Datagouv Fr

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

Beyond annotations (readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false), the description discloses that it probes LLMs, returns per-model details (score, confidence, signals, raw_response) plus combined view, and clarifies that Anthropic calls require a BYO API key and incurs direct costs. 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?

Single paragraph, front-loaded with purpose, then parameter details, and usage scenarios. Every sentence adds value; no redundancy.

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

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

Given no output schema, description adequately explains return structure (per-model {score, confidence, signals, raw_response} + combined view). Covers parameters, behavior, and use cases completely.

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

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

Schema coverage is 100%, and description adds significant meaning: explains 'entity' as the thing to ask about, 'models' lists supported values, '_apiKey' describes necessity and cost for Anthropic, and 'context' aids disambiguation. 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?

Clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility per model. Distinguishes from siblings by focusing on AI visibility audits, e.g., 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.'

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 mentions when to use (AI-marketing audits, pre-launch brand checks, competitive monitoring). Does not explicitly state when not to use or list alternatives, but the description sufficiently guides appropriate context.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by explaining that the tool routes to 3,436 tools, fills arguments, and returns structured answers with pipeworx:// citation URIs, 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?

The description is front-loaded with the key message 'PREFER OVER WEB SEARCH' and contains valuable information in every sentence, though it could be slightly more concise. It is well-organized with clear examples.

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

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

Given no output schema, the description adequately hints at the return format (structured answer with citation URIs). It covers the tool's capabilities and constraints, making it sufficiently complete for the complexity of a natural language query tool.

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

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

With 100% schema coverage, the description adds minimal extra meaning beyond the schema. It provides examples and context for the question parameter but does not deeply explain each alias or usage nuances, meeting the baseline expectation.

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

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

The description explicitly states the tool's purpose: answering factual questions using authoritative structured data with citations. It clearly distinguishes from web search and lists specific domains and trigger phrases, making the purpose unmistakable.

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

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

The description provides strong guidance on when to use the tool, including explicit preference over web search, trigger phrases like 'what is' and 'look up', and numerous examples. However, it does not explicitly exclude scenarios or compare to sibling tools like search_datasets, slightly reducing clarity.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description details the return structure (success with answer, evidence, confidence, source, etc.) and explicit refusal reasons (not_in_source, no_tool_match, etc.). No contradiction with annotations.

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

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

The description is detailed but front-loaded with the core purpose and trade-off. Each sentence provides necessary context; could be slightly more concise but still well-organized.

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

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

The description fully explains the tool's behavior, return format, refusal reasons, and use cases. Despite lack of output schema, it provides sufficient detail for an agent to understand outcomes. The 6 parameters are all aliases, so no gaps.

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

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

Schema coverage is 100% (all parameters have descriptions). The description adds that the question parameter accepts multiple aliases, but this is already evident from the schema. No additional semantic value beyond schema for parameters.

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

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

The description clearly defines the tool as a hallucination-resistant answer mode for high-stakes reads, specifying that it extracts answers only from tool results. It distinguishes itself from sibling 'ask_pipeworx' by noting the extra LLM call cost and the use case for quotable/cited answers.

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

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

Explicitly states when to use: for high-stakes reads where facts must not be invented (financial, legal, medical, public statements). Also advises preferring 'ask_pipeworx' for casual lookups, providing clear when-not guidance.

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

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

The description extensively details behavioral traits beyond annotations: fan-out to multiple data sources, resolution logic, confidence matching, handling of closed markets, low-confidence short-circuiting, and response shapes. All this adds significant value beyond the readOnlyHint, idempotentHint, and openWorldHint annotations.

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

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

The description is comprehensive but verbose, containing extensive lists, examples, and edge-case explanations. While well-structured with front-loaded purpose, it could be more concise without losing essential information. It earns its place but could be trimmed for efficiency.

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 (classifiers, fan-out patterns, response shapes, edge cases) and lack of an output schema, the description fully explains what the agent can expect, including result.market, result.analysis, result.evidence, resolver contract, parent event extractor, news fields, and safety behaviors. Everything needed to use the tool correctly is covered.

