add new parameter-level semantics beyond restating that the question is in natural language. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It distinguishes itself from the sibling ask_pipeworx by mentioning the extra LLM call and grounded extraction. The return format and refusal reasons are explicitly listed, leaving no ambiguity about what the tool does.

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

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

The description provides explicit guidance: 'Use whenever an answer will be quoted, cited, or acted on...' and 'prefer ask_pipeworx for casual lookups.' It also mentions 'high-stakes reads,' giving clear when-to-use and when-not-to-use 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?

The annotations provide readOnlyHint, idempotentHint, and destructiveHint. The description adds extensive behavioral context: low-confidence resolution short-circuits, closed market handling, spread warnings, resolution rule risk, and blocking paths. 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 very long and dense, covering many edge cases and examples. While thorough, it could be more concise. The purpose is front-loaded, but the volume of detail may overwhelm an AI agent.

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

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

No output schema exists, so the description carries full burden. It thoroughly describes response shapes (result.market, result.analysis, result.evidence), resolver contract, parent_event extractor, news fields, safety mechanisms, and resolution-rule risk. Complete for the tool's complexity.

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

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

Schema coverage is 100%, so parameters are described in schema. The description adds value with examples, enumeration of depth options, and explanation of include_raw behavior. It provides context beyond schema but is not essential.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, with specific examples of inputs (slug, URL, question text) and outputs (evidence packet, comparison). It distinguishes from sibling tools like polymarket_edges by focusing on individual 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 lists use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It provides clear context for when to use, though it does not explicitly exclude scenarios 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?

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), off-calendar fiscal year handling, and sorting by primary metric. Annotations already indicate read-only and idempotent, so description adds rich behavioral context.

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

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

The description is dense and informative, front-loaded with natural language triggers. While slightly lengthy, every sentence adds value. Could be slightly more structured, but effectively conveys all essential 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 rich annotations and schema, the description is complete. It explains purpose, usage, data sources, sorting, and output (paired data + citation URIs). No gaps for the intended use case.

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

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

Schema coverage is 100%, so baseline 3. Description adds meaning by explaining the type enum values and providing examples for the values array (tickers vs. names), which goes beyond the schema's brief descriptions.

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

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

The description clearly states the tool's function: comparing 2–5 companies or drugs side-by-side in a single call. It uses specific verbs ('compare', 'rank') and distinguishes from sequential single-pack lookups, fully differentiating from sibling tools like entity_profile.

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

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

Explicitly provides when-to-use guidance with natural language examples and an 'ALWAYS PREFER' directive over sequential lookups. It details the data each type returns, giving clear context for selection.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds critical behavioral details: account required, free vs. paid plan, time expectation (15-60s), output structure (findings packet with evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[]), and confirms it never invents. No contradictions with annotations.

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

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

Description is dense but well-structured, front-loading the critical account requirement. Every sentence adds meaningful information. Could be slightly more concise, but the detail is justified given tool complexity.

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

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

Despite no output schema, description thoroughly explains what the tool returns: a findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, and gaps[]. It also sets expectations for timing and coverage, making it very complete for an AI agent to understand the tool's behavior and outcomes.

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. Description adds value by explaining depth enum values numerically (quick=3, standard=5, thorough=8) and clarifying that questions can be broad/multi-part. Slight improvement over schema.

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

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

Description clearly states the tool performs grounded multi-source research across 932 structured data sources, decomposing questions into facets and routing to 3899 tools in parallel. It distinguishes from sibling tools like ask_pipeworx by specifying it's for broad/multi-part questions over structured data, not single lookups or news.

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

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

Explicitly provides when-to-use (broad/multi-part questions over structured data), when-not-to-use (single lookup, breaking/current-news/colloquial topics), and alternatives (ask_pipeworx for single lookup, ask_pipeworx for news). Also mentions account requirement and alternative if not signed in.

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: returns top-N tools with full schemas, ready to call directly, no second lookup. No contradictions with annotations.

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

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

The description is front-loaded with purpose and includes a comprehensive list of use cases, but remains efficient. Every sentence adds value—no filler. Slightly long due to the list of domains, but that is justified by the need to convey scope.

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 simple nature (discover tools), the description fully covers behavior, return format, and usage context. It explains what results look like and that they are directly callable. No output schema is needed. Complete for an agent to use effectively.