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

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

The input schema already provides 100% description coverage for parameters. The description adds some context (e.g., 'market' can be slug, URL, or question text; 'depth' defaults to thorough; 'include_raw' defaults false), but these are largely redundant with the schema. The description does not add radically new semantic 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 tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the inputs (slug, URL, question text) and provides numerous examples, distinctively separating it from sibling tools like polymarket_edges and 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?

The description gives explicit use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also implicitly differentiates from siblings by focusing on single bet research. However, it does not explicitly state when not to use this tool or directly compare to alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds significant behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), sorting by primary metric, handling of off-calendar fiscal years, and return of citation URIs. No contradiction with annotations.

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

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

The description is front-loaded with the key use case and is well-structured, but it is somewhat lengthy due to examples and parenthetical notes. Every sentence adds value, but it could be trimmed slightly without losing clarity.

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

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

Given the tool's complexity (two entity types, multiple data sources, sorting, return format) and no output schema, the description is remarkably complete. It covers input constraints, data specifics, sorting behavior, and return format (paired data with citations), leaving no critical gaps.

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

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

Schema coverage is 100% (both parameters described). The description enriches parameter meaning by specifying what data each type returns (e.g., revenue, net income for company; adverse-event counts for drug), which goes beyond the schema's enum and array 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 explicitly states the tool does a side-by-side comparison of 2-5 companies or drugs in one call. It includes clear verbs ('compare', 'rank') and resource types, and distinguishes from sibling tools (e.g., entity_profile) by advising preference over sequential single-entity lookups.

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

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

The description clearly states when to use the tool ('ALWAYS PREFER over sequential single-pack lookups when comparing entities'), providing explicit context, though it does not explicitly mention when not to use it.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds no extra behavioral context beyond the basic purpose. With annotations covering the safety profile, a score of 3 is appropriate as the description is adequate but not enriching.

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 one short sentence with zero wasted words. It is appropriately sized and front-loaded, efficiently communicating the tool's function.

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

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

For a simple retrieval tool with a single parameter and an output schema (not shown but exists), the description is minimally adequate. It covers the what and how but lacks details on error conditions, return format, or usage examples. Given the output schema, some burden is lifted.

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 0%, so the description must compensate. It clarifies that the parameter 'id_or_slug' can be either an ID or slug, which adds meaning beyond the schema's type-only definition. However, no format constraints or examples are given beyond the schema 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?

The description 'Single dataset by id or slug.' clearly states the tool's purpose: retrieving a specific dataset using either an ID or slug. It is a specific verb+resource combination that distinguishes it from sibling tools like 'search_datasets' which performs searches.

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

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

No guidance is provided on when to use this tool versus alternatives like 'search_datasets' or 'entity_profile'. The description does not mention prerequisites, exclusions, or context for invocation.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds value by specifying the return format (top-N tools with names, descriptions, full schemas) and that results are ready to call directly, without requiring a second lookup.

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

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

Two sentences, front-loaded with purpose. The first sentence includes a long list of examples which, while informative, could be trimmed for conciseness. Overall efficient and clear.

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

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

Given no output schema, description fully explains the return value (tool names, descriptions, schemas, examples) and notes they are ready to call. No missing behavioral or output details.

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

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

Schema covers all 6 parameters with descriptions (100% coverage). Description adds context about aliases and natural language queries, but no additional meaning beyond schema. Baseline 3 applies.

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

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

The description clearly states the tool's purpose: finding tools by describing data or task. It distinguishes itself from sibling search tools (e.g., search_datasets) by being a meta-tool for discovering other tools, not for searching within 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?

Explicitly recommends calling this tool first when exploring options: 'Call this FIRST when you have many tools available and want to see the option set.' Provides examples of queries but 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?

Annotations (readOnlyHint, idempotentHint) are already present. Description adds significant behavioral detail: fans out across multiple data sources (SEC, XBRL, USPTO, news, GLEIF), notes patent API sunset and soft-fail behavior, and specifies return structure.

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

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

The description is a single dense paragraph but front-loaded with purpose and example queries. Every sentence contributes meaningful information, though a more structured format (e.g., bullet points) could improve readability. No wasted words.

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

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

Despite no output schema, the description comprehensively explains return fields (CIK, company_name, filings with URIs, fundamentals with metrics, patents with caveat, news, LEI). All required parameters are described, and limitations are noted. The tool's behavior is fully characterized for agent use.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by reinforcing the enum constraint on 'type' and clarifying that 'value' can be ticker or zero-padded CIK, with explicit instruction to use resolve_entity for names. This provides context 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 full cross-source profile of a US public company, with specific verb 'profile' and resource 'US public company'. It lists example queries and distinguishes from sibling tools by noting it should be preferred over chaining individual lookups. The return fields are enumerated.

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 when to use: when user asks for a holistic view like 'tell me about X'. Explicit when not: names not supported, instructs to use resolve_entity first. Provides alternative to chaining single-pack lookups.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data, which is a behavioral aspect beyond what annotations provide. No contradictions.

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

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

Two sentences, each serving a distinct purpose: stating the action and providing usage guidance. No unnecessary words, front-loaded with key information.

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

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

For a simple tool with one required parameter and no output schema, the description fully covers purpose, usage context, and relationship to siblings. No gaps.

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

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

Schema covers 100% of parameters with a description for 'key'. The tool description does not add additional parameter 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 action 'Delete a previously stored memory by key', specifying the verb and resource. It also mentions related tools 'remember' and 'recall', distinguishing its purpose from siblings.

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

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

Provides explicit when-to-use scenarios: 'context is stale', 'task is done', 'clear sensitive data'. Also suggests pairing with 'remember' and 'recall', giving clear guidance on alternatives.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This adds context beyond annotations, though it could mention edge cases like non-indexable pages.

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 (two sentences plus a list of use cases), front-loaded with the primary purpose, and every sentence adds value. There is no redundancy or unnecessary detail.

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

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

Given the tool's simplicity (2 parameters, no output schema, clear annotations), the description fully covers what an agent needs: what it does, how it works, what output to expect, and when to use it. 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 both parameters well-described in the input schema. The description does not add new parameter information; it only repeats the default and max for max_links, which are already in the schema. Baseline is 3 as per guidelines.

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: 'Generate a production-ready llms.txt file for any URL'. It specifies the resource (llms.txt for a given URL) and distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on different aspects of AI visibility.

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 by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' While it doesn't explicitly state when not to use it, the context is clear and no sibling tool overlaps in functionality, making the guidance adequate.

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

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

Annotations already declare readOnly and idempotent. Description adds scope (caller's own subscriptions) and clarifies return fields, adding value beyond 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?

Two concise sentences: first states purpose and output, second gives usage guidance. No fluff; 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?

Given simple schema, no output schema, and comprehensive annotations, description fully enables agent to understand purpose, usage, and return values.

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

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

Schema coverage is 100% for the single parameter 'include_inactive', which is already described. Description does not add further 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?

Description clearly states verb 'List', resource 'subscriptions', and scope 'caller's active'. Specifies returned fields, distinguishing from sibling tools like 'subscribe' and 'unsubscribe'.

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

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

Explicitly advises use for reviewing monitored subscriptions before adding more or finding an id to cancel. Does not mention alternatives but provides clear context.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds no further behavioral context such as error handling, return format, or authentication requirements. With annotations covering safety, the description adds minimal value.

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

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

Extremely concise at 6 words, no redundancy. The core functionality is front-loaded. Every word is necessary.

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

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

Given the simplicity (single parameter, output schema exists), the description is nearly complete. It lacks mention of error cases (e.g., organization not found) but is adequate for a straightforward retrieval tool with rich 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 0%, so the description must compensate. It clarifies that the parameter can be an ID or slug (implied by 'by id or slug'), which adds meaning beyond schema. However, it does not specify format, examples, or constraints further.

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

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

The description 'Single organization by id or slug.' clearly indicates the tool retrieves a single organization entity using an identifier. It distinguishes from sibling tools like search_organizations which likely return multiple results. However, it lacks an explicit verb like 'get' or 'retrieve'.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when to prefer this over search_organizations or entity_profile, or any exclusions.