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

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

Input schema has 100% coverage with descriptions and examples for each parameter. The description adds value beyond schema by listing many specific query domains (SEC filings, FDA, etc.) and confirming aliases work, which helps the agent formulate effective queries.

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/task, distinguishing it from sibling search tools (e.g., search_datasets) by explicitly noting it is for discovering available tools, not performing tasks. It lists many concrete use cases and instructs to call it FIRST for exploring the option set.

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

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

The description provides explicit guidance: 'Use 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).' It implies when not to use it (when you already know the tool) and sets clear expectations for return format.

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 value by detailing the fan-out across SEC, XBRL, USPTO, news, GLEIF, soft-fail for patents, and parallel call nature. Provides specific return fields and URIs.

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 dense with necessary information. It front-loads examples and purpose. Every sentence adds value, explaining sources, return fields, and limitations. Could be slightly more concise, but appropriate 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 no output schema, the description is remarkably complete: lists all return fields with formatting (URIs, sorting), explains fallback for news and patents, and covers input constraints. Covers all aspects needed for agent understanding.

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

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

Schema coverage is 100%, so baseline 3. The description adds important context: 'type' is only company, 'value' must be ticker or zero-padded CIK, and explicitly states names are not supported. This clarifies constraints 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 provides a holistic profile of US public companies from multiple sources in one parallel call, with example queries. It distinguishes from siblings like resolve_entity (for name resolution) and compare_entities.

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 prefer this tool ('ALWAYS PREFER over chaining single-pack lookups when the user asks for a holistic view') and when not to (names not supported, must use resolve_entity first). Clearly specifies input formats (ticker or CIK, not names).

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 'Delete', matching the destructiveHint annotation. It does not add significant behavioral context beyond what is already declared by annotations (destructiveHint, idempotentHint) and the schema. No contradictions found.

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 concise sentences, no wasted words. It front-loads the core action and purpose, making it easy for an agent to quickly understand.

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 (one required parameter, no output schema), the description covers all necessary context: what it does, when to use it, and how it relates to other 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 coverage is 100% with a clear description for the 'key' parameter: 'Memory key to delete'. The tool description does not add extra semantic meaning beyond that, 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 'Delete a previously stored memory by key,' specifying the verb, resource, and method. It distinguishes well from siblings like 'remember' and 'recall', which are for storing and retrieving memories.

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

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

Explicitly provides use cases: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with 'remember and recall', offering clear guidance on when to use this tool versus 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which the description aligns with by stating it 'fetches the page' and 'extracts' content. The description adds useful detail about the output format (single text blob ready to drop at site-root/llms.txt). It does not disclose potential errors or limits, but the annotations already cover safety, justifying a 4.

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 ~70 words that front-loads the main purpose, then explains use cases. Every sentence adds value: context about AI crawlers, what the tool does, output format, and example applications. No redundant or filler content.

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 simple parameter set (2 params, no output schema), the description fully covers necessary information: input URL, optional max_links, output is a text blob. Use cases are provided, and annotations cover safety. No gaps remain for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (url and max_links) adequately. The description repeats the same information without adding new meaning or context beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates an llms.txt file for a given URL, specifies the action 'generate' and the resource 'llms.txt file', and distinguishes it from sibling tools by mentioning AI crawlers (ChatGPT, Claude, Perplexity) and the standard markdown format. It leaves no ambiguity about what the tool does.

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

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

The description provides explicit use cases: getting a client's site indexed, drafting for personal projects, or auditing competitors. It implies when to use the tool, though it does not explicitly state when not to use it or provide alternatives. However, the purpose is distinct enough that context signals suffice.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by specifying what full details include (budget breakdown, partner list, outputs), which goes 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?

One sentence, no wasted words, front-loaded with the primary action and resource.

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, output schema exists, and the description mentions what full details include, it is adequately complete. Could mention that the output schema details the response.

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

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

Schema coverage is 100%, so the schema already documents the 'id' parameter. The description does not add additional meaning 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?

The description clearly states the verb 'Fetch', the resource 'funded research project', and the unique identifier 'OpenAIRE ID'. It differentiates from sibling tools like 'search_projects' by specifying the use case after search.

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 directs to use after 'search_projects', providing clear context on when to invoke this tool and implying not to use it for initial searches.