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

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

Annotations are all false (no hints), so the description carries the full burden. It adds useful behavioral traits: rate-limited to 5 per identifier per day, free, and not counting against tool-call quota. It also mentions that feedback signals affect the roadmap, providing impact context beyond mere behavior.

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

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

The description is composed of several sentences that are mostly focused and front-loaded with the core purpose. It is reasonably concise at about 100 words. Minor improvement could be grouping related information into clearer paragraphs, but it is not verbose.

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 nature of the tool (3 parameters, no output schema), the description covers all necessary aspects: purpose, usage conditions, behavioral constraints, and parameter guidance. It is fully self-contained and leaves no gaps for an agent to fill.

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 each parameter having a clear description. The description reinforces the enum meanings and message format (1-2 sentences, 2000 chars max) but does not add significant new meaning beyond what the schema already provides. Baseline of 3 is appropriate.

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

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

The description clearly states the tool is for providing feedback (bug, feature, data_gap, praise) to the Pipeworx team. It distinguishes itself from sibling tools by being a dedicated feedback mechanism, contrasting with tools like ask_pipeworx or discover_tools. The verb 'tell' and resource 'Pipeworx team' are specific.

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

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

The description explicitly lists when to use: for bugs, missing features/data, or praise. It also includes a negative instruction: 'don't paste the end-user's prompt.' However, it does not explicitly mention when not to use (e.g., for general inquiries), but the context from siblings implies alternatives like ask_pipeworx.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable context: data derived from CF analytics-engine, no PII, caching behavior (5min-1h), and that it's self-aggregating signal. 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?

Three sentences, front-loaded with core purpose, no wasted words. Efficiently organized with bullet-point use cases.

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 only one parameter, no output schema, and rich annotations, the description fully covers what the tool does, how it works, and caching. No gaps.

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

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

Schema has 100% coverage for a single enum parameter (window). The description adds interpretive guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand,' which provides meaning beyond the enum labels.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over specified time windows. It distinguishes itself from siblings by focusing on aggregated trending data from other AI agents.

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

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

The description explicitly lists three use cases (discovering hot data sources, confirming canonical choice, seeing alignment). It does not provide exclusions or alternatives, but the context is clear and actionable.

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

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

The description goes beyond annotations by detailing requirements (must provide event or topic, else fails), explaining the semantic anchor (Jaccard similarity ≥0.30), partition filter (drops placeholder slugs), and response structure. No contradictions with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint).

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

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

The description is thorough but somewhat verbose, using all-caps for emphasis and including many details. It is structured with sections for modes, semantic anchor, partition filter, and response. While clear, it could be more concise without losing 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 tool's complexity (two-mode operation, multiple checks), the description is complete. It explains the two modes, the algorithms (monotonicity, partition check), filtering rules, and output structure. There is no output schema, so the description carries the full burden, and it does so thoroughly.

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

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

The description adds significant meaning beyond the input schema. For 'event', it gives example slugs and explains the behavior. For 'topic', it explains cross-event scanning and gives examples. The schema descriptions are brief, so the description compensates well, though both parameters are fully covered.

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

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

The description clearly states the tool's purpose: find arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between two modes (event and topic) and provides concrete examples. The verb 'Find' plus specific resource and method make it highly clear.

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

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

The description provides explicit guidance on when to use each mode: event for a specific market, topic for cross-event scanning. It explains that cross-event mode catches patterns that single-event misses, helping users choose. However, it does not compare to sibling tools or say when not to use this tool.

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

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

Beyond annotations (readOnly, idempotent, not destructive), the description reveals extensive behavioral traits: caching at KV level for 1 hour, three response segments with detailed logic, edge calculation with slippage and Kelly sizing, and diagnostic info for empty segments. 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 with technical details. While front-loaded with purpose, the extensive explanations of model families and knobs could be condensed without losing value. Every sentence serves a purpose, but brevity is sacrificed.

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

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

Given the tool's complexity (9 parameters, no output schema), the description covers all necessary details: response structure, three segments, knobs, diagnostics, caching, and special handling of Fed bets. 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?

Although schema coverage is 100%, the description adds significant context for parameters like min_liquidity, max_spread_pp, and min_partition_leg_kelly, explaining their purpose and trade-offs. This extra information helps the agent choose sensible values.

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

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

The description clearly states a specific verb+resource: 'Scan top Polymarket markets' with a well-defined purpose to find opportunities where Pipeworx data disagrees with market price. It is built for a daily use case, and its differentiation from siblings is implicit through detailed explanation of what it does, distinguishing it from other Polymarket 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 provides clear context for when to use the tool ('what should I bet on today') and details about knobs and defaults. However, it does not explicitly exclude conditions or name alternative tools for different tasks, leaving some ambiguity.

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

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

The disclosure goes well beyond annotations by detailing behavioral traits: compatibility_warning conditions, temporal alignment, skipped_cross_type counters, and the fact that pre-mapped topics are often not tradeable. No contradiction with annotations (readOnlyHint=true matches read-only nature).

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

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

The description is thorough but verbose, with multiple paragraphs. Important information is front-loaded, but some details could be condensed without losing clarity.

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

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

Given the complexity of cross-venue spreads, no output schema, and the need for safety fields, the description covers all return fields (matched spread, compatibility_warning, temporal_alignment, skipped_cross_type) and explains their meaning.

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

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

Schema coverage is 100%, and the description adds meaningful context by explaining the two modes and how explicit parameters override topic mapping. It clarifies the interaction between parameters.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes itself from siblings like polymarket_arbitrage by its focus on a specific cross-venue comparison.

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

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

The description explicitly outlines two modes (topic shortcuts and explicit pairing) and provides warnings about when spreads are invalid (compatibility_warning, temporal alignment). It does not explicitly name alternative tools for similar tasks.

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, so the description adds 'scoped to your identifier' and listing behavior. Adds some context but not rich.

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

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

Three sentences, front-loaded with main action, usage guidance, and scoping. No waste. 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?

No output schema, and description does not specify return format (value type, listing format). Adequate for a simple tool but missing return details.

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

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

Schema coverage is 100%, and the description repeats the key parameter behavior (omit to list all keys). No additional meaning beyond the schema.

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

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

The description clearly states 'Retrieve a value' or 'list all saved keys', using specific verbs and resource. It distinguishes from siblings like 'remember' and 'forget'.

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

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

Provides clear context: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' and pairs with remember/forget. Does not explicitly state when not to use, but implies usage for retrieval.

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

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

The description explains that mark_read changes state (flags events as read) and that the feed is persistent. It also mentions each alert's structure. Annotations already declare readOnly, openWorld, idempotent, and the description adds context without contradicting them.

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

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

The description is concise, front-loaded with the main purpose, and each sentence adds value. No unnecessary words or repetition.

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

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

The description covers return format (source, citation_uri, raw payload) and filtering options. Without an output schema, this is sufficient. It also provides an alternative access method. Minor gaps: no mention of pagination behavior or error handling, but acceptable for a simple read tool.

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

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

The description adds practical meaning to parameters (e.g., 'sec_8k' for type, ISO timestamps for since) and explains the effect of mark_read. Since the schema already provides descriptions, the baseline is 3, but the added context and examples increase clarity.

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

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

The description clearly states the tool retrieves fired events from a subscription feed, specifying the resource and action. It differentiates from siblings by mentioning an alternative GET endpoint, but does not explicitly contrast with other tools like list_subscriptions or recall.

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

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

The description indicates it is suitable for polling and provides an alternative for scripts, but does not explicitly state when not to use this tool versus siblings. No exclusion criteria or alternative tool recommendations are given.

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=true, idempotentHint=true, destructiveHint=false. The description adds context about fan-out parallelism, fallback logic, and API sunset issues, which fully informs the agent of behavioral traits 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?

Front-loaded with common user queries, then concise explanation of operation. Every sentence adds value, no fluff. Structured logically with alternatives mentioned at the end.

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 clearly states the return structure (changes grouped by source, total_changes count, citation URIs). It covers all necessary context for the agent to use the tool correctly.

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

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

Schema covers all parameters (100%), and description adds extra meaning: explains `since` accepts ISO or relative shorthand with examples, `value` is ticker or CIK, and `type` current limitation. This enriches understanding beyond the schema.