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 it is read-only, idempotent, and non-destructive. The description adds that it returns a full record, which is consistent with the annotations and provides useful context without contradiction.

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

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

The description is a single, well-structured sentence that conveys purpose and usage without unnecessary words, 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?

The tool has one required parameter, an output schema, and rich annotations. The description completes the picture by explaining when to use it and what it returns.

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

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

Schema coverage is 100% with a descriptive parameter definition. The description does not add additional parameter information beyond what is already 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 explicitly states it fetches a single research product (publication/dataset/software) by OpenAIRE ID, clearly distinguishing it from sibling search tools.

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

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

The description advises using this tool after search_publications/search_datasets/search_software to get the full record, providing clear usage context and a specific workflow.

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 readOnly, idempotent, and non-destructive traits. The description adds context about return fields and the include_inactive parameter, but does not disclose additional behavioral traits beyond what annotations provide.

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, front-loaded with the action and resource, efficient and free of 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 the tool's simplicity (one optional parameter, no output schema), the description sufficiently explains its purpose, usage, and return data. Annotations robustly handle behavioral disclosure.

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 does not add meaning beyond the input schema for the single parameter include_inactive. Schema coverage is 100%, 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 lists the caller's active subscriptions and enumerates the returned fields (id, type, params, etc.). This distinguishes 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?

It explicitly advises using this tool to review subscriptions before adding more or to find an id to cancel, providing clear when-to-use guidance. It does not explicitly state when not to use it, but the context implies 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 are minimal, so description adds crucial behavioral context like rate limit ('5 per identifier per day') and that it's free and doesn't count against quota. It also implies the feedback affects roadmap, which aids agent understanding. No contradiction with annotations.

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

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

The description is concise (4-5 sentences) and front-loaded with the core purpose. Every sentence provides necessary guidance (usage, content, limitations). No fluff or redundancy.

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

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

Given the tool's simplicity (3 params, no output schema), the description is fully complete. It covers purpose, when to use, parameters, behavioral constraints, and expected content. An agent can correctly invoke this tool based solely on the description.

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 tool description adds extra context: e.g., for 'message' it prohibits pasting end-user prompts, and for 'context' it suggests describing in terms of tools/packs. This adds 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: sending feedback about bugs, feature requests, data gaps, or praise. It uses specific verbs ('Tell', 'broken, missing, or needs to exist') and distinguishes itself from sibling tools (like ask_pipeworx) by focusing on feedback rather than 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 describes when to use each feedback type ('bug', 'feature', 'data_gap', 'praise') and what not to do ('don't paste the end-user's prompt'). Also provides guidance on content structure ('Describe the issue in terms of Pipeworx tools/packs') 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?

Annotations already indicate readOnly, idempotent, openWorld, non-destructive. The description adds valuable context: data source (CF analytics-engine), no PII, and caching behavior (5min-1h). 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?

Well-structured: first sentence states output, then three use cases, then technical details. Every sentence adds value, though slightly longer than minimal.

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 one optional parameter and no output schema, the description provides sufficient context: return shape (top tools, packs, volume), caching, data source, and privacy (no PII). Complete for its 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 already describes the window parameter with enum and description. The description adds nuance about short vs long windows but does not significantly expand beyond the schema.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a window, distinguishing it from siblings like ask_pipeworx or discover_tools by focusing on aggregate trending data.

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

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

Three explicit use cases are listed (discovering hot data sources, confirming canonical choice, aligning use case). While it doesn't explicitly say when not to use, the context is clear and alternatives are implied.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context: it walks child markets, checks monotonicity, computes partition checks, and returns opportunities with reasoning. It explains the fill check process and conditions where trades should be avoided (realizable edge ≤0). There is no contradiction with annotations.

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

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

The description is well-structured with clear sections (REQUIRES, event mode, topic mode, SEMANTIC ANCHOR, PARTITION FILTER, Response, FILL CHECK). It is front-loaded with the requirement. While slightly lengthy, every sentence adds essential information. Minor redundancy could be trimmed, but overall efficiency is high.

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 (arbitrage detection), the description covers all critical aspects: parameter usage, modes, filters, response structure, fill check, and trade guidance. There is no output schema, but the description details the response fields. Sibling references are included. The tool has only 2 parameters, and the description provides comprehensive 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?

Both parameters have schema descriptions covering 100% of parameters. The description adds significant value: explains that `event` takes a slug or full URL for single-event mode, and `topic` takes a seed question for cross-event mode. It provides examples and explains how each parameter affects behavior (walks child markets vs searches related events). This exceeds the baseline expectation for high schema coverage.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It specifies the two modes (event and topic), distinguishing them clearly. The verb 'Find' and resource 'arbitrage opportunities' are specific. It differentiates from sibling tools like polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk by describing its unique approach.

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 the requirement: one of `event` or `topic` must be provided; calling with no args fails. It recommends `event` for specific markets and `topic` for cross-event scanning. It explains when to use each mode and notes limitations like semantic anchor (Jaccard similarity ≥0.30) and partition filter (>20% placeholder fraction returns null). It also provides fill check guidance and references 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 already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds caching details, response structure, and explanations of edge_pp_net, kelly fractions, and why Fed bets are excluded, providing rich behavioral context.

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

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

Description is lengthy due to technical details but effectively packed with information. Could be more structured but remains readable and front-loaded with core purpose.

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

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

Explains response structure (by_segment, fed_candidates, diagnostics), per-opportunity fields, caching, and exclusion rationale. Without output schema, this provides full context for an agent to understand the tool's output and behavior.

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

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

Schema coverage is 100%, so baseline is 3. Description briefly mentions knobs like min_liquidity and max_spread_pp but does not add substantial 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?

First sentence clearly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' Specific verb+resource with differentiation from sibling tools like 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?

States 'Built for what should I bet on today' and describes three model families, giving clear use context. Lacks explicit when-not-to-use directives but contrasts with sibling tools implicitly.

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 the tool is read-only, non-destructive, and idempotent. The description adds valuable behavioral context: history bounded by 60-day TTL, snapshots written on cache-miss, and decay computed from daily closes (not intraday). 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 detailed but well-structured, front-loading the purpose and then detailing response fields and limitations. Every sentence adds value, though length could be reduced slightly without loss. Still efficient given the complexity.

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

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

Despite lacking an output schema, the description fully explains the return structure (tracked[], expired[], snapshot_dates[]) and the computational details (trend, decay). It covers edge cases like gaps and TTL limits. Complete for a read-only telemetry tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description restates the parameters (days, window) and their defaults but adds no new meaning beyond the schema. No additional parameter details are provided.

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 provides edge persistence and decay telemetry from daily snapshots, answering specific trading questions about edge longevity and trend. It distinguishes from siblings by focusing on historical snapshots rather than current edge data (polymarket_edges), but does not explicitly name alternatives.

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 context on when to use the tool (for assessing edge longevity and decay) and provides a trading insight, but lacks explicit guidance on when not to use it or how it compares to siblings like polymarket_edges. Usage is implied rather than directly stated.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as safe. The description adds extensive behavioral details: it walks the ladder, returns specific fields (top_of_book, vwap_fill_price, slippage, etc.), explains both modes, and highlights risks. 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 relatively long but well-structured with headings (SINGLE-MARKET and BASKET), front-loads the purpose, and every sentence adds value. Minor redundancy could be trimmed, but given the complexity, it is efficient.

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

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

Given the tool's complexity (two modes, many return fields) and the absence of an output schema, the description fully covers all returned fields and behaviors. It provides complete context for the agent to understand and 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 description coverage is 100%, providing a baseline of 3. The description adds further meaning: default values, interpretation of size_usd (max spend on buys, target proceeds on sells), and clamp constraints (10–1,000,000). This goes beyond schema.

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

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

The description clearly states the tool's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes two modes (single-market and basket) with specific verbs and resources, and the purpose is distinct 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?

Explicit usage guidance is provided: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains why (theoretical overround not capturable on thin books, partial basket fills causing unhedged directional risk).

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?

Descriptions add significant behavioral details beyond annotations: compatibility_warning conditions, temporal_alignment, skipped_cross_type/counters, and how mismatches make spreads meaningless. Annotations already declare readOnlyHint=true, which the description supports. No contradiction.

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

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

The description is lengthy but well-structured, front-loading the main purpose and modes. Every sentence adds value, though some could be tightened. The section on safety fields is detailed but essential.