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

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

The description clearly states it provides a change feed for a company over a time window, listing specific data sources (SEC EDGAR, GDELT→GNews, USPTO) and output structure. It distinguishes from the sibling tool entity_profile by specifying when to use the latter.

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 gives example queries ("what's new with X", etc.) and contrasts with entity_profile for static profiles. It explains fallback behavior between GDELT and GNews, and notes potential USPTO soft-fail. This provides clear when-to-use and 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 discloses scoping by identifier, persistence details (authenticated users get persistent memory; anonymous sessions retain memory for 24 hours), and the idempotent nature implied by 'Save'. These details go beyond the annotations, which already indicate idempotentHint=true and destructiveHint=false.

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

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

The description is concise with four sentences, front-loading the core action. It efficiently covers usage, storage details, and related tools without any 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 simplicity (key-value store) and the presence of annotations, the description fully covers behavioral context (scoping, persistence) and usage guidance. No output schema exists, but the description adequately informs the agent about what to expect.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by providing examples for the 'key' parameter (e.g., 'subject_property') and clarifying that 'value' can be any text for findings, addresses, etc., which aids the agent in populating the parameters correctly.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It specifies the resource (key-value pair) and distinguishes from sibling tools by mentioning 'recall' and 'forget' as related tools for retrieval and deletion.

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: 'when you discover something worth carrying forward'. It mentions pairing with recall and forget, but does not explicitly state when not to use this tool or provide contrasts with other tools for similar purposes.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds that calls cascade through multiple endpoints, replacing manual lookups. No contradiction.

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

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

Description is well-structured with examples and explicit guidance. Slightly long 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?

Given no output schema, the description fully explains return values for each entity type, including citation URIs, and internal behavior. Comprehensive for 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 covers 100% of parameters; description adds rich context: for 'type', specifies what returns for each (ticker, CIK, RxCUI, citation URIs); for 'value', provides concrete 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?

The description clearly states the tool resolves names to canonical IDs, with specific examples and supported types, differentiating it from sibling tools.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID', implying primary use case. Doesn't explicitly mention when not to use, but provides clear context.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds no behavioral context beyond the name 'downloadable files', which is already implied. It fails to explain what 'open world' means or any edge cases.

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

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

Extremely concise (one short sentence) but at the expense of informativeness. The description underspecifies the tool, requiring the agent to infer functionality from the name alone.

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

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

Despite having annotations and an output schema, the description omits crucial context: what the tool returns (list of resource objects vs. direct download), how to use the parameter, and any prerequisites. It is incomplete for the given complexity.

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

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

The sole parameter 'dataset_id_or_slug' has 0% schema description coverage. The description provides no explanation of what this parameter is or how to format it, leaving the agent to guess its meaning and required format.

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

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

The description states that resources are downloadable files for a dataset, which clarifies the noun 'resources' in this domain. However, it doesn't specify the action (e.g., list, download) or distinguish from the sibling 'dataset' tool that likely returns dataset metadata.

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

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

No guidance on when to use this tool versus alternatives. With 25 sibling tools, the agent receives no context on selection criteria, such as when to prefer resources over search_datasets or dataset.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint, so the safety profile is clear. The description adds no new behavioral context beyond the annotations, which is acceptable but not value-adding.

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

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

The description is a single sentence, concise and front-loaded. It earns its place by stating the core purpose, though it could benefit from minimal expansion.

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 rich annotations and an output schema, the description fails to explain pagination, result format, or how to use parameters effectively. For a search tool, this is insufficient context.

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

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

Schema description coverage is 0%, and the description provides no explanation of the three parameters (page, query, page_size). The example in the schema is not part of the description. This is a severe gap.

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

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

The description clearly states it searches 'reuses' defined as apps/analyses built on datasets, providing a specific verb+resource. However, it does not differentiate from sibling search tools like search_datasets or search_organizations.

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

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

No guidance is provided on when to use this tool versus alternatives like search_datasets. The description lacks any context for appropriate usage or exclusions.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds value by detailing the internal process ('probes each entity with ai_visibility_check, ranks by score') and the output format ('ranked list with score, confidence, signal density per entity'), providing useful behavioral context beyond annotations.