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 three optional parameters and no output schema, the description fully covers behavior including edge cases (compatibility warnings, temporal alignment) and response structure (leg-by-leg prices, top_spreads_pp). It is sufficient for an agent to select and 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% with all parameters described. The description adds value by explaining the two modes and the relationship between parameters (topic overrides vs explicit), and providing examples in the schema. This goes 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It uses specific verbs ('compute', 'match') and resources ('Kalshi', 'Polymarket'), and the unique function distinguishes it from siblings like polymarket_arbitrage and 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 explains two modes (topic shortcuts vs explicit pairings) and when to use each. It warns that most pre-mapped topics return compatibility_warning, guiding the agent away from assuming tradability. While it doesn't name alternative sibling tools, 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds scoping context ('Scoped to your identifier') and the listing behavior when key is omitted. These add some value but do not significantly extend beyond the annotations.

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

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

The description is two sentences, front-loaded with the primary action, and contains no redundant information. Every sentence contributes to understanding.

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

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

For a read-only retrieval tool with no output schema, the description adequately covers usage context, scoping, and the dual behavior (get single key vs list all). It could benefit from mentioning the output format, but the intended use is clear given the sibling tools.

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

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

Schema coverage is 100% with the single key parameter described as 'Memory key to retrieve (omit to list all keys)'. The description essentially restates this, so it adds minimal semantic value 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: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' This specifies the verb (retrieve/list) and resource (saved memory), and distinguishes it 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?

The description provides guidance on when to use: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' It also mentions pairing with related tools ('Pair with remember to save, forget to delete'), though it lacks explicit when-not-to-use guidance.

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

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

The description contradicts the annotations: it describes a state-changing behavior ('mark_read:true to flag returned events read') while annotations declare readOnlyHint=true, indicating no state mutation. This is a critical inconsistency.

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

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

The description is concise with four well-structured sentences. Each sentence adds essential information: purpose, return fields, filtering options, and alternative access. No redundant or unnecessary content.

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 5 parameters with full schema coverage and no output schema, the description covers key aspects (type, since, mark_read) and mentions return fields. It lacks details on 'limit' default/behavior and 'unread_only', and ordering is implied. Still, it is relatively complete for a list 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%. The description adds valuable context beyond the schema by providing an example for 'type' ('sec_8k'), format hint for 'since' (ISO timestamp), and explaining the behavior of 'mark_read'. However, 'limit' defaults and 'unread_only' are not elaborated.

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 pulling fired events from a subscription feed, specifying what it returns (source, citation_uri, raw payload) and how to filter. This clearly distinguishes it from sibling tools like 'list_subscriptions' which manage subscriptions rather than retrieve alerts.

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

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

The description explains when to use the tool (polls work fine) and provides an alternative for scripts and dashboards. However, it does not explicitly contrast with sibling tools or state when not to use it, missing a clear usage boundary.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds substantial behavioral detail: fans out to multiple sources (SEC, GDELT→GNews fallback, USPTO), explains fallback logic and soft-fail for patents, and clarifies return 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?

Description is dense but front-loaded with queries. Every sentence adds information; no filler. However, it runs long (multiple clauses) and might benefit from slight restructuring for faster scanning. Still efficient for the complexity.

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

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

Given the tool's complexity (multiple data sources, fallback logic, no output schema), the description covers all necessary aspects: sources, fallback, special cases, return structure (changes[], total_changes, citation URIs). No gaps identified.

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

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

Schema coverage is 100% with descriptions for all three parameters. Description adds valuable context: accepted for 'since' (ISO date or relative shorthand like '7d'), ticker/CIK for 'value', and enum for 'type'. Recommends typical monitoring value for 'since'.

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 concrete query examples that strongly signal purpose: 'What's new with X', 'latest on Y'. It explicitly names the tool's function as 'change feed for a company in the last N days/weeks/months' and distinguishes from sibling tool entity_profile by stating when to use that instead.

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 provides explicit guidance on when to use this tool vs entity_profile ('Use entity_profile instead when you want the static profile'). It also includes typical value for since ('30d' or '1m'). However, it does not explicitly state when NOT to use this tool (e.g., for real-time data).

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 writable (readOnlyHint false) and idempotent (idempotentHint true). Description adds lifetime (24h anonymous, persistent authenticated) and scoping by identifier, complementing annotations well.