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

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

The description is concise at ~100 words, with clear structure: first sentence states action and output, second gives a use case, third details return format. No unnecessary words, and the most important information is front-loaded.

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

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

Given the tool's complexity (4 parameters, no output schema), the description explains the output fields (score, confidence, signal density) and a use case. It also adds constraints (2-8 entities) not in schema. However, it could mention error handling for invalid inputs or missing API keys, which prevents a 5.

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

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

With 100% schema description coverage, the baseline is 3. The description adds meaningful context not in the schema, such as 'first entry treated as the subject for narrative' and that 'workers-ai' is the free default model, enhancing understanding of parameter semantics.

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

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

The description clearly uses a specific verb ('compare,' 'probe,' 'rank') on a defined resource ('AI visibility across multiple entities'). It explicitly mentions using 'ai_visibility_check' internally and distinguishes itself from siblings by focusing on multi-entity comparison and ranking, making it easy to differentiate.

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

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

The description provides a concrete use case ('competitive AI-marketing audits') and implies the tool is for comparing multiple entities. However, it does not explicitly state when not to use it or mention alternatives (e.g., 'ai_visibility_check' for single entities), though the context signals list siblings that could serve as 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 read-only and idempotent behavior. The description adds significant behavioral context: it fans out to two external sources, returns a specific summary block with multiple fields, and gracefully handles partial failures with a 5-30s first-time delay for bundlephobia. 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 relatively long but each sentence adds value: composite operation, sources, return fields, ecosystem scoping, and failure handling. It is front-loaded with the main purpose. Minor verbosity could be trimmed but overall 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?

There is no output schema, so the description must explain return values. It does so by listing summary fields, per-advisory detail, links, and alternative versions. It also notes the sources_failed field. However, it lacks formal JSON structure details, and does not mention if requested package/version is echoed. Still comprehensive given the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal additional meaning beyond the schema: it confirms scoped packages are accepted, but the schema already includes that. The description does not enhance understanding of the parameters 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 tool's purpose: a composite check for adding an npm package, fanning out to deps.dev and bundlephobia. It distinguishes itself from sibling tools by specifying the NPM ecosystem and referencing fallback for other ecosystems via deps.dev:version.

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

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

The description explicitly says when to use this tool: 'whenever an agent asks is X safe / popular / small' or 'what does adding lodash cost me'. It also provides exclusions for other ecosystems (PyPI, Maven, etc.) and notes graceful degradation on partial failures.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds no additional behavioral context beyond the name.

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

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

While very concise, it is under-specified and does not provide enough information to be useful.

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 presence of 5 parameters and many sibling tools, the description is insufficiently complete.

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

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

The description does not explain any parameters beyond what is in the schema. With 60% schema coverage, the description fails to compensate for undocumented parameters like 'tag' and 'query'.

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 'Search datasets' clearly states the action and resource, but does not distinguish from sibling tools like 'search_organizations' or 'dataset'.

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

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

No guidance on when to use this tool versus alternatives, or any contextual prerequisites.

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 convey readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds no behavioral context beyond what annotations provide (e.g., no mention of how results are paginated or if there are rate limits). It does not contradict annotations, so a baseline score of 3 is appropriate.

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

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

The description is a single sentence, which is concise but severely under-specified. It provides no structured information, such as parameter guidance or usage notes, making it an example of under-specification rather than effective conciseness.

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

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

Despite having an output schema, the description is too minimal to be complete. It does not explain what results are returned, how pagination works, or any search behavior. For a tool with 3 parameters, this is insufficient.

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 0%, meaning the parameter details in the schema field descriptions are absent. The description fails to explain the meaning of 'query', 'page', or 'page_size' beyond their names. This is a critical gap for an agent to use 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 states 'Search organizations,' which is a clear verb+resource. However, it is extremely vague and does not differentiate from sibling tools like 'organization' or 'search_datasets'. There is no mention of scope or filtering capabilities, making it mediocre in purpose clarity.

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

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

No guidance is provided on when to use this tool versus alternatives like 'organization' or 'search_datasets'. The description does not mention any prerequisites, limitations, or context, leaving the agent without direction on tool selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds substantial behavior beyond: 200K char cap with truncation flag, BGE-base-en embeddings with cosine similarity over 500-char windows, and that every passage includes character offset for verification.