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

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

Concise at 4-5 sentences, front-loaded with main action, no wasted words. Each sentence adds distinct 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?

Covers purpose, usage, scope, lifespan, and sibling links. Adequate for a simple 2-parameter tool with good 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?

Schema covers 100% of parameters with descriptions. Description adds example key-value pairs and purpose, enhancing understanding beyond schema.

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

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

Clearly states 'Save data the agent will need to reuse later' and provides concrete examples (ticker, address, preference). Distinguishes from sibling tools recall and forget.

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

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

Explicitly advises when to use: 'when you discover something worth carrying forward' and pairs with recall/forget. Doesn't detail exclusions but 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destruction. The description adds significant behavioral context: it mentions that each call cascades through several lookup endpoints internally, and details the exact return fields (ticker, CIK, company name, citation URI for company; RxCUI, ingredient, brand, citation for drug). This goes well beyond the annotations and provides full transparency about what the tool does internally and what it returns.

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. It front-loads usage examples, then states the purpose, then details the supported types. Every sentence is informative. It could be slightly more concise (e.g., removing '— auto-disambiguated' might be unnecessary), but overall it is efficient and structured for quick reading.

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 the lack of an output schema, the description fully explains what the tool returns for each entity type, including citation URIs. It covers both supported types completely. Given the tool's complexity (two entity types, multiple input formats, cascading lookups), the description provides all necessary context for an agent to understand inputs, outputs, and expected 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%, but the description adds substantial meaning: it explains that 'value' accepts a ticker, CIK, or company name (with auto-disambiguation) for company, and brand or generic name for drug. It also clarifies what each type returns. This is precisely the kind of extra context that helps an agent choose and invoke the tool correctly.

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

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

The description opens with concrete examples ('What's the ticker for...') and states that the tool resolves names to canonical identifiers. It clearly distinguishes the use case from sibling tools by saying 'Use FIRST whenever you have a name but need an ID.' The supported types (company, drug) and their outputs are explicitly listed, 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 explicitly states when to use the tool ('Use FIRST whenever you have a name but need an ID.') and notes that it replaces 2-3 manual lookups. While it doesn't explicitly say when NOT to use it or name alternatives, the context is clear and the sibling tools are sufficiently different. Minor improvement would be adding a note about when to use a different tool (e.g., if you already have an ID).

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, so the description adds value by explaining the probing mechanism (calls ai_visibility_check per entity), ranking logic, and output fields (score, confidence, signal density). It also mentions the first entity is treated as the 'subject'. 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 three sentences, front-loaded with the core purpose. Each sentence earns its place: purpose, method, use case. No redundant or unclear phrasing. Efficient and well-structured.

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

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

Given the tool's complexity (composite, 4 parameters, no output schema), the description covers all necessary aspects: what it does, how it works, use case, and return format. The output is described as a 'ranked list with score, confidence, signal density per entity', which is sufficient without an output schema. The annotations complement 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 description coverage is 100%, so baseline is 3. The description adds the important convention that the first entity in the array is treated as the 'subject' for narrative purpose, which is not in the schema. This provides additional meaning beyond the parameter 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 'Compare AI visibility across multiple entities side-by-side', which includes the specific verb (compare) and resource (AI visibility). It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic) by specifying the AI-presence domain and ranking 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 provides clear context for use: 'Useful for competitive AI-marketing audits' and gives an example question. It implicitly tells when to use this over ai_visibility_check (when comparing multiple entities). No explicit when-not-to-use or alternatives beyond the sibling list, but the context is strong 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 mark it as read-only, idempotent, and non-destructive. The description adds valuable detail: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. 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 detailed but well-structured, front-loading the main purpose and then covering output, limitations, and fallbacks. Slightly verbose but 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 fully enumerates return fields, error handling, ecosystem constraints, and fallback behavior. Comprehensive for the tool's complexity.

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

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

Schema description coverage is 100% with both parameters adequately described. The description doesn't add significant new meaning 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 is a composite check for npm packages, combining data from deps.dev and bundlephobia to answer questions about safety, popularity, and size. It specifies the exact output fields and distinguishes itself from siblings by focusing on npm dependencies.

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

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

Explicitly tells when to use ('is X safe / popular / small') and when not to (other ecosystems like PyPI/Maven fall under deps.dev:version directly). Provides clear guidance on the tool's scope and alternatives.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already indicate safety and read-only behavior. Description adds value by naming specific repositories and search fields (title/abstract/keywords), providing 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?