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 clear, with the main purpose front-loaded. It consists of two sentences covering use case, companion tool, and technical details. Almost every sentence adds value; could be slightly more structured but effective.

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

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

Given no output schema, the description adequately explains return values: 'top-N passages with character offsets and similarity scores'. It covers purpose, use case, limitations (200K char cap, truncation), technical details, and companion tool, making it complete for a 3-parameter tool with rich 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 description coverage is 100% (all 3 params have descriptions). The description adds extra context like text 'max ~200K chars', limit range '1-20, default 5', and natural-language query examples, enhancing understanding beyond the schema.

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

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

The description clearly states it performs 'semantic search INSIDE a fetched record', specifies example use cases (SEC 10-K, article, long tool result), and distinguishes from sibling ask_pipeworx_grounded by noting complementary use.

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 guides to use when 'the record is too big to cram into the prompt' and highlights that it 'saves context'. Mentions pairing with ask_pipeworx_grounded, implying when to use this tool together with that one. Lacks explicit 'do not use' conditions, but context is clear.

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

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

Annotations indicate readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds behavioral details like account requirements, persistence, delivery channels, and SMS cap, going beyond what annotations provide. No contradictions.

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

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

Description is well-structured with key info front-loaded. Uses examples and standard formatting. Every sentence adds value, no fluff.

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

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

Given 3 parameters, no output schema, the description covers all necessary aspects: purpose, account requirements, parameter details for each type, delivery options with constraints, and return value (subscription id). No significant gaps.

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

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

Schema coverage is 100%, but description greatly enriches each parameter: examples for each type, exact parameter shapes, and warnings (e.g., patent_grant exact form). Delivery options are explained with concrete examples and constraints. This goes well 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 'Create a proactive monitoring subscription' with a specific verb and resource. It lists supported event types and distinguishes this tool from siblings like list_subscriptions and unsubscribe.

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

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

Provides explicit context: requires Pipeworx OAuth account (anonymous/BYO cannot persist). Mentions SMS cap and verification. Does not explicitly state when not to use or compare to alternatives, but the context is clear enough for an agent to decide.

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 key behavioral traits beyond annotations: ownership enforcement and deactivation (not deletion), explaining the effect and linking to 'recent_alerts'. Annotations already indicate non-destructive and idempotent, but the description clarifies the exact mechanism.

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

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

The description is extremely concise: two sentences with no fluff. Key information (cancel, ownership, deactivation) is front-loaded.

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

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

For a tool with no output schema, the description covers the effect (deactivation, historical events remain). It lacks explicit return value but is otherwise complete given the tool's simplicity.

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

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

Schema coverage is 100%, so baseline is 3. The description reinforces that the id comes from 'subscribe', which adds useful context linking to the sibling tool, elevating clarity.

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', differentiating it from siblings like 'subscribe' (create) and 'list_subscriptions' (list). It also adds ownership enforcement and deactivation detail, making the purpose specific.

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 describes when to use (cancel a subscription) and provides constraints (ownership enforcement). It also notes that historical events remain via 'recent_alerts', implying the tool is not for permanent deletion. However, it does not explicitly list alternatives or when not to use, though the sibling context helps.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds the return types (verdict, structured form, actual value with citation, percent delta) and notes it replaces multiple calls. 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 front-loaded with trigger phrases and purpose, well-structured. Slightly long 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?

Given the complexity of natural-language claim verification and the domain, the description covers what it does, returns, and efficiency gains. No output schema, but return values are described.

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

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

Schema coverage is 100% for the single 'claim' parameter. Description adds examples and clarifies it expects natural-language factoids, going beyond the schema description.

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

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

The description clearly states the tool is for natural-language claim verification, lists trigger phrases, and specifies the domain (company-financial claims via SEC EDGAR + XBRL), distinguishing it from sibling tools.

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

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

Explicitly states when to use ('check whether something a user said is factually correct') and mentions it replaces 4-6 sequential calls, implying efficiency. Does not explicitly state when not to use, but the domain restriction is implied.

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