Two sentences with example queries and a clear purpose. No fluff, front-loaded, 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?

With an output schema present, return values need not be explained. Description covers purpose, use cases, and example queries. Could mention filtering by repository, but sufficient for a search tool.

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

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

Schema description coverage is 60% (3 of 5 parameters described). The description adds example usage for date filters but doesn't fully explain to_year/from_year in text. Baseline 3 is appropriate; marginal value from examples.

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

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

Clearly states the tool searches for research datasets in OpenAIRE repositories (Zenodo, Dryad, etc.), using specific verbs like 'find' and 'search'. It differentiates from sibling tools like search_publications and search_software by focusing on datasets.

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 example queries and states the tool is for finding open research data to reuse or cite. While it doesn't explicitly list when not to use, the context of sibling tools implies alternatives, and the description gives clear 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about data sources (CORDIS, NSF, etc.) and returned fields, but does not significantly extend beyond what annotations already convey about safety and idempotency.

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

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

Description is front-loaded with example queries and clear purpose. It contains two main ideas (examples + function) and a usage line. Slightly longer than minimal but efficient and well-structured.

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

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

Given annotations cover safety, schema fully documents parameters, and an output schema exists, the description is complete for a search tool. It covers purpose, scope, and returned fields adequately.

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

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

Input schema documentation covers 100% of parameters with descriptions. The description adds no additional parameter semantics beyond the schema, so baseline score of 3 is appropriate.

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

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

Description starts with example queries showing typical use cases, then clearly states 'search funded research projects' with specific funders (CORDIS, NSF, NIH, etc.), differentiating it from siblings like search_publications or search_datasets. Verb and resource are explicit.

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 provides example queries and states 'Use for funding-landscape analysis, grant precedent research,' giving clear usage context. It does not explicitly exclude other tools but the intended use is well communicated.

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, destructiveHint, idempotentHint. The description adds source context (OpenAIRE, specific funders) and filtering capabilities. No contradictions, but the description doesn't elaborate on behavioral traits beyond what annotations cover.

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

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

The description is slightly long but well-structured with front-loaded examples. Every sentence adds value, though minor redundancy could be trimmed.

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

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

The description covers purpose, data source, filtering, and examples. With an output schema present, return details are not needed. Adequate 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% with descriptions for all parameters. The description provides inline examples showing parameter usage, adding modest value 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 explicitly states it searches scholarly publications via OpenAIRE, lists publication types, and gives example queries. It clearly distinguishes from sibling tools like search_datasets and search_software.

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 example use cases and notes it's for academic literature with funder/policy context. It implies when to use but does not explicitly exclude alternatives or provide when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by specifying the data source (OpenAIRE) and the nature of results (citable, tied to research), which goes beyond annotation signals without contradiction.

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

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

Two concise sentences with front-loaded examples and clear purpose. Every sentence contributes value—no redundancy or 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?

Covers domain, usage, and data source adequately. Output schema exists, so return values need not be described. Slightly more context about OpenAIRE could be helpful, but current version is sufficient for a search tool.

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

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

Schema description coverage is 100%, so the schema already documents query, page, and size. The description does not add further semantic meaning or examples for parameters beyond what's in the schema, meeting 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 uses specific verbs ('search', 'Find') and resources ('research software registrations in OpenAIRE') with concrete examples ('bioinformatics alignment tool'), clearly differentiating from siblings like search_publications by focusing on software tied to published 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 states 'Use for finding citable scientific software tied to published research', providing clear context. However, no direct mention of when not to use or explicit alternatives among siblings, though the domain specificity implies exclusion of datasets/publications.

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 embedding model (BGE-base-en), window size (500-char overlapping), character cap (200K chars with truncation/flagging), and offset for verification, adding 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?

Four sentences, front-loaded with purpose and usage, 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?

Despite no output schema, description specifies return format (top-N passages with offsets and scores), making it complete for a simple 3-param tool.

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

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

Schema coverage is 100%, so baseline 3. Description adds natural-language query examples and clarifies default for limit (5), enhancing 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?

Description clearly states semantic search inside a fetched record with specific verb+resource. It distinguishes from sibling ask_pipeworx_grounded by pairing explanation.

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 (record too big for prompt) and pairs with ask_pipeworx_grounded for grounding, 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 indicate readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds significant context: requires Pipeworx OAuth account, explains supported types with examples, delivery channels with constraints (10/day SMS cap, webhook auto-disable after 10 fails, signing secret returned once). 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 well-structured: front-loads purpose and return, then details types and delivery channels in a logical order. Every sentence adds value, no filler. Length is appropriate for the complexity.

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

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

For a creation tool with no output schema, the description explains the return value (subscription id) and key side effects (e.g., webhook secret returned once). It covers major behavioral aspects but could optionally describe the response structure in more detail. Overall adequate given sibling tools for management.

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 substantial value beyond schema: it provides type-specific examples (e.g., sec_8k items codes, FRED series_id), explains delivery constraints (phone verification required, webhook signing), and clarifies optionality. This greatly aids parameter selection.

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

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

The description clearly states the verb 'Create' and resource 'proactive monitoring subscription' with context 'live-data event stream', and specifies it returns the subscription id. It distinguishes from sibling tools like list_subscriptions and unsubscribe by being the creation tool.

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

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

The description provides clear context for when to subscribe (to monitor events) with examples of types and delivery channels. However, it does not explicitly mention when not to use it or compare with alternative tools like list_subscriptions or recent_alerts, though the purpose 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 provide readOnlyHint, idempotentHint, destructiveHint=false. Description adds that it returns question examples with tool+argument shapes, complementing annotations without contradiction.

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

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

Description is moderately long but well-structured with clear sections: usage scenarios, categories, parameter guidance. 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?

For a simple tool with one optional parameter and no output schema, the description is thoroughly complete: covers purpose, when to use, parameter behavior, and expected output.

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

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

Schema covers the only parameter (topic) fully. Description adds context by listing example category values and explaining that omitting it gives full spread. Adds value 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 is the onboarding entry point for new users, returning category-bucketed example questions. It distinguishes itself from sibling tools by positioning as the first tool to use when unsure.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you'. Provides guidance on when to pass a topic vs. omit for full spread. No explicit when-not-to-use, but sufficient.

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

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

Annotations already indicate mutation (readOnlyHint=false), non-destructiveness (destructiveHint=false), and idempotency (idempotentHint=true). The description adds value by explaining the deactivation behavior and historical event availability, with no contradictions.

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

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

Two concise sentences: the first states the primary action, the second adds ownership and behavior details. No unnecessary words.

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

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

Given the tool's simplicity (1 param, no output schema), the description fully covers the operation's behavior and side effects. However, it does not explicitly mention idempotency, which is covered by 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?

Schema coverage is 100% with a single parameter 'id' described as 'Subscription id (uuid) returned by subscribe.' The description adds no additional semantics beyond 'by id'; 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 verb 'Cancel' and the resource 'subscription by id'. It explicitly distinguishes from siblings like 'subscribe' by indicating it's the inverse operation.

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

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

It specifies ownership enforcement ('you can only cancel your own subscriptions') and explains the effect ('deactivated, not deleted'). While it does not explicitly list alternatives, the context is clear for a simple cancellation 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral details: it returns a verdict with specific categories, includes a structured form, actual value with citation, and percent delta. It also explains that it replaces 4-6 sequential calls, giving the agent a sense of cost and efficiency. 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 fairly long but well-structured, front-loading example queries and the core purpose before diving into details. Every sentence adds value, though it could be slightly more concise without losing 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 has only one parameter with high schema coverage, no output schema, and comprehensive annotations, the description is fully adequate. It explains what the tool does, when to use it, what inputs are expected, and what outputs to expect, leaving no ambiguity for an AI 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 a good description for the 'claim' parameter. The description adds further meaning by elaborating on the types of claims supported (natural-language, company-financial) and showing examples. This provides context beyond the schema, making it easier for the agent to construct valid inputs.

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 validates natural-language factual claims against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It uses example queries to illustrate the range of inputs, and the purpose is distinct from sibling tools like 'deep_research' or 'search_within' which perform broader tasks.

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 whenever the agent needs to check whether something a user said is factually correct' and provides example user intents. It does not explicitly state when not to use, but the domain specificity (company-financial claims) implies limitations. Still, clear guidance for its intended use case.

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